five-fold cross validation: Topics by Science.gov

Sample records for five-fold cross validation

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

PubMed Central

Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

2015-01-01

Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462
Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

PubMed

Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.
Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

PubMed Central

Martinez, Josue G.; Carroll, Raymond J.; Müller, Samuel; Sampson, Joshua N.; Chatterjee, Nilanjan

2012-01-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso. PMID:22347720
Genomic selection across multiple breeding cycles in applied bread wheat breeding.

PubMed

Michel, Sebastian; Ametz, Christian; Gungor, Huseyin; Epure, Doru; Grausgruber, Heinrich; Löschenberger, Franziska; Buerstmayr, Hermann

2016-06-01

We evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of [Formula: see text] = 0.51 for protein content, [Formula: see text] = 0.38 for grain yield and [Formula: see text] = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to [Formula: see text] = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to [Formula: see text] = 0.19 for this derived trait.
Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
A nearest neighbor approach for automated transporter prediction and categorization from protein sequences.

PubMed

Li, Haiquan; Dai, Xinbin; Zhao, Xuechun

2008-05-01

Membrane transport proteins play a crucial role in the import and export of ions, small molecules or macromolecules across biological membranes. Currently, there are a limited number of published computational tools which enable the systematic discovery and categorization of transporters prior to costly experimental validation. To approach this problem, we utilized a nearest neighbor method which seamlessly integrates homologous search and topological analysis into a machine-learning framework. Our approach satisfactorily distinguished 484 transporter families in the Transporter Classification Database, a curated and representative database for transporters. A five-fold cross-validation on the database achieved a positive classification rate of 72.3% on average. Furthermore, this method successfully detected transporters in seven model and four non-model organisms, ranging from archaean to mammalian species. A preliminary literature-based validation has cross-validated 65.8% of our predictions on the 11 organisms, including 55.9% of our predictions overlapping with 83.6% of the predicted transporters in TransportDB.
Cross-validation pitfalls when selecting and assessing regression and classification models.

PubMed

Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

2014-03-29

We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.
SU-G-BRC-13: Model Based Classification for Optimal Position Selection for Left-Sided Breast Radiotherapy: Free Breathing, DIBH, Or Prone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, H; Liu, T; Xu, X

Purpose: There are clinical decision challenges to select optimal treatment positions for left-sided breast cancer patients—supine free breathing (FB), supine Deep Inspiration Breath Hold (DIBH) and prone free breathing (prone). Physicians often make the decision based on experiences and trials, which might not always result optimal OAR doses. We herein propose a mathematical model to predict the lowest OAR doses among these three positions, providing a quantitative tool for corresponding clinical decision. Methods: Patients were scanned in FB, DIBH, and prone positions under an IRB approved protocol. Tangential beam plans were generated for each position, and OAR doses were calculated.more » The position with least OAR doses is defined as the optimal position. The following features were extracted from each scan to build the model: heart, ipsilateral lung, breast volume, in-field heart, ipsilateral lung volume, distance between heart and target, laterality of heart, and dose to heart and ipsilateral lung. Principal Components Analysis (PCA) was applied to remove the co-linearity of the input data and also to lower the data dimensionality. Feature selection, another method to reduce dimensionality, was applied as a comparison. Support Vector Machine (SVM) was then used for classification. Thirtyseven patient data were acquired; up to now, five patient plans were available. K-fold cross validation was used to validate the accuracy of the classifier model with small training size. Results: The classification results and K-fold cross validation demonstrated the model is capable of predicting the optimal position for patients. The accuracy of K-fold cross validations has reached 80%. Compared to PCA, feature selection allows causal features of dose to be determined. This provides more clinical insights. Conclusion: The proposed classification system appeared to be feasible. We are generating plans for the rest of the 37 patient images, and more statistically significant results are to be presented.« less
Benchmark of Machine Learning Methods for Classification of a SENTINEL-2 Image

NASA Astrophysics Data System (ADS)

Pirotti, F.; Sunar, F.; Piragnolo, M.

2016-06-01

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.
Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.
GIS-aided Statistical Landslide Susceptibility Modeling And Mapping Of Antipolo Rizal (Philippines)

NASA Astrophysics Data System (ADS)

Dumlao, A. J.; Victor, J. A.

2015-09-01

Slope instability associated with heavy rainfall or earthquake is a familiar geotechnical problem in the Philippines. The main objective of this study is to perform a detailed landslide susceptibility assessment of Antipolo City. The statistical method of assessment used was logistic regression. Landslide inventory was done through interpretation of aerial photographs and satellite images with corresponding field verification. In this study, morphologic and non-morphologic factors contributing to landslide occurrence and their corresponding spatial relationships were considered. The analysis of landslide susceptibility was implemented in a Geographic Information System (GIS). The 17320 randomly selected datasets were divided into training and test data sets. K- cross fold validation is done with k= 5. The subsamples are then fitted five times with k-1 training data set and the remaining fold as the validation data set. The AUROC of each model is validated using each corresponding data set. The AUROC of the five models are; 0.978, 0.977, 0.977, 0.974, and 0.979 respectively, implying that the models are effective in correctly predicting the occurrence and nonoccurrence of landslide activity. Field verification was also done. The landslide susceptibility map was then generated from the model. It is classified into four categories; low, moderate, high and very high susceptibility. The study also shows that almost 40% of Antipolo City has been assessed to be potentially dangerous areas in terms of landslide occurrence.
Comparisons of the Outcome Prediction Performance of Injury Severity Scoring Tools Using the Abbreviated Injury Scale 90 Update 98 (AIS 98) and 2005 Update 2008 (AIS 2008).

PubMed

Tohira, Hideo; Jacobs, Ian; Mountain, David; Gibson, Nick; Yeo, Allen

2011-01-01

The Abbreviated Injury Scale (AIS) was revised in 2005 and updated in 2008 (AIS 2008). We aimed to compare the outcome prediction performance of AIS-based injury severity scoring tools by using AIS 2008 and AIS 98. We used all major trauma patients hospitalized to the Royal Perth Hospital between 1994 and 2008. We selected five AIS-based injury severity scoring tools, including Injury Severity Score (ISS), New Injury Severity Score (NISS), modified Anatomic Profile (mAP), Trauma and Injury Severity Score (TRISS) and A Severity Characterization of Trauma (ASCOT). We selected survival after injury as a target outcome. We used the area under the Receiver Operating Characteristic curve (AUROC) as a performance measure. First, we compared the five tools using all cases whose records included all variables for the TRISS (complete dataset) using a 10-fold cross-validation. Second, we compared the ISS and NISS for AIS 98 and AIS 2008 using all subjects (whole dataset). We identified 1,269 and 4,174 cases for a complete dataset and a whole dataset, respectively. With the 10-fold cross-validation, there were no clear differences in the AUROCs between the AIS 98- and AIS 2008-based scores. With the second comparison, the AIS 98-based ISS performed significantly worse than the AIS 2008-based ISS (p<0.0001), while there was no significant difference between the AIS 98- and AIS 2008-based NISSs. Researchers should be aware of these findings when they select an injury severity scoring tool for their studies.
Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity.

PubMed

Zhang, Hui; Kang, Yan-Li; Zhu, Yuan-Yuan; Zhao, Kai-Xia; Liang, Jun-Yu; Ding, Lan; Zhang, Teng-Guo; Zhang, Ji

2017-06-01

Prediction of drug candidates for mutagenicity is a regulatory requirement since mutagenic compounds could pose a toxic risk to humans. The aim of this investigation was to develop a novel prediction model of mutagenicity by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test sets. For comparison, the recursive partitioning classifier prediction model was also established and other various reported prediction models of mutagenicity were collected. Among these methods, the prediction performance of naïve Bayes classifier established here displayed very well and stable, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set I set were 89.1±0.4% and 77.3±1.5%, respectively. The concordance of the external test set II with 446 marketed drugs was 90.9±0.3%. In addition, four simple molecular descriptors (e.g., Apol, No. of H donors, Num-Rings and Wiener) related to mutagenicity and five representative substructures of mutagens (e.g., aromatic nitro, hydroxyl amine, nitroso, aromatic amine and N-methyl-N-methylenemethanaminum) produced by ECFP_14 fingerprints were identified. We hope the established naïve Bayes prediction model can be applied to risk assessment processes; and the obtained important information of mutagenic chemicals can guide the design of chemical libraries for hit and lead optimization. Copyright © 2017 Elsevier B.V. All rights reserved.
sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Heng; Ye, Hao; Ng, Hui Wen

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less
sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

PubMed Central

Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

2016-01-01

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848
sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE PAGES

Luo, Heng; Ye, Hao; Ng, Hui Wen; ...

2016-08-25

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less
Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images.

PubMed

Pareek, Gyan; Acharya, U Rajendra; Sree, S Vinitha; Swapna, G; Yantri, Ratna; Martis, Roshan Joy; Saba, Luca; Krishnamurthi, Ganapathy; Mallarini, Giorgio; El-Baz, Ayman; Al Ekish, Shadi; Beland, Michael; Suri, Jasjit S

2013-12-01

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.
Comparisons of the Outcome Prediction Performance of Injury Severity Scoring Tools Using the Abbreviated Injury Scale 90 Update 98 (AIS 98) and 2005 Update 2008 (AIS 2008)

PubMed Central

Tohira, Hideo; Jacobs, Ian; Mountain, David; Gibson, Nick; Yeo, Allen

2011-01-01

The Abbreviated Injury Scale (AIS) was revised in 2005 and updated in 2008 (AIS 2008). We aimed to compare the outcome prediction performance of AIS-based injury severity scoring tools by using AIS 2008 and AIS 98. We used all major trauma patients hospitalized to the Royal Perth Hospital between 1994 and 2008. We selected five AIS-based injury severity scoring tools, including Injury Severity Score (ISS), New Injury Severity Score (NISS), modified Anatomic Profile (mAP), Trauma and Injury Severity Score (TRISS) and A Severity Characterization of Trauma (ASCOT). We selected survival after injury as a target outcome. We used the area under the Receiver Operating Characteristic curve (AUROC) as a performance measure. First, we compared the five tools using all cases whose records included all variables for the TRISS (complete dataset) using a 10-fold cross-validation. Second, we compared the ISS and NISS for AIS 98 and AIS 2008 using all subjects (whole dataset). We identified 1,269 and 4,174 cases for a complete dataset and a whole dataset, respectively. With the 10-fold cross-validation, there were no clear differences in the AUROCs between the AIS 98- and AIS 2008-based scores. With the second comparison, the AIS 98-based ISS performed significantly worse than the AIS 2008-based ISS (p<0.0001), while there was no significant difference between the AIS 98- and AIS 2008-based NISSs. Researchers should be aware of these findings when they select an injury severity scoring tool for their studies. PMID:22105401
Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .
EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.

PubMed

Zhou, Jiyun; Lu, Qin; Xu, Ruifeng; He, Yulan; Wang, Hongpeng

2017-08-29

Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community.

Multivariate Adaptive Regression Splines (Preprint)

DTIC Science & Technology

1990-08-01

fold cross -validation would take about ten time as long, and MARS is not all that fast to begin with. Friedman has a number of examples showing...standardized mean squared error of prediction (MSEP), the generalized cross validation (GCV), and the number of selected terms (TERMS). In accordance with...and mi= 10 case were almost exclusively spurious cross product terms and terms involving the nuisance variables x6 through xlo. This large number of
GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

PubMed

Maciukiewicz, Malgorzata; Marshe, Victoria S; Hauschild, Anne-Christin; Foster, Jane A; Rotzinger, Susan; Kennedy, James L; Kennedy, Sidney H; Müller, Daniel J; Geraci, Joseph

2018-04-01

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction. Copyright © 2017. Published by Elsevier Ltd.
Cross-Cultural Validation of the Five-Factor Structure of Social Goals: A Filipino Investigation

ERIC Educational Resources Information Center

King, Ronnel B.; Watkins, David A.

2012-01-01

The aim of the present study was to test the cross-cultural validity of the five-factor structure of social goals that Dowson and McInerney proposed. Using both between-network and within-network approaches to construct validation, 1,147 Filipino high school students participated in the study. Confirmatory factor analysis indicated that the…
K-Fold Crossvalidation in Canonical Analysis.

ERIC Educational Resources Information Center

Liang, Kun-Hsia; And Others

1995-01-01

A computer-assisted, K-fold cross-validation technique is discussed in the framework of canonical correlation analysis of randomly generated data sets. Analysis results suggest that this technique can effectively reduce the contamination of canonical variates and canonical correlations by sample-specific variance components. (Author/SLD)
Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

PubMed

Hsu, Yu-Liang; Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-07-15

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.
Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

PubMed Central

Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-01-01

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment. PMID:28714884
Estimates of Commercial Motor Vehicles Using the Southwest Border Crossings

DOT National Transportation Integrated Search

2000-09-20

The United States has experienced almost a five-fold increase in commercial motor vehicle traffic to and from Mexico during the past sixteen years. There were more than 4< million commercial motor vehicle (CMV) crossings from Mexico into the United S...
Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

NASA Astrophysics Data System (ADS)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC) = 0.65 (p = 0.004), 0.73 (p = 0.026), and 0.66 (p = 0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC = 0.68 (p = 0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC = 0.60 (p = 0.092) and 0.65 (p = 0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.
Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone

NASA Astrophysics Data System (ADS)

Rampun, Andrik; Zheng, Ling; Malcolm, Paul; Tiddeman, Bernie; Zwiggelaar, Reyer

2016-07-01

In this paper we propose a prostate cancer computer-aided diagnosis (CAD) system and suggest a set of discriminant texture descriptors extracted from T2-weighted MRI data which can be used as a good basis for a multimodality system. For this purpose, 215 texture descriptors were extracted and eleven different classifiers were employed to achieve the best possible results. The proposed method was tested based on 418 T2-weighted MR images taken from 45 patients and evaluated using 9-fold cross validation with five patients in each fold. The results demonstrated comparable results to existing CAD systems using multimodality MRI. We achieved an area under the receiver operating curve (A z ) values equal to 90.0%+/- 7.6% , 89.5%+/- 8.9% , 87.9%+/- 9.3% and 87.4%+/- 9.2% for Bayesian networks, ADTree, random forest and multilayer perceptron classifiers, respectively, while a meta-voting classifier using average probability as a combination rule achieved 92.7%+/- 7.4% .
Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?

PubMed

Bozkurt, Selen; Bostanci, Asli; Turhan, Murat

2017-08-11

The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination. In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used. Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model. Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.
Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks.

PubMed

Park, Jinhee; Javier, Rios Jesus; Moon, Taesup; Kim, Youngwook

2016-11-24

Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.
In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

PubMed

Cao, D-S; Zhao, J-C; Yang, Y-N; Zhao, C-X; Yan, J; Liu, S; Hu, Q-N; Xu, Q-S; Liang, Y-Z

2012-01-01

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.
A Diagnostic Model for Impending Death in Cancer Patients: Preliminary Report

PubMed Central

Hui, David; Hess, Kenneth; dos Santos, Renata; Chisholm, Gary; Bruera, Eduardo

2015-01-01

Background We recently identified several highly specific bedside physical signs associated with impending death within 3 days among patients with advanced cancer. In this study, we developed and assessed a diagnostic model for impending death based on these physical signs. Methods We systematically documented 62 physical signs every 12 hours from admission to death or discharge in 357 patients with advanced cancer admitted to acute palliative care units (APCUs) at two tertiary care cancer centers. We used recursive partitioning analysis (RPA) to develop a prediction model for impending death in 3 days using admission data. We validated the model with 5 iterations of 10-fold cross-validation, and also applied the model to APCU days 2/3/4/5/6. Results Among 322/357 (90%) patients with complete data for all signs, the 3-day mortality was 24% on admission. The final model was based on 2 variables (palliative performance scale [PPS] and drooping of nasolabial fold) and had 4 terminal leaves: PPS≤20% and drooping of nasolabial fold present, PPS≤20% and drooping of nasolabial fold absent, PPS 30–60% and PPS ≥ 70%, with 3-day mortality of 94%, 42%, 16% and 3%, respectively. The diagnostic accuracy was 81% for the original tree, 80% for cross-validation, and 79%–84% for subsequent APCU days. Conclusion(s) We developed a diagnostic model for impending death within 3 days based on 2 objective bedside physical signs. This model was applicable to both APCU admission and subsequent days. Upon further external validation, this model may help clinicians to formulate the diagnosis of impending death. PMID:26218612
Identifying a predictive model for response to atypical antipsychotic monotherapy treatment in south Indian schizophrenia patients.

PubMed

Gupta, Meenal; Moily, Nagaraj S; Kaur, Harpreet; Jajodia, Ajay; Jain, Sanjeev; Kukreti, Ritushree

2013-08-01

Atypical antipsychotic (AAP) drugs are the preferred choice of treatment for schizophrenia patients. Patients who do not show favorable response to AAP monotherapy are subjected to random prolonged therapeutic treatment with AAP multitherapy, typical antipsychotics or a combination of both. Therefore, prior identification of patients' response to drugs can be an important step in providing efficacious and safe therapeutic treatment. We thus attempted to elucidate a genetic signature which could predict patients' response to AAP monotherapy. Our logistic regression analyses indicated the probability that 76% patients carrying combination of four SNPs will not show favorable response to AAP therapy. The robustness of this prediction model was assessed using repeated 10-fold cross validation method, and the results across n-fold cross-validations (mean accuracy=71.91%; 95%CI=71.47-72.35) suggest high accuracy and reliability of the prediction model. Further validations of these results in large sample sets are likely to establish their clinical applicability. Copyright © 2013 Elsevier Inc. All rights reserved.
The development and cross-validation of an MMPI typology of murderers.

PubMed

Holcomb, W R; Adams, N A; Ponder, H M

1985-06-01

A sample of 80 male offenders charged with premeditated murder were divided into five personality types using MMPI scores. A hierarchical clustering procedure was used with a subsequent internal cross-validation analysis using a second sample of 80 premeditated murderers. A Discriminant Analysis resulted in a 96.25% correct classification of subjects from the second sample into the five types. Clinical data from a mental status interview schedule supported the external validity of these types. There were significant differences among the five types in hallucinations, disorientation, hostility, depression, and paranoid thinking. Both similarities and differences of the present typology with prior research was discussed. Additional research questions were suggested.
Taking the Next Step: Combining Incrementally Valid Indicators to Improve Recidivism Prediction

ERIC Educational Resources Information Center

Walters, Glenn D.

2011-01-01

The possibility of combining indicators to improve recidivism prediction was evaluated in a sample of released federal prisoners randomly divided into a derivation subsample (n = 550) and a cross-validation subsample (n = 551). Five incrementally valid indicators were selected from five domains: demographic (age), historical (prior convictions),…
A diagnostic model for impending death in cancer patients: Preliminary report.

PubMed

Hui, David; Hess, Kenneth; dos Santos, Renata; Chisholm, Gary; Bruera, Eduardo

2015-11-01

Several highly specific bedside physical signs associated with impending death within 3 days for patients with advanced cancer were recently identified. A diagnostic model for impending death based on these physical signs was developed and assessed. Sixty-two physical signs were systematically documented every 12 hours from admission to death or discharge for 357 patients with advanced cancer who were admitted to acute palliative care units (APCUs) at 2 tertiary care cancer centers. Recursive partitioning analysis was used to develop a prediction model for impending death within 3 days with admission data. The model was validated with 5 iterations of 10-fold cross-validation, and the model was also applied to APCU days 2 to 6. For the 322 of 357 patients (90%) with complete data for all signs, the 3-day mortality rate was 24% on admission. The final model was based on 2 variables (Palliative Performance Scale [PPS] and drooping of nasolabial folds) and had 4 terminal leaves: PPS score ≤ 20% and drooping of nasolabial folds present, PPS score ≤ 20% and drooping of nasolabial folds absent, PPS score of 30% to 60%, and PPS score ≥ 70%. The 3-day mortality rates were 94%, 42%, 16%, and 3%, respectively. The diagnostic accuracy was 81% for the original tree, 80% for cross-validation, and 79% to 84% for subsequent APCU days. Based on 2 objective bedside physical signs, a diagnostic model was developed for impending death within 3 days. This model was applicable to both APCU admission and subsequent days. Upon further external validation, this model may help clinicians to formulate the diagnosis of impending death. © 2015 American Cancer Society.
BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

PubMed

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan

2015-06-01

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.
Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.

PubMed

Zhang, Hui; Cao, Zhi-Xing; Li, Meng; Li, Yu-Zhi; Peng, Cheng

2016-11-01

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M W ), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development. Copyright © 2016 Elsevier Ltd. All rights reserved.
An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

NASA Astrophysics Data System (ADS)

Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

2017-03-01

In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks.

PubMed

Vigneault, Davis M; Xie, Weidi; Ho, Carolyn Y; Bluemke, David A; Noble, J Alison

2018-05-22

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained using three-fold cross-validation on a cohort of patients with hypertrophic cardiomyopathy (HCM, N=42) and healthy control subjects (N=21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch using five-fold cross-validation on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally. Published by Elsevier B.V.
Dideoxynucleoside resistance emerges with prolonged zidovudine monotherapy. The RV43 Study Group.

PubMed Central

Mayers, D L; Japour, A J; Arduino, J M; Hammer, S M; Reichman, R; Wagner, K F; Chung, R; Lane, J; Crumpacker, C S; McLeod, G X

1994-01-01

Human immunodeficiency virus type 1 (HIV-1) isolates resistant to zidovudine (ZDV) have previously been demonstrated to exhibit in vitro cross-resistance to other similar dideoxynucleoside agents which contain a 3'-azido group. However, cross-resistance to didanosine (ddI) or dideoxycytidine (ddC) has been less well documented. ZDV, ddI, and ddC susceptibility data have been collected from clinical HIV-1 isolates obtained by five clinical centers and their respective retrovirology laboratories. All subjects were treated only with ZDV. Clinical HIV-1 isolates were isolated, amplified, and assayed for drug susceptibility in standardized cultures of phytohemagglutinin-stimulated donor peripheral blood mononuclear cells obtained from healthy seronegative donors. All five cohorts showed a correlation between decreased in vitro susceptibility to ZDV and decreased susceptibility to ddI and ddC. For each 10-fold decrease in ZDV susceptibility, an average corresponding decrease of 2.2-fold in ddI susceptibility was observed (129 isolates studied; P < 0.001, Fisher's test of combined significance). Similarly, susceptibility to ddC decreased 2.0-fold for each 10-fold decrease in ZDV susceptibility (82 isolates studied; P < 0.001, Fisher's test of combined significance). These data indicate that a correlation exists between HIV-1 susceptibilities to ZDV and ddI or ddC for clinical HIV-1 isolates. PMID:8192457
Candidate soil indicators for monitoring the progress of constructed wetlands toward a natural state: a statistical approach

USGS Publications Warehouse

Stapanian, Martin A.; Adams, Jean V.; Fennessy, M. Siobhan; Mack, John; Micacchion, Mick

2013-01-01

A persistent question among ecologists and environmental managers is whether constructed wetlands are structurally or functionally equivalent to naturally occurring wetlands. We examined 19 variables collected from 10 constructed and nine natural emergent wetlands in Ohio, USA. Our primary objective was to identify candidate indicators of wetland class (natural or constructed), based on measurements of soil properties and an index of vegetation integrity, that can be used to track the progress of constructed wetlands toward a natural state. The method of nearest shrunken centroids was used to find a subset of variables that would serve as the best classifiers of wetland class, and error rate was calculated using a five-fold cross-validation procedure. The shrunken differences of percent total organic carbon (% TOC) and percent dry weight of the soil exhibited the greatest distances from the overall centroid. Classification based on these two variables yielded a misclassification rate of 11% based on cross-validation. Our results indicate that % TOC and percent dry weight can be used as candidate indicators of the status of emergent, constructed wetlands in Ohio and for assessing the performance of mitigation. The method of nearest shrunken centroids has excellent potential for further applications in ecology.
Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction.

PubMed

Zhou, Yao; Vales, M Isabel; Wang, Aoxue; Zhang, Zhiwu

2017-09-01

Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that the minimum value for prediction accuracy should hover around zero when randomly permuted data sets are analyzed. We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by the mathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as the mean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using a modified formula. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Predicting protein-binding regions in RNA using nucleotide profiles and compositions.

PubMed

Choi, Daesik; Park, Byungkyu; Chae, Hanju; Lee, Wook; Han, Kyungsook

2017-03-14

Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use. We developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others. Sequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding .
Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

PubMed

Deist, Timo M; Jochems, A; van Soest, Johan; Nalbantov, Georgi; Oberije, Cary; Walsh, Seán; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Dekker, Andre; Lambin, Philippe

2017-06-01

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future 'big data' infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade [Formula: see text]. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.
A machine learning approach to multi-level ECG signal quality classification.

PubMed

Li, Qiao; Rajagopalan, Cadathur; Clifford, Gari D

2014-12-01

Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm. A total of 13 signal quality metrics were derived from segments of ECG waveforms, which were labeled by experts. A support vector machine (SVM) was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB). The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet/Computing in Cardiology Challenge database, and adding three types of real ECG noise at different signal-to-noise ratio (SNR) levels from the MIT-BIH Noise Stress Test Database (NSTDB). The MITDB was re-annotated for five levels of signal quality. Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB. Performance was assessed using classification accuracy (Ac), and a single class overlap accuracy (OAc), which assumes that an individual type classified into an adjacent class is acceptable. An Ac of 80.26% and an OAc of 98.60% on the test set were obtained by selecting 10 metrics while 57.26% (Ac) and 94.23% (OAc) were the numbers for the unseen MITDB validation data without retraining. By performing the fivefold cross validation, an Ac of 88.07±0.32% and OAc of 99.34±0.07% were gained on the validation fold of MITDB. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Development of a QSAR Model for Thyroperoxidase Inhbition ...

EPA Pesticide Factsheets

hyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the cross- and external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre-registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to
An Automated Approach for Ranking Journals to Help in Clinician Decision Support

PubMed Central

Jonnalagadda, Siddhartha R.; Moosavinasab, Soheil; Nath, Chinmoy; Li, Dingcheng; Chute, Christopher G.; Liu, Hongfang

2014-01-01

Point of care access to knowledge from full text journal articles supports decision-making and decreases medical errors. However, it is an overwhelming task to search through full text journal articles and find quality information needed by clinicians. We developed a method to rate journals for a given clinical topic, Congestive Heart Failure (CHF). Our method enables filtering of journals and ranking of journal articles based on source journal in relation to CHF. We also obtained a journal priority score, which automatically rates any journal based on its importance to CHF. Comparing our ranking with data gathered by surveying 169 cardiologists, who publish on CHF, our best Multiple Linear Regression model showed a correlation of 0.880, based on five-fold cross validation. Our ranking system can be extended to other clinical topics. PMID:25954382
Proteome-wide characterization of signalling interactions in the hippocampal CA4/DG subfield of patients with Alzheimer’s disease

PubMed Central

Ho Kim, Jae; Franck, Julien; Kang, Taewook; Heinsen, Helmut; Ravid, Rivka; Ferrer, Isidro; Hee Cheon, Mi; Lee, Joo-Yong; Shin Yoo, Jong; Steinbusch, Harry W; Salzet, Michel; Fournier, Isabelle; Mok Park, Young

2015-01-01

Alzheimer’s disease (AD) is the most common form of dementia; however, mechanisms and biomarkers remain unclear. Here, we examined hippocampal CA4 and dentate gyrus subfields, which are less studied in the context of AD pathology, in post-mortem AD and control tissue to identify possible biomarkers. We performed mass spectrometry-based proteomic analysis combined with label-free quantification for identification of differentially expressed proteins. We identified 4,328 proteins, of which 113 showed more than 2-fold higher or lower expression in AD hippocampi than in control tissues. Five proteins were identified as putative AD biomarkers (MDH2, PCLO, TRRAP, YWHAZ, and MUC19 isoform 5) and were cross-validated by immunoblotting, selected reaction monitoring, and MALDI imaging. We also used a bioinformatics approach to examine upstream signalling interactions of the 113 regulated proteins. Five upstream signalling (IGF1, BDNF, ZAP70, MYC, and cyclosporin A) factors showed novel interactions in AD hippocampi. Taken together, these results demonstrate a novel platform that may provide new strategies for the early detection of AD and thus its diagnosis. PMID:26059363
Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification.

PubMed

AbuHassan, Kamal J; Bakhori, Noremylia M; Kusnin, Norzila; Azmi, Umi Z M; Tania, Marzia H; Evans, Benjamin A; Yusof, Nor A; Hossain, M A

2017-07-01

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.
Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints.

PubMed

Ai, Haixin; Chen, Wen; Zhang, Li; Huang, Liangchao; Yin, Zimo; Hu, Huan; Zhao, Qi; Zhao, Jian; Liu, Hongsheng

2018-05-21

Drug-induced liver injury (DILI) is a major safety concern in the drug-development process, and various methods have been proposed to predict the hepatotoxicity of compounds during the early stages of drug trials. In this study, we developed an ensemble model using three machine learning algorithms and 12 molecular fingerprints from a dataset containing 1,241 diverse compounds. The ensemble model achieved an average accuracy of 71.1±2.6%, sensitivity of 79.9±3.6%, specificity of 60.3±4.8%, and area under the receiver operating characteristic curve (AUC) of 0.764±0.026 in five-fold cross-validation and an accuracy of 84.3%, sensitivity of 86.9%, specificity of 75.4%, and AUC of 0.904 in an external validation dataset of 286 compounds collected from the Liver Toxicity Knowledge Base (LTKB). Compared with previous methods, the ensemble model achieved relatively high accuracy and sensitivity. We also identified several substructures related to DILI. In addition, we provide a web server offering access to our models (http://ccsipb.lnu.edu.cn/toxicity/HepatoPred-EL/).
Development of a Bayesian model to estimate health care outcomes in the severely wounded

PubMed Central

Stojadinovic, Alexander; Eberhardt, John; Brown, Trevor S; Hawksworth, Jason S; Gage, Frederick; Tadaki, Douglas K; Forsberg, Jonathan A; Davis, Thomas A; Potter, Benjamin K; Dunne, James R; Elster, E A

2010-01-01

Background: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations. Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves. Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC. Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations. PMID:21197361
[Study of adaptation and validation of the Practice environment scale of the nursing work index for the Portuguese reality].

PubMed

Ferreira, Maria Regina Sardinheiro do Céu Furtado; Martins, José Joaquim Penedos Amendoeira

2014-08-01

Testing the psychometric properties of the Portuguese version of the Practice Environment Scale of the Nursing Work Index. A descriptive, analytical and cross-sectional study, for the cross-cultural adaptation and validation of the psychometric properties of the scale. The study participants were 236 nurses from two hospitals in the regions of Lisbon and Vale do Tejo. The 0.92 Cronbach's alpha was obtained for overall reliability and support of a five-dimension structure. The excellent quality of adjustment of analysis confirms the validity of the adapted version to hospital care settings, although there was no total coincidence of items in the five dimensions
Development of novel in silico model for developmental toxicity assessment by using naïve Bayes classifier method.

PubMed

Zhang, Hui; Ren, Ji-Xia; Kang, Yan-Li; Bo, Peng; Liang, Jun-Yu; Ding, Lan; Kong, Wei-Bao; Zhang, Ji

2017-08-01

Toxicological testing associated with developmental toxicity endpoints are very expensive, time consuming and labor intensive. Thus, developing alternative approaches for developmental toxicity testing is an important and urgent task in the drug development filed. In this investigation, the naïve Bayes classifier was applied to develop a novel prediction model for developmental toxicity. The established prediction model was evaluated by the internal 5-fold cross validation and external test set. The overall prediction results for the internal 5-fold cross validation of the training set and external test set were 96.6% and 82.8%, respectively. In addition, four simple descriptors and some representative substructures of developmental toxicants were identified. Thus, we hope the established in silico prediction model could be used as alternative method for toxicological assessment. And these obtained molecular information could afford a deeper understanding on the developmental toxicants, and provide guidance for medicinal chemists working in drug discovery and lead optimization. Copyright © 2017 Elsevier Inc. All rights reserved.
Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches.

PubMed

Beretta, Lorenzo; Santaniello, Alessandro; Cappiello, Francesca; Chawla, Nitesh V; Vonk, Madelon C; Carreira, Patricia E; Allanore, Yannick; Popa-Diaconu, D A; Cossu, Marta; Bertolotti, Francesca; Ferraccioli, Gianfranco; Mazzone, Antonino; Scorza, Raffaella

2010-01-01

Systemic sclerosis (SSc) is a multiorgan disease with high mortality rates. Several clinical features have been associated with poor survival in different populations of SSc patients, but no clear and reproducible prognostic model to assess individual survival prediction in scleroderma patients has ever been developed. We used Cox regression and three data mining-based classifiers (Naïve Bayes Classifier [NBC], Random Forests [RND-F] and logistic regression [Log-Reg]) to develop a robust and reproducible 5-year prognostic model. All the models were built and internally validated by means of 5-fold cross-validation on a population of 558 Italian SSc patients. Their predictive ability and capability of generalisation was then tested on an independent population of 356 patients recruited from 5 external centres and finally compared to the predictions made by two SSc domain experts on the same population. The NBC outperformed the Cox-based classifier and the other data mining algorithms after internal cross-validation (area under receiving operator characteristic curve, AUROC: NBC=0.759; RND-F=0.736; Log-Reg=0.754 and Cox= 0.724). The NBC had also a remarkable and better trade-off between sensitivity and specificity (e.g. Balanced accuracy, BA) than the Cox-based classifier, when tested on an independent population of SSc patients (BA: NBC=0.769, Cox=0.622). The NBC was also superior to domain experts in predicting 5-year survival in this population (AUROC=0.829 vs. AUROC=0.788 and BA=0.769 vs. BA=0.67). We provide a model to make consistent 5-year prognostic predictions in SSc patients. Its internal validity, as well as capability of generalisation and reduced uncertainty compared to human experts support its use at bedside. Available at: http://www.nd.edu/~nchawla/survival.xls.
Thiamethoxam Resistance in the House Fly, Musca domestica L.: Current Status, Resistance Selection, Cross-Resistance Potential and Possible Biochemical Mechanisms.

PubMed

Khan, Hafiz Azhar Ali; Akram, Waseem; Iqbal, Javaid; Naeem-Ullah, Unsar

2015-01-01

The house fly, Musca domestica L., is an important ectoparasite with the ability to develop resistance to insecticides used for their control. Thiamethoxam, a neonicotinoid, is a relatively new insecticide and effectively used against house flies with a few reports of resistance around the globe. To understand the status of resistance to thiamethoxam, eight adult house fly strains were evaluated under laboratory conditions. In addition, to assess the risks of resistance development, cross-resistance potential and possible biochemical mechanisms, a field strain of house flies was selected with thiamethoxam in the laboratory. The results revealed that the field strains showed varying level of resistance to thiamethoxam with resistance ratios (RR) at LC50 ranged from 7.66-20.13 folds. Continuous selection of the field strain (Thia-SEL) for five generations increased the RR from initial 7.66 fold to 33.59 fold. However, resistance declined significantly when the Thia-SEL strain reared for the next five generations without exposure to thiamethoxam. Compared to the laboratory susceptible reference strain (Lab-susceptible), the Thia-SEL strain showed cross-resistance to imidacloprid. Synergism tests revealed that S,S,S-tributylphosphorotrithioate (DEF) and piperonyl butoxide (PBO) produced synergism of thiamethoxam effects in the Thia-SEL strain (2.94 and 5.00 fold, respectively). In addition, biochemical analyses revealed that the activities of carboxylesterase (CarE) and mixed function oxidase (MFO) in the Thia-SEL strain were significantly higher than the Lab-susceptible strain. It seems that metabolic detoxification by CarE and MFO was a major mechanism for thiamethoxam resistance in the Thia-SEL strain of house flies. The results could be helpful in the future to develop an improved control strategy against house flies.
Thiamethoxam Resistance in the House Fly, Musca domestica L.: Current Status, Resistance Selection, Cross-Resistance Potential and Possible Biochemical Mechanisms

PubMed Central

Khan, Hafiz Azhar Ali; Akram, Waseem; Iqbal, Javaid; Naeem-Ullah, Unsar

2015-01-01

The house fly, Musca domestica L., is an important ectoparasite with the ability to develop resistance to insecticides used for their control. Thiamethoxam, a neonicotinoid, is a relatively new insecticide and effectively used against house flies with a few reports of resistance around the globe. To understand the status of resistance to thiamethoxam, eight adult house fly strains were evaluated under laboratory conditions. In addition, to assess the risks of resistance development, cross-resistance potential and possible biochemical mechanisms, a field strain of house flies was selected with thiamethoxam in the laboratory. The results revealed that the field strains showed varying level of resistance to thiamethoxam with resistance ratios (RR) at LC50 ranged from 7.66-20.13 folds. Continuous selection of the field strain (Thia-SEL) for five generations increased the RR from initial 7.66 fold to 33.59 fold. However, resistance declined significantly when the Thia-SEL strain reared for the next five generations without exposure to thiamethoxam. Compared to the laboratory susceptible reference strain (Lab-susceptible), the Thia-SEL strain showed cross-resistance to imidacloprid. Synergism tests revealed that S,S,S-tributylphosphorotrithioate (DEF) and piperonyl butoxide (PBO) produced synergism of thiamethoxam effects in the Thia-SEL strain (2.94 and 5.00 fold, respectively). In addition, biochemical analyses revealed that the activities of carboxylesterase (CarE) and mixed function oxidase (MFO) in the Thia-SEL strain were significantly higher than the Lab-susceptible strain. It seems that metabolic detoxification by CarE and MFO was a major mechanism for thiamethoxam resistance in the Thia-SEL strain of house flies. The results could be helpful in the future to develop an improved control strategy against house flies. PMID:25938578
Non-destructive detection of cross-sectional strain and defect structure in an individual Ag five-fold twinned nanowire by 3D electron diffraction mapping.

PubMed

Fu, Xin; Yuan, Jun

2017-07-24

Coherent x-ray diffraction investigations on Ag five-fold twinned nanowires (FTNWs) have drawn controversial conclusions concerning whether the intrinsic 7.35° angular gap could be compensated homogeneously through phase transformation or inhomogeneously by forming disclination strain field. In those studies, the x-ray techniques only provided an ensemble average of the structural information from all the Ag nanowires. Here, using three-dimensional (3D) electron diffraction mapping approach, we non-destructively explore the cross-sectional strain and the related strain-relief defect structures of an individual Ag FTNW with diameter about 30 nm. The quantitative analysis of the fine structure of intensity distribution combining with kinematic electron diffraction simulation confirms that for such a Ag FTNW, the intrinsic 7.35° angular deficiency results in an inhomogeneous strain field within each single crystalline segment consistent with the disclination model of stress-relief. Moreover, the five crystalline segments are found to be strained differently. Modeling analysis in combination with system energy calculation further indicates that the elastic strain energy within some crystalline segments, could be partially relieved by the creation of stacking fault layers near the twin boundaries. Our study demonstrates that 3D electron diffraction mapping is a powerful tool for the cross-sectional strain analysis of complex 1D nanostructures.
Modeling of autocatalytic hydrolysis of adefovir dipivoxil in solid formulations.

PubMed

Dong, Ying; Zhang, Yan; Xiang, Bingren; Deng, Haishan; Wu, Jingfang

2011-04-01

The stability and hydrolysis kinetics of a phosphate prodrug, adefovir dipivoxil, in solid formulations were studied. The stability relationship between five solid formulations was explored. An autocatalytic mechanism for hydrolysis could be proposed according to the kinetic behavior which fits the Prout-Tompkins model well. For the classical kinetic models could hardly describe and predict the hydrolysis kinetics of adefovir dipivoxil in solid formulations accurately when the temperature is high, a feedforward multilayer perceptron (MLP) neural network was constructed to model the hydrolysis kinetics. The build-in approaches in Weka, such as lazy classifiers and rule-based learners (IBk, KStar, DecisionTable and M5Rules), were used to verify the performance of MLP. The predictability of the models was evaluated by 10-fold cross-validation and an external test set. It reveals that MLP should be of general applicability proposing an alternative efficient way to model and predict autocatalytic hydrolysis kinetics for phosphate prodrugs.

Soft computing techniques toward modeling the water supplies of Cyprus.

PubMed

Iliadis, L; Maris, F; Tachos, S

2011-10-01

This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. Initially, ε-Regression Support Vector Machines (ε-RSVM) and fuzzy weighted ε-RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers. Copyright © 2011 Elsevier Ltd. All rights reserved.
Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.

PubMed

Liu, Bin; Wu, Hao; Zhang, Deyuan; Wang, Xiaolong; Chou, Kuo-Chen

2017-02-21

To expedite the pace in conducting genome/proteome analysis, we have developed a Python package called Pse-Analysis. The powerful package can automatically complete the following five procedures: (1) sample feature extraction, (2) optimal parameter selection, (3) model training, (4) cross validation, and (5) evaluating prediction quality. All the work a user needs to do is to input a benchmark dataset along with the query biological sequences concerned. Based on the benchmark dataset, Pse-Analysis will automatically construct an ideal predictor, followed by yielding the predicted results for the submitted query samples. All the aforementioned tedious jobs can be automatically done by the computer. Moreover, the multiprocessing technique was adopted to enhance computational speed by about 6 folds. The Pse-Analysis Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/Pse-Analysis/, and can be directly run on Windows, Linux, and Unix.
In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

PubMed

Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

2011-03-01

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.
Automated measurement of vocal fold vibratory asymmetry from high-speed videoendoscopy recordings.

PubMed

Mehta, Daryush D; Deliyski, Dimitar D; Quatieri, Thomas F; Hillman, Robert E

2011-02-01

In prior work, a manually derived measure of vocal fold vibratory phase asymmetry correlated to varying degrees with visual judgments made from laryngeal high-speed videoendoscopy (HSV) recordings. This investigation extended this work by establishing an automated HSV-based framework to quantify 3 categories of vocal fold vibratory asymmetry. HSV-based analysis provided for cycle-to-cycle estimates of left-right phase asymmetry, left-right amplitude asymmetry, and axis shift during glottal closure for 52 speakers with no vocal pathology producing comfortable and pressed phonation. An initial cross-validation of the automated left-right phase asymmetry measure was performed by correlating the measure with other objective and subjective assessments of phase asymmetry. Vocal fold vibratory asymmetry was exhibited to a similar extent in both comfortable and pressed phonations. The automated measure of left-right phase asymmetry strongly correlated with manually derived measures and moderately correlated with visual-perceptual ratings. Correlations with the visual-perceptual ratings remained relatively consistent as the automated measure was derived from kymograms taken at different glottal locations. An automated HSV-based framework for the quantification of vocal fold vibratory asymmetry was developed and initially validated. This framework serves as a platform for investigating relationships between vocal fold tissue motion and acoustic measures of voice function.
Environmental drivers of spatial patterns of topsoil nitrogen and phosphorus under monsoon conditions in a complex terrain of South Korea

PubMed Central

Choi, Kwanghun; Spohn, Marie; Park, Soo Jin; Huwe, Bernd; Ließ, Mareike

2017-01-01

Nitrogen (N) and phosphorus (P) in topsoils are critical for plant nutrition. Relatively little is known about the spatial patterns of N and P in the organic layer of mountainous landscapes. Therefore, the spatial distributions of N and P in both the organic layer and the A horizon were analyzed using a light detection and ranging (LiDAR) digital elevation model and vegetation metrics. The objective of the study was to analyze the effect of vegetation and topography on the spatial patterns of N and P in a small watershed covered by forest in South Korea. Soil samples were collected using the conditioned latin hypercube method. LiDAR vegetation metrics, the normalized difference vegetation index (NDVI), and terrain parameters were derived as predictors. Spatial explicit predictions of N/P ratios were obtained using a random forest with uncertainty analysis. We tested different strategies of model validation (repeated 2-fold to 20-fold and leave-one-out cross validation). Repeated 10-fold cross validation was selected for model validation due to the comparatively high accuracy and low variance of prediction. Surface curvature was the best predictor of P contents in the organic layer and in the A horizon, while LiDAR vegetation metrics and NDVI were important predictors of N in the organic layer. N/P ratios increased with surface curvature and were higher on the convex upper slope than on the concave lower slope. This was due to P enrichment of the soil on the lower slope and a more even spatial distribution of N. Our digital soil maps showed that the topsoils on the upper slopes contained relatively little P. These findings are critical for understanding N and P dynamics in mountainous ecosystems. PMID:28837590
Direct Validation of Differential Prediction.

ERIC Educational Resources Information Center

Lunneborg, Clifford E.

Using academic achievement data for 655 University students, direct validation of differential predictions based on a battery of aptitude/achievement measures selected for their differential prediction efficiency was attempted. In the cross-validation of the prediction of actual differences among five academic area GPA's, this set of differential…
Non-destructive Techniques for Classifying Aircraft Coating Degradation

DTIC Science & Technology

2015-03-26

model is bidirectional reflectance distribution func- tions ( BRDF ) which describes how much radiation is reflected for each solid angle and each...incident angle. An intermediate model between ideal reflectors and BRDF is to assume all reflectance is a combination of diffuse and specular reflectance...19 K-Fold Cross Validation
Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

USGS Publications Warehouse

Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

2012-01-01

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.
Novel Screening Tool for Stroke Using Artificial Neural Network.

PubMed

Abedi, Vida; Goyal, Nitin; Tsivgoulis, Georgios; Hosseinichimeh, Niyousha; Hontecillas, Raquel; Bassaganya-Riera, Josep; Elijovich, Lucas; Metter, Jeffrey E; Alexandrov, Anne W; Liebeskind, David S; Alexandrov, Andrei V; Zand, Ramin

2017-06-01

The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method. A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3). Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination. © 2017 American Heart Association, Inc.
Watch-Dog: Detecting Self-Harming Activities From Wrist Worn Accelerometers.

PubMed

Bharti, Pratool; Panwar, Anurag; Gopalakrishna, Ganesh; Chellappan, Sriram

2018-05-01

In a 2012 survey, in the United States alone, there were more than 35 000 reported suicides with approximately 1800 of being psychiatric inpatients. Recent Centers for Disease Control and Prevention (CDC) reports indicate an upward trend in these numbers. In psychiatric facilities, staff perform intermittent or continuous observation of patients manually in order to prevent such tragedies, but studies show that they are insufficient, and also consume staff time and resources. In this paper, we present the Watch-Dog system, to address the problem of detecting self-harming activities when attempted by in-patients in clinical settings. Watch-Dog comprises of three key components-Data sensed by tiny accelerometer sensors worn on wrists of subjects; an efficient algorithm to classify whether a user is active versus dormant (i.e., performing a physical activity versus not performing any activity); and a novel decision selection algorithm based on random forests and continuity indices for fine grained activity classification. With data acquired from 11 subjects performing a series of activities (both self-harming and otherwise), Watch-Dog achieves a classification accuracy of , , and for same-user 10-fold cross-validation, cross-user 10-fold cross-validation, and cross-user leave-one-out evaluation, respectively. We believe that the problem addressed in this paper is practical, important, and timely. We also believe that our proposed system is practically deployable, and related discussions are provided in this paper.
Common measure of quality of life for people with systemic sclerosis across seven European countries: a cross-sectional study.

PubMed

Ndosi, Mwidimi; Alcacer-Pitarch, Begonya; Allanore, Yannick; Del Galdo, Francesco; Frerix, Marc; García-Díaz, Sílvia; Hesselstrand, Roger; Kendall, Christine; Matucci-Cerinic, Marco; Mueller-Ladner, Ulf; Sandqvist, Gunnel; Torrente-Segarra, Vicenç; Schmeiser, Tim; Sierakowska, Matylda; Sierakowska, Justyna; Sierakowski, Stanslaw; Redmond, Anthony

2018-02-20

The aim of this study was to adapt the Systemic Sclerosis Quality of Life Questionnaire (SScQoL) into six European cultures and validate it as a common measure of quality of life in systemic sclerosis (SSc). This was a seven-country (Germany, France, Italy, Poland, Spain, Sweden and UK) cross-sectional study. A forward-backward translation process was used to adapt the English SScQoL into target languages. SScQoL was completed by patients with SSc, then data were validated against the Rasch model. To correct local response dependency, items were grouped into the following subscales: function, emotion, sleep, social and pain and reanalysed for fit to the model, unidimensionality and cross-cultural equivalence. The adaptation of the SScQoL was seamless in all countries except Germany. Cross-cultural validation included 1080 patients with a mean age 58.0 years (SD 13.9) and 87% were women. Local dependency was evident in individual country data. Grouping items into testlets corrected the local dependency in most country specific data. Fit to the model, reliability and unidimensionality was achieved in six-country data after cross-cultural adjustment for Italy in the social subscale. The SScQoL was then calibrated into an interval level scale. The individual SScQoL items have translated well into five languages and overall, the scale maintained its construct validity, working well as a five-subscale questionnaire. Measures of quality of life in SSc can be directly compared across five countries (France, Poland Spain, Sweden and UK). Data from Italy are also comparable with the other five countries although require an adjustment. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Overexpression of Plasminogen Activator Inhibitor-1 in Advanced Gastric Cancer with Aggressive Lymph Node Metastasis

PubMed Central

Suh, Yun-Suhk; Yu, Jieun; Kim, Byung Chul; Choi, Boram; Han, Tae-Su; Ahn, Hye Seong; Kong, Seong-Ho; Lee, Hyuk-Joon; Kim, Woo Ho; Yang, Han-Kwang

2015-01-01

Purpose The purpose of this study is to investigate differentially expressed genes using DNA microarray between advanced gastric cancer (AGC) with aggressive lymph node (LN) metastasis and that with a more advanced tumor stage but without LN metastasis. Materials and Methods Five sample pairs of gastric cancer tissue and normal gastric mucosa were taken from three patients with T3N3 stage (highN) and two with T4N0 stage (lowN). Data from triplicate DNA microarray experiments were analyzed, and candidate genes were identified using a volcano plot that showed ≥ 2-fold differential expression and were significant by Welch's t test (p < 0.05) between highN and lowN. Those selected genes were validated independently by reverse-transcriptase–polymerase chain reaction (RT-PCR) using five AGC patients, and tissue-microarray (TMA) comprising 47 AGC patients. Results CFTR, LAMC2, SERPINE2, F2R, MMP7, FN1, TIMP1, plasminogen activator inhibitor-1 (PAI-1), ITGB8, SDS, and TMPRSS4 were commonly up-regulated over 2-fold in highN. REG3A, CD24, ITLN1, and WBP5 were commonly down-regulated over 2-fold in lowN. Among these genes, overexpression of PAI-1 was validated by RT-PCR, and TMA showed 16.7% (7/42) PAI-1 expression in T3N3, but none (0/5) in T4N0 (p=0.393). Conclusion DNA microarray analysis and validation by RT-PCR and TMA showed that overexpression of PAI-1 is related to aggressive LN metastasis in AGC. PMID:25687870
Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…
Validation of the Technology Acceptance Measure for Pre-Service Teachers (TAMPST) on a Malaysian Sample: A Cross-Cultural Study

ERIC Educational Resources Information Center

Teo, Timothy

2010-01-01

Purpose: The purpose of this paper is to assess the cross-cultural validity of the technology acceptance measure for pre-service teachers (TAMPST) on a Malaysian sample. Design/methodology/approach: A total of 193 pre-service teachers from a Malaysian university completed a survey questionnaire measuring their responses to five constructs in the…
Parkinson's disease detection based on dysphonia measurements

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-04-01

Assessing dysphonic symptoms is a noninvasive and effective approach to detect Parkinson's disease (PD) in patients. The main purpose of this study is to investigate the effect of different dysphonia measurements on PD detection by support vector machine (SVM). Seven categories of dysphonia measurements are considered. Experimental results from ten-fold cross-validation technique demonstrate that vocal fundamental frequency statistics yield the highest accuracy of 88 % ± 0.04. When all dysphonia measurements are employed, the SVM classifier achieves 94 % ± 0.03 accuracy. A refinement of the original patterns space by removing dysphonia measurements with similar variation across healthy and PD subjects allows achieving 97.03 % ± 0.03 accuracy. The latter performance is larger than what is reported in the literature on the same dataset with ten-fold cross-validation technique. Finally, it was found that measures of ratio of noise to tonal components in the voice are the most suitable dysphonic symptoms to detect PD subjects as they achieve 99.64 % ± 0.01 specificity. This finding is highly promising for understanding PD symptoms.
A Computational Model for Predicting RNase H Domain of Retrovirus.

PubMed

Wu, Sijia; Zhang, Xinman; Han, Jiuqiang

2016-01-01

RNase H (RNH) is a pivotal domain in retrovirus to cleave the DNA-RNA hybrid for continuing retroviral replication. The crucial role indicates that RNH is a promising drug target for therapeutic intervention. However, annotated RNHs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. In this work, a computational RNH model was proposed to annotate new putative RNHs (np-RNHs) in the retroviruses. It basically predicts RNH domains through recognizing their start and end sites separately with SVM method. The classification accuracy rates are 100%, 99.01% and 97.52% respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Subsequently, this model discovered 14,033 np-RNHs after scanning sequences without RNH annotations. All these predicted np-RNHs and annotated RNHs were employed to analyze the length, hydrophobicity and evolutionary relationship of RNH domains. They are all related to retroviral genera, which validates the classification of retroviruses to a certain degree. In the end, a software tool was designed for the application of our prediction model. The software together with datasets involved in this paper can be available for free download at https://sourceforge.net/projects/rhtool/files/?source=navbar.
LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
Mapping the Transmission Risk of Zika Virus using Machine Learning Models.

PubMed

Jiang, Dong; Hao, Mengmeng; Ding, Fangyu; Fu, Jingying; Li, Meng

2018-06-19

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment. Copyright © 2018. Published by Elsevier B.V.
GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

PubMed

Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

2018-03-01

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.
Intelligent wear mode identification system for marine diesel engines based on multi-level belief rule base methodology

NASA Astrophysics Data System (ADS)

Yan, Xinping; Xu, Xiaojian; Sheng, Chenxing; Yuan, Chengqing; Li, Zhixiong

2018-01-01

Wear faults are among the chief causes of main-engine damage, significantly influencing the secure and economical operation of ships. It is difficult for engineers to utilize multi-source information to identify wear modes, so an intelligent wear mode identification model needs to be developed to assist engineers in diagnosing wear faults in diesel engines. For this purpose, a multi-level belief rule base (BBRB) system is proposed in this paper. The BBRB system consists of two-level belief rule bases, and the 2D and 3D characteristics of wear particles are used as antecedent attributes on each level. Quantitative and qualitative wear information with uncertainties can be processed simultaneously by the BBRB system. In order to enhance the efficiency of the BBRB, the silhouette value is adopted to determine referential points and the fuzzy c-means clustering algorithm is used to transform input wear information into belief degrees. In addition, the initial parameters of the BBRB system are constructed on the basis of expert-domain knowledge and then optimized by the genetic algorithm to ensure the robustness of the system. To verify the validity of the BBRB system, experimental data acquired from real-world diesel engines are analyzed. Five-fold cross-validation is conducted on the experimental data and the BBRB is compared with the other four models in the cross-validation. In addition, a verification dataset containing different wear particles is used to highlight the effectiveness of the BBRB system in wear mode identification. The verification results demonstrate that the proposed BBRB is effective and efficient for wear mode identification with better performance and stability than competing systems.

Test-retest reliability and cross validation of the functioning everyday with a wheelchair instrument.

PubMed

Mills, Tamara L; Holm, Margo B; Schmeler, Mark

2007-01-01

The purpose of this study was to establish the test-retest reliability and content validity of an outcomes tool designed to measure the effectiveness of seating-mobility interventions on the functional performance of individuals who use wheelchairs or scooters as their primary seating-mobility device. The instrument, Functioning Everyday With a Wheelchair (FEW), is a questionnaire designed to measure perceived user function related to wheelchair/scooter use. Using consumer-generated items, FEW Beta Version 1.0 was developed and test-retest reliability was established. Cross-validation of FEW Beta Version 1.0 was then carried out with five samples of seating-mobility users to establish content validity. Based on the content validity study, FEW Version 2.0 was developed and administered to seating-mobility consumers to examine its test-retest reliability. FEW Beta Version 1.0 yielded an intraclass correlation coefficient (ICC) Model (3,k) of .92, p < .001, and the content validity results revealed that FEW Beta Version 1.0 captured 55% of seating-mobility goals reported by consumers across five samples. FEW Version 2.0 yielded ICC(3,k) = .86, p < .001, and captured 98.5% of consumers' seating-mobility goals. The cross-validation study identified new categories of seating-mobility goals for inclusion in FEW Version 2.0, and the content validity of FEW Version 2.0 was confirmed. FEW Beta Version 1.0 and FEW Version 2.0 were highly stable in their measurement of participants' seating-mobility goals over a 1-week interval.
Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands

PubMed Central

2015-01-01

The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure–activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs. PMID:24410373
Development of Sorting System for Fishes by Feed-forward Neural Networks Using Rotation Invariant Features

NASA Astrophysics Data System (ADS)

Shiraishi, Yuhki; Takeda, Fumiaki

In this research, we have developed a sorting system for fishes, which is comprised of a conveyance part, a capturing image part, and a sorting part. In the conveyance part, we have developed an independent conveyance system in order to separate one fish from an intertwined group of fishes. After the image of the separated fish is captured in the capturing part, a rotation invariant feature is extracted using two-dimensional fast Fourier transform, which is the mean value of the power spectrum with the same distance from the origin in the spectrum field. After that, the fishes are classified by three-layered feed-forward neural networks. The experimental results show that the developed system classifies three kinds of fishes captured in various angles with the classification ratio of 98.95% for 1044 captured images of five fishes. The other experimental results show the classification ratio of 90.7% for 300 fishes by 10-fold cross validation method.
Prediction of fatty acid-binding residues on protein surfaces with three-dimensional probability distributions of interacting atoms.

PubMed

Mahalingam, Rajasekaran; Peng, Hung-Pin; Yang, An-Suei

2014-08-01

Protein-fatty acid interaction is vital for many cellular processes and understanding this interaction is important for functional annotation as well as drug discovery. In this work, we present a method for predicting the fatty acid (FA)-binding residues by using three-dimensional probability density distributions of interacting atoms of FAs on protein surfaces which are derived from the known protein-FA complex structures. A machine learning algorithm was established to learn the characteristic patterns of the probability density maps specific to the FA-binding sites. The predictor was trained with five-fold cross validation on a non-redundant training set and then evaluated with an independent test set as well as on holo-apo pair's dataset. The results showed good accuracy in predicting the FA-binding residues. Further, the predictor developed in this study is implemented as an online server which is freely accessible at the following website, http://ismblab.genomics.sinica.edu.tw/. Copyright © 2014 Elsevier B.V. All rights reserved.
Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text
Using patient data similarities to predict radiation pneumonitis via a self-organizing map

NASA Astrophysics Data System (ADS)

Chen, Shifeng; Zhou, Sumin; Yin, Fang-Fang; Marks, Lawrence B.; Das, Shiva K.

2008-01-01

This work investigates the use of the self-organizing map (SOM) technique for predicting lung radiation pneumonitis (RP) risk. SOM is an effective method for projecting and visualizing high-dimensional data in a low-dimensional space (map). By projecting patients with similar data (dose and non-dose factors) onto the same region of the map, commonalities in their outcomes can be visualized and categorized. Once built, the SOM may be used to predict pneumonitis risk by identifying the region of the map that is most similar to a patient's characteristics. Two SOM models were developed from a database of 219 lung cancer patients treated with radiation therapy (34 clinically diagnosed with Grade 2+ pneumonitis). The models were: SOMall built from all dose and non-dose factors and, for comparison, SOMdose built from dose factors alone. Both models were tested using ten-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Models SOMall and SOMdose yielded ten-fold cross-validated ROC areas of 0.73 (sensitivity/specificity = 71%/68%) and 0.67 (sensitivity/specificity = 63%/66%), respectively. The significant difference between the cross-validated ROC areas of these two models (p < 0.05) implies that non-dose features add important information toward predicting RP risk. Among the input features selected by model SOMall, the two with highest impact for increasing RP risk were: (a) higher mean lung dose and (b) chemotherapy prior to radiation therapy. The SOM model developed here may not be extrapolated to treatment techniques outside that used in our database, such as several-field lung intensity modulated radiation therapy or gated radiation therapy.
Support vector machines and generalisation in HEP

NASA Astrophysics Data System (ADS)

Bevan, Adrian; Gamboa Goñi, Rodrigo; Hays, Jon; Stevenson, Tom

2017-10-01

We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for H → τ + τ - at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.
Cross-cultural validation of Lupus Impact Tracker in five European clinical practice settings.

PubMed

Schneider, Matthias; Mosca, Marta; Pego-Reigosa, José-Maria; Gunnarsson, Iva; Maurel, Frédérique; Garofano, Anna; Perna, Alessandra; Porcasi, Rolando; Devilliers, Hervé

2017-05-01

The aim was to evaluate the cross-cultural validity of the Lupus Impact Tracker (LIT) in five European countries and to assess its acceptability and feasibility from the patient and physician perspectives. A prospective, observational, cross-sectional and multicentre validation study was conducted in clinical settings. Before the visit, patients completed LIT, Short Form 36 (SF-36) and care satisfaction questionnaires. During the visit, physicians assessed disease activity [Safety of Estrogens in Lupus Erythematosus National Assessment (SELENA)-SLEDAI], organ damage [SLICC/ACR damage index (SDI)] and flare occurrence. Cross-cultural validity was assessed using the Differential Item Functioning method. Five hundred and sixty-nine SLE patients were included by 25 specialists; 91.7% were outpatients and 89.9% female, with mean age 43.5 (13.0) years. Disease profile was as follows: 18.3% experienced flares; mean SELENA-SLEDAI score 3.4 (4.5); mean SDI score 0.8 (1.4); and SF-36 mean physical and mental component summary scores: physical component summary 42.8 (10.8) and mental component summary 43.0 (12.3). Mean LIT score was 34.2 (22.3) (median: 32.5), indicating that lupus moderately impacted patients' daily life. A cultural Differential Item Functioning of negligible magnitude was detected across countries (pseudo- R 2 difference of 0.01-0.04). Differences were observed between LIT scores and Physician Global Assessment, SELENA-SLEDAI, SDI scores = 0 (P < 0.035) and absence of flares (P = 0.004). The LIT showed a strong association with SF-36 physical and social role functioning, vitality, bodily pain and mental health (P < 0.001). The LIT was well accepted by patients and physicians. It was reliable, with Cronbach α coefficients ranging from 0.89 to 0.92 among countries. The LIT is validated in the five participating European countries. The results show its reliability and cultural invariability across countries. They suggest that LIT can be used in routine clinical practice to evaluate and follow patient-reported outcomes in order to improve patient-physician interaction. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Predicting turns in proteins with a unified model.

PubMed

Song, Qi; Li, Tonghua; Cong, Peisheng; Sun, Jiangming; Li, Dapeng; Tang, Shengnan

2012-01-01

Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.
Predicting Turns in Proteins with a Unified Model

PubMed Central

Song, Qi; Li, Tonghua; Cong, Peisheng; Sun, Jiangming; Li, Dapeng; Tang, Shengnan

2012-01-01

Motivation Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. Results In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications. PMID:23144872
Classification of echolocation clicks from odontocetes in the Southern California Bight.

PubMed

Roch, Marie A; Klinck, Holger; Baumann-Pickering, Simone; Mellinger, David K; Qui, Simon; Soldevilla, Melissa S; Hildebrand, John A

2011-01-01

This study presents a system for classifying echolocation clicks of six species of odontocetes in the Southern California Bight: Visually confirmed bottlenose dolphins, short- and long-beaked common dolphins, Pacific white-sided dolphins, Risso's dolphins, and presumed Cuvier's beaked whales. Echolocation clicks are represented by cepstral feature vectors that are classified by Gaussian mixture models. A randomized cross-validation experiment is designed to provide conditions similar to those found in a field-deployed system. To prevent matched conditions from inappropriately lowering the error rate, echolocation clicks associated with a single sighting are never split across the training and test data. Sightings are randomly permuted before assignment to folds in the experiment. This allows different combinations of the training and test data to be used while keeping data from each sighting entirely in the training or test set. The system achieves a mean error rate of 22% across 100 randomized three-fold cross-validation experiments. Four of the six species had mean error rates lower than the overall mean, with the presumed Cuvier's beaked whale clicks showing the best performance (<2% error rate). Long-beaked common and bottlenose dolphins proved the most difficult to classify, with mean error rates of 53% and 68%, respectively.
Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data.

PubMed

Zhang, Yingtao; Wang, Tao; Liu, Kangkang; Xia, Yao; Lu, Yi; Jing, Qinlong; Yang, Zhicong; Hu, Wenbiao; Lu, Jiahai

2016-02-01

Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information. We obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation. Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845-2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938-0.967). The sensitivity and specificity obtained from k-fold cross-validation was 78.83% and 92.48% respectively, with a forecasting threshold of 3 cases per week; 91.17% and 91.39%, with a threshold of 2 cases; and 85.16% and 87.25% with a threshold of 1 case. The out-of-sample prediction for the epidemics in 2014 also showed satisfactory performance. Our study findings suggest that the occurrence of dengue outbreaks in Guangzhou could impact dengue outbreaks in Zhongshan under suitable weather conditions. Future studies should focus on developing integrated early warning systems for dengue transmission including local weather and human movement.
Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

PubMed

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.
In vivo multi-modality photoacoustic and pulse echo tracking of prostate tumor growth using a window chamber

NASA Astrophysics Data System (ADS)

Bauer, Daniel R.; Olafsson, Ragnar; Montilla, Leonardo G.; Witte, Russell S.

2010-02-01

Understanding the tumor microenvironment is critical to characterizing how cancers operate and predicting how they will eventually respond to treatment. The mouse window chamber model is an excellent tool for cancer research, because it enables high resolution tumor imaging and cross-validation using multiple modalities. We describe a novel multimodality imaging system that incorporates three dimensional (3D) photoacoustics with pulse echo ultrasound for imaging the tumor microenvironment and tracking tissue growth in mice. Three mice were implanted with a dorsal skin flap window chamber. PC-3 prostate tumor cells, expressing green fluorescent protein (GFP), were injected into the skin. The ensuing tumor invasion was mapped using photoacoustic and pulse echo imaging, as well as optical and fluorescent imaging for comparison and cross validation. The photoacoustic imaging and spectroscopy system, consisting of a tunable (680-1000nm) pulsed laser and 25 MHz ultrasound transducer, revealed near infrared absorbing regions, primarily blood vessels. Pulse echo images, obtained simultaneously, provided details of the tumor microstructure and growth with 100-μm3 resolution. The tumor size in all three mice increased between three and five fold during 3+ weeks of imaging. Results were consistent with the optical and fluorescent images. Photoacoustic imaging revealed detailed maps of the tumor vasculature, whereas photoacoustic spectroscopy identified regions of oxygenated and deoxygenated blood vessels. The 3D photoacoustic and pulse echo imaging system provided complementary information to track the tumor microenvironment, evaluate new cancer therapies, and develop molecular imaging agents in vivo. Finally, these safe and noninvasive techniques are potentially applicable for human cancer imaging.
Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.

PubMed

Sriraam, N; Raghu, S

2017-09-02

Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p < 0.05) and z-score (-1.96 > z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey's range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.
Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites

NASA Astrophysics Data System (ADS)

Lu, Cheng-Tsung; Chen, Shu-An; Bretaña, Neil Arvin; Cheng, Tzu-Hsiu; Lee, Tzong-Yi

2011-10-01

In proteins, glutamate (Glu) residues are transformed into γ-carboxyglutamate (Gla) residues in a process called carboxylation. The process of protein carboxylation catalyzed by γ-glutamyl carboxylase is deemed to be important due to its involvement in biological processes such as blood clotting cascade and bone growth. There is an increasing interest within the scientific community to identify protein carboxylation sites. However, experimental identification of carboxylation sites via mass spectrometry-based methods is observed to be expensive, time-consuming, and labor-intensive. Thus, we were motivated to design a computational method for identifying protein carboxylation sites. This work aims to investigate the protein carboxylation by considering the composition of amino acids that surround modification sites. With the implication of a modified residue prefers to be accessible on the surface of a protein, the solvent-accessible surface area (ASA) around carboxylation sites is also investigated. Radial basis function network is then employed to build a predictive model using various features for identifying carboxylation sites. Based on a five-fold cross-validation evaluation, a predictive model trained using the combined features of amino acid sequence (AA20D), amino acid composition, and ASA, yields the highest accuracy at 0.874. Furthermore, an independent test done involving data not included in the cross-validation process indicates that in silico identification is a feasible means of preliminary analysis. Additionally, the predictive method presented in this work is implemented as Carboxylator (http://csb.cse.yzu.edu.tw/Carboxylator/), a web-based tool for identifying carboxylated proteins with modification sites in order to help users in investigating γ-glutamyl carboxylation.
Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.

PubMed

Ferrández, Oscar; South, Brett R; Shen, Shuying; Friedlin, F Jeffrey; Samore, Matthew H; Meystre, Stéphane M

2012-07-27

The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI), which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act "Safe Harbor" method.This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA) clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance. We installed and evaluated five text de-identification systems "out-of-the-box" using a corpus of VHA clinical documents. The systems based on machine learning methods were trained with the 2006 i2b2 de-identification corpora and evaluated with our VHA corpus, and also evaluated with a ten-fold cross-validation experiment using our VHA corpus. We counted exact, partial, and fully contained matches with reference annotations, considering each PHI type separately, or only one unique 'PHI' category. Performance of the systems was assessed using recall (equivalent to sensitivity) and precision (equivalent to positive predictive value) metrics, as well as the F(2)-measure. Overall, systems based on rules and pattern matching achieved better recall, and precision was always better with systems based on machine learning approaches. The highest "out-of-the-box" F(2)-measure was 67% for partial matches; the best precision and recall were 95% and 78%, respectively. Finally, the ten-fold cross validation experiment allowed for an increase of the F(2)-measure to 79% with partial matches. The "out-of-the-box" evaluation of text de-identification systems provided us with compelling insight about the best methods for de-identification of VHA clinical documents. The errors analysis demonstrated an important need for customization to PHI formats specific to VHA documents. This study informed the planning and development of a "best-of-breed" automatic de-identification application for VHA clinical text.
Prediction of drug indications based on chemical interactions and chemical similarities.

PubMed

Huang, Guohua; Lu, Yin; Lu, Changhong; Zheng, Mingyue; Cai, Yu-Dong

2015-01-01

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.
Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

PubMed Central

Huang, Guohua; Lu, Yin; Lu, Changhong; Cai, Yu-Dong

2015-01-01

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs. PMID:25821813
Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics.

PubMed

Mahajan, Ruhi; Viangteeravat, Teeradache; Akbilgic, Oguz

2017-12-01

A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools. Copyright © 2017 Elsevier B.V. All rights reserved.

Tumour gene expression predicts response to cetuximab in patients with KRAS wild-type metastatic colorectal cancer.

PubMed

Baker, J B; Dutta, D; Watson, D; Maddala, T; Munneke, B M; Shak, S; Rowinsky, E K; Xu, L-A; Harbison, C T; Clark, E A; Mauro, D J; Khambata-Ford, S

2011-02-01

Although it is accepted that metastatic colorectal cancers (mCRCs) that carry activating mutations in KRAS are unresponsive to anti-epidermal growth factor receptor (EGFR) monoclonal antibodies, a significant fraction of KRAS wild-type (wt) mCRCs are also unresponsive to anti-EGFR therapy. Genes encoding EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are promising gene expression-based markers but have not been incorporated into a test to dichotomise KRAS wt mCRC patients with respect to sensitivity to anti-EGFR treatment. We used RT-PCR to test 110 candidate gene expression markers in primary tumours from 144 KRAS wt mCRC patients who received monotherapy with the anti-EGFR antibody cetuximab. Results were correlated with multiple clinical endpoints: disease control, objective response, and progression-free survival (PFS). Expression of many of the tested candidate genes, including EREG and AREG, strongly associate with all clinical endpoints. Using multivariate analysis with two-layer five-fold cross-validation, we constructed a four-gene predictive classifier. Strikingly, patients below the classifier cutpoint had PFS and disease control rates similar to those of patients with KRAS mutant mCRC. Gene expression appears to identify KRAS wt mCRC patients who receive little benefit from cetuximab. It will be important to test this model in an independent validation study.
Geometry and Kinematics of Fault-Propagation Folds with Variable Interlimb Angles

NASA Astrophysics Data System (ADS)

Dhont, D.; Jabbour, M.; Hervouet, Y.; Deroin, J.

2009-12-01

Fault-propagation folds are common features in foreland basins and fold-and-thrust belts. Several conceptual models have been proposed to account for their geometry and kinematics. It is generally accepted that the shape of fault-propagation folds depends directly from both the amount of displacement along the basal decollement level and the dip angle of the ramp. Among these, the variable interlimb angle model proposed by Mitra (1990) is based on a folding kinematics that is able to explain open and close natural folds. However, the application of this model is limited because the geometric evolution and thickness variation of the fold directly depend on imposed parameters such as the maximal value of the ramp height. Here, we use the ramp and the interlimb angles as input data to develop a forward fold modelling accounting for thickness variations in the forelimb. The relationship between the fold amplitude and fold wavelength are subsequently applied to build balanced geologic cross-sections from surface parameters only, and to propose a kinematic restoration of the folding through time. We considered three natural examples to validate the variable interlimb angle model. Observed thickness variations in the forelimb of the Turner Valley anticline in the Alberta foothills of Canada precisely correspond to the theoretical values proposed by our model. Deep reconstruction of the Alima anticline in the southern Tunisian Atlas implies that the decollement level is localized in the Triassic-Liassic series, as highlighted by seismic imaging. Our kinematic reconstruction of the Ucero anticline in the Spanish Castilian mountains is also in agreement with the anticline geometry derived from two cross-sections. The variable interlimb angle model implies that the fault-propagation fold can be symmetric, normal asymmetric (with a greater dip value in the forelimb than in the backlimb), or reversely asymmetric (with greater dip in the backlimb) depending on the shortening amount. This model allows also: (i) to easily explain folds with wide variety of geometries; (ii) to understand the deep architecture of anticlines; and (iii) to deduce the kinematic evolution of folding with time. Mitra, S., 1990, Fault-propagation folds: geometry, kinematic evolution, and hydrocarbon traps. AAPG Bulletin, v. 74, no. 6, p. 921-945.
Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

PubMed Central

Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

2013-01-01

Objective Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Design Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Patients Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. Measurements and Results To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Conclusions Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients. PMID:24391903
Automatic detection of whole night snoring events using non-contact microphone.

PubMed

Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

2013-01-01

Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2), m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.
Development and Cross-National Validation of a Laboratory Classroom Environment Instrument for Senior High School Science.

ERIC Educational Resources Information Center

Fraser, Barry J.; And Others

1993-01-01

Describes the development of the Science Laboratory Environment Inventory (SLEI) instrument for assessing perceptions of the psychosocial environment in science laboratory classrooms, and reports validation information for samples of senior high school students from six different countries. The SLEI assesses five dimensions of the actual and…
RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.
Solving protein structures using short-distance cross-linking constraints as a guide for discrete molecular dynamics simulations

PubMed Central

Brodie, Nicholas I.; Popov, Konstantin I.; Petrotchenko, Evgeniy V.; Dokholyan, Nikolay V.; Borchers, Christoph H.

2017-01-01

We present an integrated experimental and computational approach for de novo protein structure determination in which short-distance cross-linking data are incorporated into rapid discrete molecular dynamics (DMD) simulations as constraints, reducing the conformational space and achieving the correct protein folding on practical time scales. We tested our approach on myoglobin and FK506 binding protein—models for α helix–rich and β sheet–rich proteins, respectively—and found that the lowest-energy structures obtained were in agreement with the crystal structure, hydrogen-deuterium exchange, surface modification, and long-distance cross-linking validation data. Our approach is readily applicable to other proteins with unknown structures. PMID:28695211
Solving protein structures using short-distance cross-linking constraints as a guide for discrete molecular dynamics simulations.

PubMed

Brodie, Nicholas I; Popov, Konstantin I; Petrotchenko, Evgeniy V; Dokholyan, Nikolay V; Borchers, Christoph H

2017-07-01

We present an integrated experimental and computational approach for de novo protein structure determination in which short-distance cross-linking data are incorporated into rapid discrete molecular dynamics (DMD) simulations as constraints, reducing the conformational space and achieving the correct protein folding on practical time scales. We tested our approach on myoglobin and FK506 binding protein-models for α helix-rich and β sheet-rich proteins, respectively-and found that the lowest-energy structures obtained were in agreement with the crystal structure, hydrogen-deuterium exchange, surface modification, and long-distance cross-linking validation data. Our approach is readily applicable to other proteins with unknown structures.
Aromatic Cluster Sensor of Protein Folding: Near-UV Electronic Circular Dichroism Bands Assigned to Fold Compactness.

PubMed

Farkas, Viktor; Jákli, Imre; Tóth, Gábor K; Perczel, András

2016-09-19

Both far- and near-UV electronic circular dichroism (ECD) spectra have bands sensitive to thermal unfolding of Trp and Tyr residues containing proteins. Beside spectral changes at 222 nm reporting secondary structural variations (far-UV range), L b bands (near-UV range) are applicable as 3D-fold sensors of protein's core structure. In this study we show that both L b (Tyr) and L b (Trp) ECD bands could be used as sensors of fold compactness. ECD is a relative method and thus requires NMR referencing and cross-validation, also provided here. The ensemble of 204 ECD spectra of Trp-cage miniproteins is analysed as a training set for "calibrating" Trp↔Tyr folded systems of known NMR structure. While in the far-UV ECD spectra changes are linear as a function of the temperature, near-UV ECD data indicate a non-linear and thus, cooperative unfolding mechanism of these proteins. Ensemble of ECD spectra deconvoluted gives both conformational weights and insight to a protein folding↔unfolding mechanism. We found that the L b 293 band is reporting on the 3D-structure compactness. In addition, the pure near-UV ECD spectrum of the unfolded state is described here for the first time. Thus, ECD folding information now validated can be applied with confidence in a large thermal window (5≤T≤85 °C) compared to NMR for studying the unfolding of Trp↔Tyr residue pairs. In conclusion, folding propensities of important proteins (RNA polymerase II, ubiquitin protein ligase, tryptase-inhibitor etc.) can now be analysed with higher confidence. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950
Classification of burn wounds using support vector machines

NASA Astrophysics Data System (ADS)

Acha, Begona; Serrano, Carmen; Palencia, Sergio; Murillo, Juan Jose

2004-05-01

The purpose of this work is to improve a previous method developed by the authors for the classification of burn wounds into their depths. The inputs of the system are color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. Our previous work consisted in segmenting the burn wound from the rest of the image and classifying the burn into its depth. In this paper we focus on the classification problem only. We already proposed to use a Fuzzy-ARTMAP neural network (NN). However, we may take advantage of new powerful classification tools such as Support Vector Machines (SVM). We apply the five-folded cross validation scheme to divide the database into training and validating sets. Then, we apply a feature selection method for each classifier, which will give us the set of features that yields the smallest classification error for each classifier. Features used to classify are first-order statistical parameters extracted from the L*, u* and v* color components of the image. The feature selection algorithms used are the Sequential Forward Selection (SFS) and the Sequential Backward Selection (SBS) methods. As data of the problem faced here are not linearly separable, the SVM was trained using some different kernels. The validating process shows that the SVM method, when using a Gaussian kernel of variance 1, outperforms classification results obtained with the rest of the classifiers, yielding an error classification rate of 0.7% whereas the Fuzzy-ARTMAP NN attained 1.6 %.
The structure of post-traumatic stress symptoms in survivors of war: confirmatory factor analyses of the Impact of Event Scale--revised.

PubMed

Morina, Nexhmedin; Böhme, Hendryk F; Ajdukovic, Dean; Bogic, Marija; Franciskovic, Tanja; Galeazzi, Gian M; Kucukalic, Abdulah; Lecic-Tosevski, Dusica; Popovski, Mihajlo; Schützwohl, Matthias; Stangier, Ulrich; Priebe, Stefan

2010-08-01

The study aimed at establishing the factor structure of the Impact of Event Scale-Revised (IES-R) in survivors of war. A total sample of 4167 participants with potentially traumatic experiences during the war in Ex-Yugoslavia was split into three samples: two independent samples of people who stayed in the area of conflict and one sample of refugees to Western European countries. Alternative models with three, four, and five factors of post-traumatic symptoms were tested in one sample. The other samples were used for cross-validation. Results indicated that the model of best fit had five factors, i.e., intrusion, avoidance, hyperarousal, numbing, and sleep disturbance. Model superiority was cross-validated in the two other samples. These findings suggest a five-factor model of post-traumatic stress symptoms in war survivors with numbing and sleep disturbance as separate factors in addition to intrusion, avoidance and hyperarousal. (c) 2010 Elsevier Ltd. All rights reserved.
Benchmarking protein classification algorithms via supervised cross-validation.

PubMed

Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

2008-04-24

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.
Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis.

PubMed

Collins, Ryan L; Hu, Ting; Wejse, Christian; Sirugo, Giorgio; Williams, Scott M; Moore, Jason H

2013-02-18

Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB). The study used a previously published data set consisting of 19 candidate single-nucleotide polymorphisms (SNPs) in 321 pulmonary TB cases and 347 healthy controls from Guniea-Bissau in Africa. The ReliefF algorithm was applied first to generate a smaller set of the five most informative SNPs. MDR with 10-fold cross-validation was then applied to look at all possible combinations of two, three, four and five SNPs. The MDR model with the best testing accuracy (TA) consisted of SNPs rs2305619, rs187084, and rs11465421 (TA = 0.588) in PTX3, TLR9 and DC-Sign, respectively. A general 1000-fold permutation test of the null hypothesis of no association confirmed the statistical significance of the model (p = 0.008). An additional 1000-fold permutation test designed specifically to test the linear null hypothesis that the association effects are only additive confirmed the presence of non-additive (i.e. nonlinear) or epistatic effects (p = 0.013). An independent information-gain measure corroborated these results with a third-order epistatic interaction that was stronger than any lower-order associations. We have identified statistically significant evidence for a three-way epistatic interaction that is associated with susceptibility to TB. This interaction is stronger than any previously described one-way or two-way associations. This study highlights the importance of using machine learning methods that are designed to embrace, rather than ignore, the complexity of common diseases such as TB. We recommend future studies of the genetics of TB take into account the possibility that high-order epistatic interactions might play an important role in disease susceptibility.
Detection of resistance, cross-resistance, and stability of resistance to new chemistry insecticides in Bemisia tabaci (Homoptera: Aleyrodidae).

PubMed

Basit, Muhammad; Saeed, Shafqat; Saleem, Mushtaq Ahmad; Denholm, Ian; Shah, Maqbool

2013-06-01

Resistance levels in whitefly, Bemisia tabaci (Gennadius) collections from cotton and sunflower (up to four districts) for five neonicotinoids and two insect growth regulators (IGRs) were investigated for two consecutive years. Based on the LC50(s), all collections showed slight to moderate levels of resistance for the tested insecticides compared with the laboratory susceptible population. The data also indicated that cotton and sunflower collections had similar resistance levels. In comparison (four collections), Vehari collections showed higher resistance for acetamiprid, thiacloprid, and nitenpyram compared with those of others. Average resistance ratios for acetamiprid, thiacloprid, and nitenpyram ranged from 5- to 13-, 4- to 8-, and 9- to 13-fold, respectively. Multan and Vehari collections also exhibited moderate levels (9- to 16-fold) of resistance to buprofezin. Furthermore, toxicity of neonicotinoids against immature stages was equal to that of insect growth regulators. The data also suggested that resistance in the field populations was stable. After selection for four generations with bifenthrin (G1 to G4), resistance to bifenthrin increased to 14-fold compared with the laboratory susceptible population. Selection also increased resistance to fenpropathrin, lambdacyhalothrin, imidacloprid, acetamiprid, and diafenthuron. Cross-resistance and stability of resistance in the field populations is of some concern. Rotation of insecticides having no cross-resistance and targeting the control against immature stages may control the resistant insects, simultaneously reducing the selection pressure imposed.
Breast cancer detection via Hu moment invariant and feedforward neural network

NASA Astrophysics Data System (ADS)

Zhang, Xiaowei; Yang, Jiquan; Nguyen, Elijah

2018-04-01

One of eight women can get breast cancer during all her life. This study used Hu moment invariant and feedforward neural network to diagnose breast cancer. With the help of K-fold cross validation, we can test the out-of-sample accuracy of our method. Finally, we found that our methods can improve the accuracy of detecting breast cancer and reduce the difficulty of judging.
Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.
A Monte Carlo Evaluation of Estimated Parameters of Five Shrinkage Estimate Formuli.

ERIC Educational Resources Information Center

Newman, Isadore; And Others

A Monte Carlo study was conducted to estimate the efficiency of and the relationship between five equations and the use of cross validation as methods for estimating shrinkage in multiple correlations. Two of the methods were intended to estimate shrinkage to population values and the other methods were intended to estimate shrinkage from sample…
First record of plicidentine in Synapsida and patterns of tooth root shape change in Early Permian sphenacodontians.

PubMed

Brink, Kirstin S; LeBlanc, Aaron R H; Reisz, Robert R

2014-11-01

Recent histological studies have revealed a diversity of dental features in Permo-Carboniferous tetrapods. Here, we report on the occurrence of plicidentine (infolded dentine around the base of the tooth root) in Sphenacodontia, the first such documentation in Synapsida, the clade that includes mammals. Five taxa were examined histologically, Ianthodon schultzei, Sphenacodon ferocior, Dimetrodon limbatus, Dimetrodon grandis, and Secodontosaurus obtusidens. The tooth roots of Ianthodon possess multiple folds, which is generally viewed as the primitive condition for amniotes. Sphenacodon and D. limbatus have distinctive "four-leaf clover"-shaped roots in cross section, whereas Secodontosaurus has an elongate square shape with only subtle folding. The most derived and largest taxon examined in this study, D. grandis, has rounded roots in cross section and therefore no plicidentine. This pattern of a loss of plicidentine in sphenacodontids supports previous functional hypotheses of plicidentine, where teeth with shallow roots require folds to increase the area of attachment to the tooth-bearing element, whereas teeth with long roots do not. This pattern may also reflect differences in diet between co-occurring sphenacodontids as well as changes in feeding niche through time, specifically in the apex predator Dimetrodon.
Infused autograft lymphocyte-to-monocyte ratio and survival in T-cell lymphoma post-autologous peripheral blood hematopoietic stem cell transplantation.

PubMed

Porrata, Luis F; Inwards, David J; Ansell, Stephen M; Micallef, Ivana N; Johnston, Patrick B; Hogan, William J; Markovic, Svetomir N

2015-07-03

The infused autograft lymphocyte-to-monocyte ratio (A-LMR) is a prognostic factor for survival in B-cell lymphomas post-autologous peripheral hematopoietic stem cell transplantation (APHSCT). Thus, we set out to investigate if the A-LMR is also a prognostic factor for survival post-APHSCT in T-cell lymphomas. From 1998 to 2014, 109 T-cell lymphoma patients that underwent APHSCT were studied. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to identify the optimal cut-off value of A-LMR for survival analysis and k-fold cross-validation model to validate the A-LMR cut-off value. Univariate and multivariate Cox proportional hazard models were used to assess the prognostic discriminator power of A-LMR. ROC and AUC identified an A-LMR ≥ 1 as the best cut-off value and was validated by k-fold cross-validation. Multivariate analysis showed A-LMR to be an independent prognostic factor for overall survival (OS) and progression-free survival (PFS). Patients with an A-LMR ≥ 1.0 experienced a superior OS and PFS versus patients with an A-LMR < 1.0 [median OS was not reached vs 17.9 months, 5-year OS rates of 87% (95% confidence interval (CI), 75-94%) vs 26% (95% CI, 13-42%), p < 0.0001; median PFS was not reached vs 11.9 months, 5-year PFS rates of 72% (95% CI, 58-83%) vs 16% (95% CI, 6-32%), p < 0.0001]. A-LMR is also a prognostic factor for clinical outcomes in patients with T-cell lymphomas undergoing APHSCT.

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

PubMed

Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).
Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

PubMed

Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

2015-11-01

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.
Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.
Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

NASA Astrophysics Data System (ADS)

Wu, Chaofan; Shen, Huanhuan; Shen, Aihua; Deng, Jinsong; Gan, Muye; Zhu, Jinxia; Xu, Hongwei; Wang, Ke

2016-07-01

Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44 ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.
Predicting the Operational Acceptability of Route Advisories

NASA Technical Reports Server (NTRS)

Evans, Antony; Lee, Paul

2017-01-01

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.
Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways.

PubMed

Gu, Xiang; Liu, Cong-Jian; Wei, Jian-Jie

2017-11-13

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.
Five-dimensional ultrasound system for soft tissue visualization.

PubMed

Deshmukh, Nishikant P; Caban, Jesus J; Taylor, Russell H; Hager, Gregory D; Boctor, Emad M

2015-12-01

A five-dimensional ultrasound (US) system is proposed as a real-time pipeline involving fusion of 3D B-mode data with the 3D ultrasound elastography (USE) data as well as visualization of these fused data and a real-time update capability over time for each consecutive scan. 3D B-mode data assist in visualizing the anatomy of the target organ, and 3D elastography data adds strain information. We investigate the feasibility of such a system and show that an end-to-end real-time system, from acquisition to visualization, can be developed. We present a system that consists of (a) a real-time 3D elastography algorithm based on a normalized cross-correlation (NCC) computation on a GPU; (b) real-time 3D B-mode acquisition and network transfer; (c) scan conversion of 3D elastography and B-mode volumes (if acquired by 4D wobbler probe); and (d) visualization software that fuses, visualizes, and updates 3D B-mode and 3D elastography data in real time. We achieved a speed improvement of 4.45-fold for the threaded version of the NCC-based 3D USE versus the non-threaded version. The maximum speed was 79 volumes/s for 3D scan conversion. In a phantom, we validated the dimensions of a 2.2-cm-diameter sphere scan-converted to B-mode volume. Also, we validated the 5D US system visualization transfer function and detected 1- and 2-cm spherical objects (phantom lesion). Finally, we applied the system to a phantom consisting of three lesions to delineate the lesions from the surrounding background regions of the phantom. A 5D US system is achievable with real-time performance. We can distinguish between hard and soft areas in a phantom using the transfer functions.
Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server.

PubMed

Lee, Kyoungyeul; Lee, Minho; Kim, Dongsup

2017-12-28

The identification of target molecules is important for understanding the mechanism of "target deconvolution" in phenotypic screening and "polypharmacology" of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets. We developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of each target model was tested by employing the ROC curve and the mean score using an internal five-fold cross validation. Moreover, recall rates for top-k targets were calculated to assess the performance of target ranking. A benchmark model using an optimized sampling method and parameters was examined via external validation set. The result shows recall rates of 67.6% and 73.9% for top-11 (1% of the total targets) and top-33, respectively. We provide a website for users to search the top-k targets for query ligands available publicly at http://rfqsar.kaist.ac.kr . The target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probability so that users can estimate how likely a ligand-target interaction is active. The user interface of our web site is user friendly and intuitive, offering useful information and cross references.
A new fold-cross metal mesh filter for suppressing side lobe leakage in terahertz region

NASA Astrophysics Data System (ADS)

Lu, Changgui; Qi, Zhengqing; Guo, Wengao; Cui, Yiping

2018-04-01

In this paper we propose a new type of fold-cross metal mesh band pass filter, which keeps diffraction side lobe far away from the main transmission peak and shows much better side lobe suppression. Both experimental and theoretical studies are made to analyze the mechanism of side lobe. Compared to the traditional cross filter, the fold-cross filter has a much lower side lobe with almost the same central frequency, bandwidth and highest transmission about 98%. Using the photolithography and electroplating techniques, we experimentally extend the distance between the main peak and diffraction side lobe to larger than 1 THz for the fold-cross filter, which is two times larger than the cross filter while maintaining the main peak transmissions of 89% at 1.25 THz for the two structures. This type of single layer substrate-free fold-cross metal structure shows better design flexibility and structure reliability with the introduction of fold arms for metal mesh band pass filters.
Cross-Cultural Validation of the Preventive Health Model for Colorectal Cancer Screening: An Australian Study

ERIC Educational Resources Information Center

Flight, Ingrid H.; Wilson, Carlene J.; McGillivray, Jane; Myers, Ronald E.

2010-01-01

We investigated whether the five-factor structure of the Preventive Health Model for colorectal cancer screening, developed in the United States, has validity in Australia. We also tested extending the model with the addition of the factor Self-Efficacy to Screen using Fecal Occult Blood Test (SESFOBT). Randomly selected men and women aged between…
Use of integrated analogue and numerical modelling to predict tridimensional fracture intensity in fault-related-folds.

NASA Astrophysics Data System (ADS)

Pizzati, Mattia; Cavozzi, Cristian; Magistroni, Corrado; Storti, Fabrizio

2016-04-01

Fracture density pattern predictions with low uncertainty is a fundamental issue for constraining fluid flow pathways in thrust-related anticlines in the frontal parts of thrust-and-fold belts and accretionary prisms, which can also provide plays for hydrocarbon exploration and development. Among the drivers that concur to determine the distribution of fractures in fold-and-thrust-belts, the complex kinematic pathways of folded structures play a key role. In areas with scarce and not reliable underground information, analogue modelling can provide effective support for developing and validating reliable hypotheses on structural architectures and their evolution. In this contribution, we propose a working method that combines analogue and numerical modelling. We deformed a sand-silicone multilayer to eventually produce a non-cylindrical thrust-related anticline at the wedge toe, which was our test geological structure at the reservoir scale. We cut 60 serial cross-sections through the central part of the deformed model to analyze faults and folds geometry using dedicated software (3D Move). The cross-sections were also used to reconstruct the 3D geometry of reference surfaces that compose the mechanical stratigraphy thanks to the use of the software GoCad. From the 3D model of the experimental anticline, by using 3D Move it was possible to calculate the cumulative stress and strain underwent by the deformed reference layers at the end of the deformation and also in incremental steps of fold growth. Based on these model outputs it was also possible to predict the orientation of three main fractures sets (joints and conjugate shear fractures) and their occurrence and density on model surfaces. The next step was the upscaling of the fracture network to the entire digital model volume, to create DFNs.
Validation of the Adolescent Concerns Measure (ACM): evidence from exploratory and confirmatory factor analysis.

PubMed

Ang, Rebecca P; Chong, Wan Har; Huan, Vivien S; Yeo, Lay See

2007-01-01

This article reports the development and initial validation of scores obtained from the Adolescent Concerns Measure (ACM), a scale which assesses concerns of Asian adolescent students. In Study 1, findings from exploratory factor analysis using 619 adolescents suggested a 24-item scale with four correlated factors--Family Concerns (9 items), Peer Concerns (5 items), Personal Concerns (6 items), and School Concerns (4 items). Initial estimates of convergent validity for ACM scores were also reported. The four-factor structure of ACM scores derived from Study 1 was confirmed via confirmatory factor analysis in Study 2 using a two-fold cross-validation procedure with a separate sample of 811 adolescents. Support was found for both the multidimensional and hierarchical models of adolescent concerns using the ACM. Internal consistency and test-retest reliability estimates were adequate for research purposes. ACM scores show promise as a reliable and potentially valid measure of Asian adolescents' concerns.
Tertiary model of a plant cellulose synthase

PubMed Central

Sethaphong, Latsavongsakda; Haigler, Candace H.; Kubicki, James D.; Zimmer, Jochen; Bonetta, Dario; DeBolt, Seth; Yingling, Yaroslava G.

2013-01-01

A 3D atomistic model of a plant cellulose synthase (CESA) has remained elusive despite over forty years of experimental effort. Here, we report a computationally predicted 3D structure of 506 amino acids of cotton CESA within the cytosolic region. Comparison of the predicted plant CESA structure with the solved structure of a bacterial cellulose-synthesizing protein validates the overall fold of the modeled glycosyltransferase (GT) domain. The coaligned plant and bacterial GT domains share a six-stranded β-sheet, five α-helices, and conserved motifs similar to those required for catalysis in other GT-2 glycosyltransferases. Extending beyond the cross-kingdom similarities related to cellulose polymerization, the predicted structure of cotton CESA reveals that plant-specific modules (plant-conserved region and class-specific region) fold into distinct subdomains on the periphery of the catalytic region. Computational results support the importance of the plant-conserved region and/or class-specific region in CESA oligomerization to form the multimeric cellulose–synthesis complexes that are characteristic of plants. Relatively high sequence conservation between plant CESAs allowed mapping of known mutations and two previously undescribed mutations that perturb cellulose synthesis in Arabidopsis thaliana to their analogous positions in the modeled structure. Most of these mutation sites are near the predicted catalytic region, and the confluence of other mutation sites supports the existence of previously undefined functional nodes within the catalytic core of CESA. Overall, the predicted tertiary structure provides a platform for the biochemical engineering of plant CESAs. PMID:23592721
The cross-cultural equivalence of participation instruments: a systematic review.

PubMed

Stevelink, S A M; van Brakel, W H

2013-07-01

Concepts such as health-related quality of life, disability and participation may differ across cultures. Consequently, when assessing such a concept using a measure developed elsewhere, it is important to test its cultural equivalence. Previous research suggested a lack of cultural equivalence testing in several areas of measurement. This paper reviews the process of cross-cultural equivalence testing of instruments to measure participation in society. An existing cultural equivalence framework was adapted and used to assess participation instruments on five categories of equivalence: conceptual, item, semantic, measurement and operational equivalence. For each category, several aspects were rated, resulting in an overall category rating of 'minimal/none', 'partial' or 'extensive'. The best possible overall study rating was five 'extensive' ratings. Articles were included if the instruments focussed explicitly on measuring 'participation' and were theoretically grounded in the ICIDH(-2) or ICF. Cross-validation articles were only included if it concerned an adaptation of an instrument developed in a high or middle-income country to a low-income country or vice versa. Eight cross-cultural validation studies were included in which five participation instruments were tested (Impact on Participation and Autonomy, London Handicap Scale, Perceived Impact and Problem Profile, Craig Handicap Assessment Reporting Technique, Participation Scale). Of these eight studies, only three received at least two 'extensive' ratings for the different categories of equivalence. The majority of the cultural equivalence ratings given were 'partial' and 'minimal/none'. The majority of the 'none/minimal' ratings were given for item and measurement equivalence. The cross-cultural equivalence testing of the participation instruments included leaves much to be desired. A detailed checklist is proposed for designing a cross-validation study. Once a study has been conducted, the checklist can be used to ensure comprehensive reporting of the validation (equivalence) testing process and its results. • Participation instruments are often used in a different cultural setting than initial developed for. • The conceptualization of participation may vary across cultures. Therefore, cultural equivalence – the extent to which an instrument is equally suitable for use in two or more cultures – is an important concept to address. • This review showed that the process of cultural equivalence testing of the included participation instruments was often addressed insufficiently. • Clinicians should be aware that application of participations instruments in a different culture than initially developed for needs prior testing of cultural validity in the next context.
Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

Tong, Tong; Ledig, Christian; Guerrero, Ricardo; Schuh, Andreas; Koikkalainen, Juha; Tolonen, Antti; Rhodius, Hanneke; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W; Soininen, Hilkka; Remes, Anne M; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; Baroni, Marta; Lötjönen, Jyrki; Flier, Wiesje van der; Rueckert, Daniel

2017-01-01

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.
Figure of merit for macrouniformity based on image quality ruler evaluation and machine learning framework

NASA Astrophysics Data System (ADS)

Wang, Weibao; Overall, Gary; Riggs, Travis; Silveston-Keith, Rebecca; Whitney, Julie; Chiu, George; Allebach, Jan P.

2013-01-01

Assessment of macro-uniformity is a capability that is important for the development and manufacture of printer products. Our goal is to develop a metric that will predict macro-uniformity, as judged by human subjects, by scanning and analyzing printed pages. We consider two different machine learning frameworks for the metric: linear regression and the support vector machine. We have implemented the image quality ruler, based on the recommendations of the INCITS W1.1 macro-uniformity team. Using 12 subjects at Purdue University and 20 subjects at Lexmark, evenly balanced with respect to gender, we conducted subjective evaluations with a set of 35 uniform b/w prints from seven different printers with five levels of tint coverage. Our results suggest that the image quality ruler method provides a reliable means to assess macro-uniformity. We then defined and implemented separate features to measure graininess, mottle, large area variation, jitter, and large-scale non-uniformity. The algorithms that we used are largely based on ISO image quality standards. Finally, we used these features computed for a set of test pages and the subjects' image quality ruler assessments of these pages to train the two different predictors - one based on linear regression and the other based on the support vector machine (SVM). Using five-fold cross-validation, we confirmed the efficacy of our predictor.
Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

PubMed Central

Kim, Marlene; Sedykh, Alexander; Chakravarti, Suman K.; Saiakhov, Roustem D.; Zhu, Hao

2014-01-01

Purpose Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time -consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. Methods We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. Results The external predictivity of %F values was poor (R2=0.28, n=995, MAE=24), but was improved (R2=0.40, n=362, MAE=21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F<50% as “low”, %F≥50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%. Conclusions In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models. PMID:24306326
[Determination of calcium and magnesium in tobacco by near-infrared spectroscopy and least squares-support vector machine].

PubMed

Tian, Kuang-da; Qiu, Kai-xian; Li, Zu-hong; Lü, Ya-qiong; Zhang, Qiu-ju; Xiong, Yan-mei; Min, Shun-geng

2014-12-01

The purpose of the present paper is to determine calcium and magnesium in tobacco using NIR combined with least squares-support vector machine (LS-SVM). Five hundred ground and dried tobacco samples from Qujing city, Yunnan province, China, were surveyed by a MATRIX-I spectrometer (Bruker Optics, Bremen, Germany). At the beginning of data processing, outliers of samples were eliminated for stability of the model. The rest 487 samples were divided into several calibration sets and validation sets according to a hybrid modeling strategy. Monte-Carlo cross validation was used to choose the best spectral preprocess method from multiplicative scatter correction (MSC), standard normal variate transformation (SNV), S-G smoothing, 1st derivative, etc., and their combinations. To optimize parameters of LS-SVM model, the multilayer grid search and 10-fold cross validation were applied. The final LS-SVM models with the optimizing parameters were trained by the calibration set and accessed by 287 validation samples picked by Kennard-Stone method. For the quantitative model of calcium in tobacco, Savitzky-Golay FIR smoothing with frame size 21 showed the best performance. The regularization parameter λ of LS-SVM was e16.11, while the bandwidth of the RBF kernel σ2 was e8.42. The determination coefficient for prediction (Rc(2)) was 0.9755 and the determination coefficient for prediction (Rp(2)) was 0.9422, better than the performance of PLS model (Rc(2)=0.9593, Rp(2)=0.9344). For the quantitative analysis of magnesium, SNV made the regression model more precise than other preprocess. The optimized λ was e15.25 and σ2 was e6.32. Rc(2) and Rp(2) were 0.9961 and 0.9301, respectively, better than PLS model (Rc(2)=0.9716, Rp(2)=0.8924). After modeling, the whole progress of NIR scan and data analysis for one sample was within tens of seconds. The overall results show that NIR spectroscopy combined with LS-SVM can be efficiently utilized for rapid and accurate analysis of calcium and magnesium in tobacco.
Cation-induced folding of alginate-bearing bilayer gels: an unusual example of spontaneous folding along the long axis.

PubMed

Athas, Jasmin C; Nguyen, Catherine P; Kummar, Shailaa; Raghavan, Srinivasa R

2018-04-04

The spontaneous folding of flat gel films into tubes is an interesting example of self-assembly. Typically, a rectangular film folds along its short axis when forming a tube; folding along the long axis has been seen only in rare instances when the film is constrained. Here, we report a case where the same free-swelling gel film folds along either its long or short axis depending on the concentration of a solute. Our gels are sandwiches (bilayers) of two layers: a passive layer of cross-linked N,N'-dimethylyacrylamide (DMAA) and an active layer of cross-linked DMAA that also contains chains of the biopolymer alginate. Multivalent cations like Ca2+ and Cu2+ induce these bilayer gels to fold into tubes. The folding occurs instantly when a flat film of the gel is introduced into a solution of these cations. The likely cause for folding is that the active layer stiffens and shrinks (because the alginate chains in it get cross-linked by the cations) whereas the passive layer is unaffected. The resulting mismatch in swelling degree between the two layers creates internal stresses that drive folding. Cations that are incapable of cross-linking alginate, such as Na+ and Mg2+, do not induce gel folding. Moreover, the striking aspect is the direction of folding. When the Ca2+ concentration is high (100 mM or higher), the gels fold along their long axis, whereas when the Ca2+ concentration is low (40 to 80 mM), the gels fold along their short axis. We hypothesize that the folding axis is dictated by the inhomogeneous nature of alginate-cation cross-linking, i.e., that the edges get cross-linked before the faces of the gel. At high Ca2+ concentration, the stiffer edges constrain the folding; in turn, the gel folds such that the longer edges are deformed less, which explains the folding along the long axis. At low Ca2+ concentration, the edges and the faces of the gel are more similar in their degree of cross-linking; therefore, the gel folds along its short axis. An analogy can be made to natural structures (such as leaves and seed pods) where stiff elements provide the directionality for folding.
iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.

PubMed

Xu, Zhao-Chun; Wang, Peng; Qiu, Wang-Ren; Xiao, Xuan

2017-08-15

Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed. To identify the splice donor sites and splice acceptor sites accurately and quickly, a deep sparse auto-encoder model with two hidden layers, called iSS-PC, was constructed based on minimum error law, in which we incorporated twelve physical-chemical properties of the dinucleotides within DNA into PseDNC to formulate given sequence samples via a battery of cross-covariance and auto-covariance transformations. In this paper, five-fold cross-validation test results based on the same benchmark data-sets indicated that the new predictor remarkably outperformed the existing prediction methods in this field. Furthermore, it is expected that many other related problems can be also studied by this approach. To implement classification accurately and quickly, an easy-to-use web-server for identifying slicing sites has been established for free access at: http://www.jci-bioinfo.cn/iSS-PC.

Mirage: a visible signature evaluation tool

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.; Meehan, Alaster J.; Shao, Q. T.; Richards, Noel

2017-10-01

This paper presents the Mirage visible signature evaluation tool, designed to provide a visible signature evaluation capability that will appropriately reflect the effect of scene content on the detectability of targets, providing a capability to assess visible signatures in the context of the environment. Mirage is based on a parametric evaluation of input images, assessing the value of a range of image metrics and combining them using the boosted decision tree machine learning method to produce target detectability estimates. It has been developed using experimental data from photosimulation experiments, where human observers search for vehicle targets in a variety of digital images. The images used for tool development are synthetic (computer generated) images, showing vehicles in many different scenes and exhibiting a wide variation in scene content. A preliminary validation has been performed using k-fold cross validation, where 90% of the image data set was used for training and 10% of the image data set was used for testing. The results of the k-fold validation from 200 independent tests show a prediction accuracy between Mirage predictions of detection probability and observed probability of detection of r(262) = 0:63, p < 0:0001 (Pearson correlation) and a MAE = 0:21 (mean absolute error).
Differential gene expression between African American and European American colorectal cancer patients.

PubMed

Jovov, Biljana; Araujo-Perez, Felix; Sigel, Carlie S; Stratford, Jeran K; McCoy, Amber N; Yeh, Jen Jen; Keku, Temitope

2012-01-01

The incidence and mortality of colorectal cancer (CRC) is higher in African Americans (AAs) than other ethnic groups in the U. S., but reasons for the disparities are unknown. We performed gene expression profiling of sporadic CRCs from AAs vs. European Americans (EAs) to assess the contribution to CRC disparities. We evaluated the gene expression of 43 AA and 43 EA CRC tumors matched by stage and 40 matching normal colorectal tissues using the Agilent human whole genome 4x44K cDNA arrays. Gene and pathway analyses were performed using Significance Analysis of Microarrays (SAM), Ten-fold cross validation, and Ingenuity Pathway Analysis (IPA). SAM revealed that 95 genes were differentially expressed between AA and EA patients at a false discovery rate of ≤5%. Using IPA we determined that most prominent disease and pathway associations of differentially expressed genes were related to inflammation and immune response. Ten-fold cross validation demonstrated that following 10 genes can predict ethnicity with an accuracy of 94%: CRYBB2, PSPH, ADAL, VSIG10L, C17orf81, ANKRD36B, ZNF835, ARHGAP6, TRNT1 and WDR8. Expression of these 10 genes was validated by qRT-PCR in an independent test set of 28 patients (10 AA, 18 EA). Our results are the first to implicate differential gene expression in CRC racial disparities and indicate prominent difference in CRC inflammation between AA and EA patients. Differences in susceptibility to inflammation support the existence of distinct tumor microenvironments in these two patient populations.
Differential Gene Expression between African American and European American Colorectal Cancer Patients

PubMed Central

Jovov, Biljana; Araujo-Perez, Felix; Sigel, Carlie S.; Stratford, Jeran K.; McCoy, Amber N.; Yeh, Jen Jen; Keku, Temitope

2012-01-01

The incidence and mortality of colorectal cancer (CRC) is higher in African Americans (AAs) than other ethnic groups in the U. S., but reasons for the disparities are unknown. We performed gene expression profiling of sporadic CRCs from AAs vs. European Americans (EAs) to assess the contribution to CRC disparities. We evaluated the gene expression of 43 AA and 43 EA CRC tumors matched by stage and 40 matching normal colorectal tissues using the Agilent human whole genome 4x44K cDNA arrays. Gene and pathway analyses were performed using Significance Analysis of Microarrays (SAM), Ten-fold cross validation, and Ingenuity Pathway Analysis (IPA). SAM revealed that 95 genes were differentially expressed between AA and EA patients at a false discovery rate of ≤5%. Using IPA we determined that most prominent disease and pathway associations of differentially expressed genes were related to inflammation and immune response. Ten-fold cross validation demonstrated that following 10 genes can predict ethnicity with an accuracy of 94%: CRYBB2, PSPH, ADAL, VSIG10L, C17orf81, ANKRD36B, ZNF835, ARHGAP6, TRNT1 and WDR8. Expression of these 10 genes was validated by qRT-PCR in an independent test set of 28 patients (10 AA, 18 EA). Our results are the first to implicate differential gene expression in CRC racial disparities and indicate prominent difference in CRC inflammation between AA and EA patients. Differences in susceptibility to inflammation support the existence of distinct tumor microenvironments in these two patient populations. PMID:22276153
Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.
Factors associated with vocal fold pathologies in teachers.

PubMed

Souza, Carla Lima de; Carvalho, Fernando Martins; Araújo, Tânia Maria de; Reis, Eduardo José Farias Borges Dos; Lima, Verônica Maria Cadena; Porto, Lauro Antonio

2011-10-01

To analyze factors associated with the prevalence of the medical diagnosis of vocal fold pathologies in teachers. A census-based epidemiological, cross-sectional study was conducted with 4,495 public primary and secondary school teachers in the city of Salvador, Northeastern Brazil, between March and April 2006. The dependent variable was the self-reported medical diagnosis of vocal fold pathologies and the independent variables were sociodemographic characteristics; professional activity; work organization/interpersonal relationships; physical work environment characteristics; frequency of common mental disorders, measured by the Self-Reporting Questionnaire-20 (SRQ-20 >7); and general health conditions. Descriptive statistical, bivariate and multiple logistic regression analysis techniques were used. The prevalence of self-reported medical diagnosis of vocal fold pathologies was 18.9%. In the logistic regression analysis, the variables that remained associated with this medical diagnosis were as follows: being female, having worked as a teacher for more than seven years, excessive voice use, reporting more than five unfavorable physical work environment characteristics and presence of common mental disorders. The presence of self-reported vocal fold pathologies was associated with factors that point out the need of actions that promote teachers' vocal health and changes in their work structure and organization.
Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

PubMed Central

Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

2013-01-01

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568
Using support vector machine to predict beta- and gamma-turns in proteins.

PubMed

Hu, Xiuzhen; Li, Qianzhong

2008-09-01

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.
Short communication: Variations in major mineral contents of Mediterranean buffalo milk and application of Fourier-transform infrared spectroscopy for their prediction.

PubMed

Stocco, G; Cipolat-Gotet, C; Bonfatti, V; Schiavon, S; Bittante, G; Cecchinato, A

2016-11-01

The aims of this study were (1) to assess variability in the major mineral components of buffalo milk, (2) to estimate the effect of certain environmental sources of variation on the major minerals during lactation, and (3) to investigate the possibility of using Fourier-transform infrared (FTIR) spectroscopy as an indirect, noninvasive tool for routine prediction of the mineral content of buffalo milk. A total of 173 buffaloes reared in 5 herds were sampled once during the morning milking. Milk samples were analyzed for Ca, P, K, and Mg contents within 3h of sample collection using inductively coupled plasma optical emission spectrometry. A Milkoscan FT2 (Foss, Hillerød, Denmark) was used to acquire milk spectra over the spectral range from 5,000 to 900 wavenumber/cm. Prediction models were built using a partial least square approach, and cross-validation was used to assess the prediction accuracy of FTIR. Prediction models were validated using a 4-fold random cross-validation, thus dividing the calibration-test set in 4 folds, using one of them to check the results (prediction models) and the remaining 3 to develop the calibration models. Buffalo milk minerals averaged 162, 117, 86, and 14.4mg/dL of milk for Ca, P, K, and Mg, respectively. Herd and days in milk were the most important sources of variation in the traits investigated. Parity slightly affected only Ca content. Coefficients of determination of cross-validation between the FTIR-predicted and the measured values were 0.71, 0.70, and 0.72 for Ca, Mg, and P, respectively, whereas prediction accuracy was lower for K (0.55). Our findings reveal FTIR to be an unsuitable tool when milk mineral content needs to be predicted with high accuracy. Predictions may play a role as indicator traits in selective breeding (if the additive genetic correlation between FTIR predictions and measures of milk minerals is high enough) or in monitoring the milk of buffalo populations for dairy industry purposes. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Exploring Mouse Protein Function via Multiple Approaches.

PubMed

Huang, Guohua; Chu, Chen; Huang, Tao; Kong, Xiangyin; Zhang, Yunhua; Zhang, Ning; Cai, Yu-Dong

2016-01-01

Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1st-order predictions in the leave-one-out and ten-fold cross-validations. For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in reality.
Exploring Mouse Protein Function via Multiple Approaches

PubMed Central

Huang, Tao; Kong, Xiangyin; Zhang, Yunhua; Zhang, Ning

2016-01-01

Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1st-order predictions in the leave-one-out and ten-fold cross-validations. For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in reality. PMID:27846315
{sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Can Quantify and Predict Esophageal Injury During Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niedzielski, Joshua S., E-mail: jsniedzielski@mdanderson.org; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas; Yang, Jinzhong

Purpose: We sought to investigate the ability of mid-treatment {sup 18}F-fluorodeoxyglucose positron emission tomography (PET) studies to objectively and spatially quantify esophageal injury in vivo from radiation therapy for non-small cell lung cancer. Methods and Materials: This retrospective study was approved by the local institutional review board, with written informed consent obtained before enrollment. We normalized {sup 18}F-fluorodeoxyglucose PET uptake to each patient's low-irradiated region (<5 Gy) of the esophagus, as a radiation response measure. Spatially localized metrics of normalized uptake (normalized standard uptake value [nSUV]) were derived for 79 patients undergoing concurrent chemoradiation therapy for non-small cell lung cancer. We usedmore » nSUV metrics to classify esophagitis grade at the time of the PET study, as well as maximum severity by treatment completion, according to National Cancer Institute Common Terminology Criteria for Adverse Events, using multivariate least absolute shrinkage and selection operator (LASSO) logistic regression and repeated 3-fold cross validation (training, validation, and test folds). This 3-fold cross-validation LASSO model procedure was used to predict toxicity progression from 43 asymptomatic patients during the PET study. Dose-volume metrics were also tested in both the multivariate classification and the symptom progression prediction analyses. Classification performance was quantified with the area under the curve (AUC) from receiver operating characteristic analysis on the test set from the 3-fold analyses. Results: Statistical analysis showed increasing nSUV is related to esophagitis severity. Axial-averaged maximum nSUV for 1 esophageal slice and esophageal length with at least 40% of axial-averaged nSUV both had AUCs of 0.85 for classifying grade 2 or higher esophagitis at the time of the PET study and AUCs of 0.91 and 0.92, respectively, for maximum grade 2 or higher by treatment completion. Symptom progression was predicted with an AUC of 0.75. Dose metrics performed poorly at classifying esophagitis (AUC of 0.52, grade 2 or higher mid treatment) or predicting symptom progression (AUC of 0.67). Conclusions: Normalized uptake can objectively, locally, and noninvasively quantify esophagitis during radiation therapy and predict eventual symptoms from asymptomatic patients. Normalized uptake may provide patient-specific dose-response information not discernible from dose.« less
(18)F-Fluorodeoxyglucose Positron Emission Tomography Can Quantify and Predict Esophageal Injury During Radiation Therapy.

PubMed

Niedzielski, Joshua S; Yang, Jinzhong; Liao, Zhongxing; Gomez, Daniel R; Stingo, Francesco; Mohan, Radhe; Martel, Mary K; Briere, Tina M; Court, Laurence E

2016-11-01

We sought to investigate the ability of mid-treatment (18)F-fluorodeoxyglucose positron emission tomography (PET) studies to objectively and spatially quantify esophageal injury in vivo from radiation therapy for non-small cell lung cancer. This retrospective study was approved by the local institutional review board, with written informed consent obtained before enrollment. We normalized (18)F-fluorodeoxyglucose PET uptake to each patient's low-irradiated region (<5 Gy) of the esophagus, as a radiation response measure. Spatially localized metrics of normalized uptake (normalized standard uptake value [nSUV]) were derived for 79 patients undergoing concurrent chemoradiation therapy for non-small cell lung cancer. We used nSUV metrics to classify esophagitis grade at the time of the PET study, as well as maximum severity by treatment completion, according to National Cancer Institute Common Terminology Criteria for Adverse Events, using multivariate least absolute shrinkage and selection operator (LASSO) logistic regression and repeated 3-fold cross validation (training, validation, and test folds). This 3-fold cross-validation LASSO model procedure was used to predict toxicity progression from 43 asymptomatic patients during the PET study. Dose-volume metrics were also tested in both the multivariate classification and the symptom progression prediction analyses. Classification performance was quantified with the area under the curve (AUC) from receiver operating characteristic analysis on the test set from the 3-fold analyses. Statistical analysis showed increasing nSUV is related to esophagitis severity. Axial-averaged maximum nSUV for 1 esophageal slice and esophageal length with at least 40% of axial-averaged nSUV both had AUCs of 0.85 for classifying grade 2 or higher esophagitis at the time of the PET study and AUCs of 0.91 and 0.92, respectively, for maximum grade 2 or higher by treatment completion. Symptom progression was predicted with an AUC of 0.75. Dose metrics performed poorly at classifying esophagitis (AUC of 0.52, grade 2 or higher mid treatment) or predicting symptom progression (AUC of 0.67). Normalized uptake can objectively, locally, and noninvasively quantify esophagitis during radiation therapy and predict eventual symptoms from asymptomatic patients. Normalized uptake may provide patient-specific dose-response information not discernible from dose. Copyright © 2016 Elsevier Inc. All rights reserved.
A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.
Assessing the Potential of Folded Globular Polyproteins As Hydrogel Building Blocks

PubMed Central

2016-01-01

The native states of proteins generally have stable well-defined folded structures endowing these biomolecules with specific functionality and molecular recognition abilities. Here we explore the potential of using folded globular polyproteins as building blocks for hydrogels. Photochemically cross-linked hydrogels were produced from polyproteins containing either five domains of I27 ((I27)5), protein L ((pL)5), or a 1:1 blend of these proteins. SAXS analysis showed that (I27)5 exists as a single rod-like structure, while (pL)5 shows signatures of self-aggregation in solution. SANS measurements showed that both polyprotein hydrogels have a similar nanoscopic structure, with protein L hydrogels being formed from smaller and more compact clusters. The polyprotein hydrogels showed small energy dissipation in a load/unload cycle, which significantly increased when the hydrogels were formed in the unfolded state. This study demonstrates the use of folded proteins as building blocks in hydrogels, and highlights the potential versatility that can be offered in tuning the mechanical, structural, and functional properties of polyproteins. PMID:28006103
KINKFOLD—an AutoLISP program for construction of geological cross-sections using borehole image data

NASA Astrophysics Data System (ADS)

Özkaya, Sait Ismail

2002-04-01

KINKFOLD is an AutoLISP program designed to construct geological cross-sections from borehole image or dip meter logs. The program uses the kink-fold method for cross-section construction. Beds are folded around hinge lines as angle bisectors so that bedding thickness remains unchanged. KINKFOLD may be used to model a wide variety of parallel fold structures, including overturned and faulted folds, and folds truncated by unconformities. The program accepts data from vertical or inclined boreholes. The KINKFOLD program cannot be used to model fault drag, growth folds, inversion structures or disharmonic folds where the bed thickness changes either because of deformation or deposition. Faulted structures and similar folds can be modelled by KINKFOLD by omitting dip measurements within fault drag zones and near axial planes of similar folds.
Addressing Participant Validity in a Small Internet Health Survey (The Restore Study): Protocol and Recommendations for Survey Response Validation

PubMed Central

Dewitt, James; Capistrant, Benjamin; Kohli, Nidhi; Mitteldorf, Darryl; Merengwa, Enyinnaya; West, William

2018-01-01

Background While deduplication and cross-validation protocols have been recommended for large Web-based studies, protocols for survey response validation of smaller studies have not been published. Objective This paper reports the challenges of survey validation inherent in a small Web-based health survey research. Methods The subject population was North American, gay and bisexual, prostate cancer survivors, who represent an under-researched, hidden, difficult-to-recruit, minority-within-a-minority population. In 2015-2016, advertising on a large Web-based cancer survivor support network, using email and social media, yielded 478 completed surveys. Results Our manual deduplication and cross-validation protocol identified 289 survey submissions (289/478, 60.4%) as likely spam, most stemming from advertising on social media. The basic components of this deduplication and validation protocol are detailed. An unexpected challenge encountered was invalid survey responses evolving across the study period. This necessitated the static detection protocol be augmented with a dynamic one. Conclusions Five recommendations for validation of Web-based samples, especially with smaller difficult-to-recruit populations, are detailed. PMID:29691203
[Resistance mechanisms and cross-resistance of phoxim-resistant Frankliniella occidentalis Pergande population].

PubMed

Wang, Sheng-Yin; Zhou, Xian-Hong; Zhang, An-Sheng; Li, Li-Li; Men, Xing-Yuan; Zhang, Si-Cong; Liu, Yong-Jie; Yu, Yi

2012-07-01

To understand the resistance risks of Frankliniella occidentalis Pergande against phoxim, this paper studied the resistance mechanisms of phoxim-resistant F. occidentalis population against phoxim and the cross-resistance of the population against other insecticides. The phoxim-resistant population had medium level cross-resistance to chlorpyrifos, lambda-cyhalothrin, and methomyl, low level cross-resistance to chlorfenapyr, imidacloprid, emamectin-benzoate, and spinosad, but no cross-resistance to acetamiprid and abamectin. The synergists piperonyl butoxide (PBO), s, s, s-tributyl phosphorotrithioate (DEF), and triphenyl phosphate (TPP) had significant synergism (P < 0.05) on the toxicity of phoxim to the resistant (XK), field (BJ), and susceptible (S) populations, while diethyl maleate (DEM) had no significant synergism to XK and S populations but had significant synergism to BJ population. As compared with S population, the XK and BJ populations had significantly increased activities of mixed-functional oxidases P450 (2.79-fold and 1.48-fold), b, (2.88-fold and 1.88-fold), O-demethylase (2.60-fold and 1.68-fold), and carboxylesterase (2.02-fold and 1.61-fold, respectively), and XK population had a significantly increased acetylcholine esterase activity (3.10-fold). Both XK and BJ population had an increased activity of glutathione S-transferases (1.11-fold and 1.20-fold, respectively), but the increment was not significant. The increased detoxification enzymes activities in F. occidentalis could play an important role in the resistance of the plant against phoxim.
77 FR 67668 - Folding Gift Boxes From China; Revised Scheduling of the Expedited Five-Year Review Concerning...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-13

... INTERNATIONAL TRADE COMMISSION [Investigation No. 731-TA-921 (Second Review)] Folding Gift Boxes... on Folding Gift Boxes From China AGENCY: United States International Trade Commission. ACTION: Notice... the second five-year review of the antidumping duty order on Folding Gift Boxes from China...
Pettiness: Conceptualization, measurement and cross-cultural differences.

PubMed

Ng, Reuben; Levy, Becca

2018-01-01

Although pettiness, defined as the tendency to get agitated over trivial matters, is a facet of neuroticism which has negative health implications, no measure exists. The goal of the current study was to develop, and validate a short pettiness scale. In Study 1 (N = 2136), Exploratory Factor Analysis distilled a one-factor model with five items. Convergent validity was established using the Big Five Inventory, DASS, Satisfaction with Life Scale, and Conner-Davidson Resilience Scale. As predicted, pettiness was positively associated with neuroticism, depression, anxiety and stress but negatively related to extraversion, agreeableness, conscientiousness, openness, life satisfaction and resilience. Also, as predicted, pettiness was not significantly related to physical functioning, or blind and constructive patriotism, indicating discriminant validity. Confirmatory Factor Analysis in Study 2 (N = 734) revealed a stable one-factor model of pettiness. In Study 3 (N = 532), the scale, which showed a similar factor structure in the USA and Singapore, also reflected predicted cross-cultural patterns: Pettiness was found to be significantly lower in the United States, a culture categorized as "looser" than in Singapore, a culture classified as "tighter" in terms of Gelfand and colleagues' framework of national tendencies to oppose social deviance. Results suggest that this brief 5-item tool is a reliable and valid measure of pettiness, and its use in health research is encouraged.
Use of Nanofibers to Strengthen Hydrogels of Silica, Other Oxides, and Aerogels

NASA Technical Reports Server (NTRS)

Meador, Mary Ann B.; Capadona, Lynn A.; Hurwitz, Frances; Vivod, Stephanie L.; Lake, Max

2010-01-01

Research has shown that including up to 5 percent w/w carbon nanofibers in a silica backbone of polymer crosslinked aerogels improves its strength, tripling compressive modulus and increasing tensile stress-at-break five-fold with no increase in density or decrease in porosity. In addition, the initial silica hydrogels, which are produced as a first step in manufacturing the aerogels, can be quite fragile and difficult to handle before cross-linking. The addition of the carbon nanofiber also improves the strength of the initial hydrogels before cross-linking, improving the manufacturing process. This can also be extended to other oxide aerogels, such as alumina or aluminosilicates, and other nanofiber types, such as silicon carbide.

Assessing the Validity of Self-Rated Health with the Short Physical Performance Battery: A Cross-Sectional Analysis of the International Mobility in Aging Study.

PubMed

Pérez-Zepeda, Mario U; Belanger, Emmanuelle; Zunzunegui, Maria-Victoria; Phillips, Susan; Ylli, Alban; Guralnik, Jack

2016-01-01

The aim of this study was to explore the validity of self-rated health across different populations of older adults, when compared to the Short Physical Performance Battery. Cross-sectional analysis of the International Mobility in Aging Study. Five locations: Saint-Hyacinthe and Kingston (Canada), Tirana (Albania), Manizales (Colombia), and Natal (Brazil). Older adults between 65 and 74 years old (n = 1,995). The Short Physical Performance Battery (SPPB) was used to measure physical performance. Self-rated health was assessed with one single five-point question. Linear trends between SPPB scores and self-rated health were tested separately for men and women at each of the five international study sites. Poor physical performance (independent variable) (SPPB less than 8) was used in logistic regression models of self-rated health (dependent variable), adjusting for potential covariates. All analyses were stratified by gender and site of origin. A significant linear association was found between the mean scores of the Short Physical Performance Battery and ordinal categories of self-rated health across research sites and gender groups. After extensive control for objective physical and mental health indicators and socio-demographic variables, these graded associations became non-significant in some research sites. These findings further confirm the validity of SRH as a measure of overall health status in older adults.
Subsurface structural interpretation by applying trishear algorithm: An example from the Lenghu5 fold-and-thrust belt, Qaidam Basin, Northern Tibetan Plateau

NASA Astrophysics Data System (ADS)

Pei, Yangwen; Paton, Douglas A.; Wu, Kongyou; Xie, Liujuan

2017-08-01

The application of trishear algorithm, in which deformation occurs in a triangle zone in front of a propagating fault tip, is often used to understand fault related folding. In comparison to kink-band methods, a key characteristic of trishear algorithm is that non-uniform deformation within the triangle zone allows the layer thickness and horizon length to change during deformation, which is commonly observed in natural structures. An example from the Lenghu5 fold-and-thrust belt (Qaidam Basin, Northern Tibetan Plateau) is interpreted to help understand how to employ trishear forward modelling to improve the accuracy of seismic interpretation. High resolution fieldwork data, including high-angle dips, 'dragging structures', thinning hanging-wall and thickening footwall, are used to determined best-fit trishear model to explain the deformation happened to the Lenghu5 fold-and-thrust belt. We also consider the factors that increase the complexity of trishear models, including: (a) fault-dip changes and (b) pre-existing faults. We integrate fault dip change and pre-existing faults to predict subsurface structures that are apparently under seismic resolution. The analogue analysis by trishear models indicates that the Lenghu5 fold-and-thrust belt is controlled by an upward-steepening reverse fault above a pre-existing opposite-thrusting fault in deeper subsurface. The validity of the trishear model is confirmed by the high accordance between the model and the high-resolution fieldwork. The validated trishear forward model provides geometric constraints to the faults and horizons in the seismic section, e.g., fault cutoffs and fault tip position, faults' intersecting relationship and horizon/fault cross-cutting relationship. The subsurface prediction using trishear algorithm can significantly increase the accuracy of seismic interpretation, particularly in seismic sections with low signal/noise ratio.
Cross-Cultural Validation of the Patient Perception of Integrated Care Survey.

PubMed

Tietschert, Maike V; Angeli, Federica; van Raak, Arno J A; Ruwaard, Dirk; Singer, Sara J

2017-07-20

To test the cross-cultural validity of the U.S. Patient Perception of Integrated Care (PPIC) Survey in a Dutch sample using a standardized procedure. Primary data collected from patients of five primary care centers in the south of the Netherlands, through survey research from 2014 to 2015. Cross-sectional data collected from patients who saw multiple health care providers during 6 months preceding data collection. The PPIC survey includes 59 questions that measure patient perceived care integration across providers, settings, and time. Data analysis followed a standardized procedure guiding data preparation, psychometric analysis, and included invariance testing with the U.S. dataset. Latent scale structures of the Dutch and U.S. survey were highly comparable. Factor "Integration with specialist" had lower reliability scores and noninvariance. For the remaining factors, internal consistency and invariance estimates were strong. The standardized cross-cultural validation procedure produced strong support for comparable psychometric characteristics of the Dutch and U.S. surveys. Future research should examine the usability of the proposed procedure for contexts with greater cultural differences. © Health Research and Educational Trust.
Recognizing emotional speech in Persian: a validated database of Persian emotional speech (Persian ESD).

PubMed

Keshtiari, Niloofar; Kuhlmann, Michael; Eslami, Moharram; Klann-Delius, Gisela

2015-03-01

Research on emotional speech often requires valid stimuli for assessing perceived emotion through prosody and lexical content. To date, no comprehensive emotional speech database for Persian is officially available. The present article reports the process of designing, compiling, and evaluating a comprehensive emotional speech database for colloquial Persian. The database contains a set of 90 validated novel Persian sentences classified in five basic emotional categories (anger, disgust, fear, happiness, and sadness), as well as a neutral category. These sentences were validated in two experiments by a group of 1,126 native Persian speakers. The sentences were articulated by two native Persian speakers (one male, one female) in three conditions: (1) congruent (emotional lexical content articulated in a congruent emotional voice), (2) incongruent (neutral sentences articulated in an emotional voice), and (3) baseline (all emotional and neutral sentences articulated in neutral voice). The speech materials comprise about 470 sentences. The validity of the database was evaluated by a group of 34 native speakers in a perception test. Utterances recognized better than five times chance performance (71.4 %) were regarded as valid portrayals of the target emotions. Acoustic analysis of the valid emotional utterances revealed differences in pitch, intensity, and duration, attributes that may help listeners to correctly classify the intended emotion. The database is designed to be used as a reliable material source (for both text and speech) in future cross-cultural or cross-linguistic studies of emotional speech, and it is available for academic research purposes free of charge. To access the database, please contact the first author.
Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.
Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide
Literature classification for semi-automated updating of biological knowledgebases

PubMed Central

2013-01-01

Background As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. PMID:24564403
Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Dvořák, P.; Kropatsch, W. G.; Bartušek, K.

2013-10-01

This work focuses on fully automatic detection of brain tumors. The first aim is to determine, whether the image contains a brain with a tumor, and if it does, localize it. The goal of this work is not the exact segmentation of tumors, but the localization of their approximate position. The test database contains 203 T2-weighted images of which 131 are images of healthy brain and the remaining 72 images contain brain with pathological area. The estimation, whether the image shows an afflicted brain and where a pathological area is, is done by multi resolution symmetry analysis. The first goal was tested by five-fold cross-validation technique with 100 repetitions to avoid the result dependency on sample order. This part of the proposed method reaches the true positive rate of 87.52% and the true negative rate of 93.14% for an afflicted brain detection. The evaluation of the second part of the algorithm was carried out by comparing the estimated location to the true tumor location. The detection of the tumor location reaches the rate of 95.83% of correct anomaly detection and the rate 87.5% of correct tumor location.
e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

PubMed Central

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-01-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist. PMID:29651416
Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.
Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.
Deep learning for brain tumor classification

NASA Astrophysics Data System (ADS)

Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

2017-03-01

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.
Sorting protein decoys by machine-learning-to-rank

PubMed Central

Jing, Xiaoyang; Wang, Kai; Lu, Ruqian; Dong, Qiwen

2016-01-01

Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, and these methods could be roughly divided into three categories: the single-model methods, clustering-based methods and quasi single-model methods. In this study, we develop a single-model method MQAPRank based on the learning-to-rank algorithm firstly, and then implement a quasi single-model method Quasi-MQAPRank. The proposed methods are benchmarked on the 3DRobot and CASP11 dataset. The five-fold cross-validation on the 3DRobot dataset shows the proposed single model method outperforms other methods whose outputs are taken as features of the proposed method, and the quasi single-model method can further enhance the performance. On the CASP11 dataset, the proposed methods also perform well compared with other leading methods in corresponding categories. In particular, the Quasi-MQAPRank method achieves a considerable performance on the CASP11 Best150 dataset. PMID:27530967
e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-learning Methods

NASA Astrophysics Data System (ADS)

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-03-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.
Sorting protein decoys by machine-learning-to-rank.

PubMed

Jing, Xiaoyang; Wang, Kai; Lu, Ruqian; Dong, Qiwen

2016-08-17

Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, and these methods could be roughly divided into three categories: the single-model methods, clustering-based methods and quasi single-model methods. In this study, we develop a single-model method MQAPRank based on the learning-to-rank algorithm firstly, and then implement a quasi single-model method Quasi-MQAPRank. The proposed methods are benchmarked on the 3DRobot and CASP11 dataset. The five-fold cross-validation on the 3DRobot dataset shows the proposed single model method outperforms other methods whose outputs are taken as features of the proposed method, and the quasi single-model method can further enhance the performance. On the CASP11 dataset, the proposed methods also perform well compared with other leading methods in corresponding categories. In particular, the Quasi-MQAPRank method achieves a considerable performance on the CASP11 Best150 dataset.
Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides.

PubMed

Panwar, Bharat; Raghava, Gajendra P S

2015-04-01

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/). Copyright © 2015 Elsevier Inc. All rights reserved.
Inter-rater and intra-rater reliability of the Bahasa Melayu version of Rose Angina Questionnaire.

PubMed

Hassan, N B; Choudhury, S R; Naing, L; Conroy, R M; Rahman, A R A

2007-01-01

The objective of the study is to translate the Rose Questionnaire (RQ) into a Bahasa Melayu version and adapt it cross-culturally, and to measure its inter-rater and intrarater reliability. This cross sectional study was conducted in the respondents' homes or workplaces in Kelantan, Malaysia. One hundred respondents aged 30 and above with different socio-demographic status were interviewed for face validity. For each inter-rater and intra-rater reliability, a sample of 150 respondents was interviewed. Inter-rater and intra-rater reliabilities were assessed by Cohen's kappa. The overall inter-rater agreements by the five pair of interviewers at point one and two were 0.86, and intrarater reliability by the five interviewers on the seven-item questionnaire at poinone and two was 0.88, as measured by kappa coefficient. The translated Malay version of RQ demonstrated an almost perfect inter-rater and intra-rater reliability and further validation such as sensitivity and specificity analysis of this translated questionnaire is highly recommended.
Application of proteomics in the discovery of candidate protein biomarkers in a Diabetes Autoantibody Standardization Program (DASP) sample subset

PubMed Central

Metz, Thomas O.; Qian, Wei-Jun; Jacobs, Jon M.; Gritsenko, Marina A.; Moore, Ronald J.; Polpitiya, Ashoka D.; Monroe, Matthew E.; Camp, David G.; Mueller, Patricia W.; Smith, Richard D.

2009-01-01

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted “-omics” approaches are under-utilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. Alpha-2-glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples. PMID:18092746
Application of proteomics in the discovery of candidate protein biomarkers in a diabetes autoantibody standardization program sample subset.

PubMed

Metz, Thomas O; Qian, Wei-Jun; Jacobs, Jon M; Gritsenko, Marina A; Moore, Ronald J; Polpitiya, Ashoka D; Monroe, Matthew E; Camp, David G; Mueller, Patricia W; Smith, Richard D

2008-02-01

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted "-omics" approaches are underutilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program, with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. alpha-2-Glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples.
Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

PubMed

Zhang, Shu-Bo; Tang, Qiang-Rong

2016-07-21

Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

PubMed

Zwanenburg, Alex; Andriessen, Peter; Jellema, Reint K; Niemarkt, Hendrik J; Wolfs, Tim G A M; Kramer, Boris W; Delhaas, Tammo

2015-03-01

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

PubMed

Zhang, Li; Ai, Haixin; Chen, Wen; Yin, Zimo; Hu, Huan; Zhu, Junfeng; Zhao, Jian; Zhao, Qi; Liu, Hongsheng

2017-05-18

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).
Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition.

PubMed

Zhao, Xiaowei; Ning, Qiao; Ai, Meiyue; Chai, Haiting; Yang, Guifu

2016-06-07

As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/. Copyright © 2016 Elsevier Ltd. All rights reserved.
Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

PubMed

Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

2014-09-07

Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.
Can multi-subpopulation reference sets improve the genomic predictive ability for pigs?

PubMed

Fangmann, A; Bergfelder-Drüing, S; Tholen, E; Simianer, H; Erbe, M

2015-12-01

In most countries and for most livestock species, genomic evaluations are obtained from within-breed analyses. To achieve reliable breeding values, however, a sufficient reference sample size is essential. To increase this size, the use of multibreed reference populations for small populations is considered a suitable option in other species. Over decades, the separate breeding work of different pig breeding organizations in Germany has led to stratified subpopulations in the breed German Large White. Due to this fact and the limited number of Large White animals available in each organization, there was a pressing need for ascertaining if multi-subpopulation genomic prediction is superior compared with within-subpopulation prediction in pigs. Direct genomic breeding values were estimated with genomic BLUP for the trait "number of piglets born alive" using genotype data (Illumina Porcine 60K SNP BeadChip) from 2,053 German Large White animals from five different commercial pig breeding companies. To assess the prediction accuracy of within- and multi-subpopulation reference sets, a random 5-fold cross-validation with 20 replications was performed. The five subpopulations considered were only slightly differentiated from each other. However, the prediction accuracy of the multi-subpopulations approach was not better than that of the within-subpopulation evaluation, for which the predictive ability was already high. Reference sets composed of closely related multi-subpopulation sets performed better than sets of distantly related subpopulations but not better than the within-subpopulation approach. Despite the low differentiation of the five subpopulations, the genetic connectedness between these different subpopulations seems to be too small to improve the prediction accuracy by applying multi-subpopulation reference sets. Consequently, resources should be used for enlarging the reference population within subpopulation, for example, by adding genotyped females.
Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.
Generative Topographic Mapping of Conformational Space.

PubMed

Horvath, Dragos; Baskin, Igor; Marcou, Gilles; Varnek, Alexandre

2017-10-01

Herein, Generative Topographic Mapping (GTM) was challenged to produce planar projections of the high-dimensional conformational space of complex molecules (the 1LE1 peptide). GTM is a probability-based mapping strategy, and its capacity to support property prediction models serves to objectively assess map quality (in terms of regression statistics). The properties to predict were total, non-bonded and contact energies, surface area and fingerprint darkness. Map building and selection was controlled by a previously introduced evolutionary strategy allowed to choose the best-suited conformational descriptors, options including classical terms and novel atom-centric autocorrellograms. The latter condensate interatomic distance patterns into descriptors of rather low dimensionality, yet precise enough to differentiate between close favorable contacts and atom clashes. A subset of 20 K conformers of the 1LE1 peptide, randomly selected from a pool of 2 M geometries (generated by the S4MPLE tool) was employed for map building and cross-validation of property regression models. The GTM build-up challenge reached robust three-fold cross-validated determination coefficients of Q 2 =0.7…0.8, for all modeled properties. Mapping of the full 2 M conformer set produced intuitive and information-rich property landscapes. Functional and folding subspaces appear as well-separated zones, even though RMSD with respect to the PDB structure was never used as a selection criterion of the maps. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

PubMed

Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

2016-10-01

Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.
The Vocal Cord Dysfunction Questionnaire: Validity and Reliability of the Persian Version.

PubMed

Ghaemi, Hamide; Khoddami, Seyyedeh Maryam; Soleymani, Zahra; Zandieh, Fariborz; Jalaie, Shohreh; Ahanchian, Hamid; Khadivi, Ehsan

2017-12-25

The aim of this study was to develop, validate, and assess the reliability of the Persian version of Vocal Cord Dysfunction Questionnaire (VCDQ P ). The study design was cross-sectional or cultural survey. Forty-four patients with vocal fold dysfunction (VFD) and 40 healthy volunteers were recruited for the study. To assess the content validity, the prefinal questions were given to 15 experts to comment on its essential. Ten patients with VFD rated the importance of VCDQ P in detecting face validity. Eighteen of the patients with VFD completed the VCDQ 1 week later for test-retest reliability. To detect absolute reliability, standard error of measurement and smallest detected change were calculated. Concurrent validity was assessed by completing the Persian Chronic Obstructive Pulmonary Disease (COPD) Assessment Test (CAT) by 34 patients with VFD. Discriminant validity was measured from 34 participants. The VCDQ was further validated by administering the questionnaire to 40 healthy volunteers. Validation of the VCDQ as a treatment outcome tool was conducted in 18 patients with VFD using pre- and posttreatment scores. The internal consistency was confirmed (Cronbach α = 0.78). The test-retest reliability was excellent (intraclass correlation coefficient = 0.97). The standard error of measurement and smallest detected change values were acceptable (0.39 and 1.08, respectively). There was a significant correlation between the VCDQ P and the CAT total scores (P < 0.05). Discriminative validity was significantly different. The VCDQ scores in patients with VFD before and after treatment was significantly different (P < 0.001). The VCDQ was cross-culturally adapted to Persian and demonstrated to be a valid and reliable self-administered questionnaire in Persian-speaking population. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Elevated expression of esterase and cytochrome P450 are related with lambda-cyhalothrin resistance and lead to cross resistance in Aphis glycines Matsumura.

PubMed

Xi, Jinghui; Pan, Yiou; Bi, Rui; Gao, Xiwu; Chen, Xuewei; Peng, Tianfei; Zhang, Min; Zhang, Hua; Hu, Xiaoyue; Shang, Qingli

2015-02-01

A resistant strain of the Aphis glycines Matsumura (CRR) has developed 76.67-fold resistance to lambda-cyhalothrin compared with the susceptible (CSS) strain. Synergists piperonyl butoxide (PBO), S,S,S-Tributyltrithiophosphate (DEF) and triphenyl phosphate (TPP) dramatically increased the toxicity of lambda-cyhalothrin to the resistant strain. Bioassay results indicated that the CRR strain had developed high levels of cross-resistance to chlorpyrifos (11.66-fold), acephate (8.20-fold), cypermethrin (53.24-fold), esfenvalerate (13.83-fold), cyfluthrin (9.64-fold), carbofuran (14.60-fold), methomyl (9.32-fold) and bifenthrin (4.81-fold), but did not have cross-resistance to chlorfenapyr, imidacloprid, diafenthiuron, abamectin. The transcriptional levels of CYP6A2-like, CYP6A14-like and cytochrome b-c1 complex subunit 9-like increased significantly in the resistant strain than that in the susceptible. Similar trend were observed in the transcripts and DNA copy number of CarE and E4 esterase. Overall, these results demonstrate that increased esterase hydrolysis activity, combined with elevated cytochrome P450 monooxygenase detoxicatication, plays an important role in the high levels of lambda-cyhalothrin resistance and can cause cross-resistance to other insecticides in the CRR strain. Copyright © 2014 Elsevier Inc. All rights reserved.
One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor

PubMed Central

Riaz, Qaiser; Vögele, Anna; Krüger, Björn; Weber, Andreas

2015-01-01

A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person. PMID:26703601
Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Richard E; Zhang, Hao; Parker, Lynne Edwards

2013-01-01

Kernel methods have difficulties scaling to large modern data sets. The scalability issues are based on computational and memory requirements for working with a large matrix. These requirements have been addressed over the years by using low-rank kernel approximations or by improving the solvers scalability. However, Least Squares Support VectorMachines (LS-SVM), a popular SVM variant, and Kernel Ridge Regression still have several scalability issues. In particular, the O(n^3) computational complexity for solving a single model, and the overall computational complexity associated with tuning hyperparameters are still major problems. We address these problems by introducing an O(n log n) approximate l-foldmore » cross-validation method that uses a multi-level circulant matrix to approximate the kernel. In addition, we prove our algorithm s computational complexity and present empirical runtimes on data sets with approximately 1 million data points. We also validate our approximate method s effectiveness at selecting hyperparameters on real world and standard benchmark data sets. Lastly, we provide experimental results on using a multi-level circulant kernel approximation to solve LS-SVM problems with hyperparameters selected using our method.« less
The Irrational Beliefs Inventory: psychometric properties and cross-cultural validation of its Arabic version.

PubMed

Al-Heeti, Khalaf N M; Hamid, Abdalla A R M; Alghorani, Mohammad A

2012-08-01

The purpose of this study was to examine the psychometric properties of the adapted Irrational Beliefs Inventory (IBI-34) and thus begin the process of assessing its adequacy for use in an Arab culture. The scale was translated and then administered to two samples of undergraduate students from the United Arab Emirates University. Data from 384 students were used in the main analysis, and data from 251 students were used for cross-validation. Principal components analysis (PCA) with varimax rotation followed by PCA with oblimin rotation yielded the same five components in both the main sample and the validation sample, thus consistent with the original Dutch study. Only 34 of the original 50 items were adequate to represent the five constructs. Cronbach's alpha coefficient for the overall scale was .76 and for the subscales ranged between .71 and .76, except for the Rigidity subscale, which was .54. The adapted IBI-34 correlated significantly and negatively with the General Health Questionnaire and Beck Depression Inventory, providing support for concurrent validity. Due to the non-significant differences between male and female participants on the total score of the IBI-34, the scale can be used for both sexes by summing across all items to give a total score that can be used as a general indicator of the irrational thinking.
Assessing the Validity of Self-Rated Health with the Short Physical Performance Battery: A Cross-Sectional Analysis of the International Mobility in Aging Study

PubMed Central

Belanger, Emmanuelle; Zunzunegui, Maria–Victoria; Phillips, Susan; Ylli, Alban; Guralnik, Jack

2016-01-01

Objective The aim of this study was to explore the validity of self-rated health across different populations of older adults, when compared to the Short Physical Performance Battery. Design Cross-sectional analysis of the International Mobility in Aging Study. Setting Five locations: Saint-Hyacinthe and Kingston (Canada), Tirana (Albania), Manizales (Colombia), and Natal (Brazil). Participants Older adults between 65 and 74 years old (n = 1,995). Methods The Short Physical Performance Battery (SPPB) was used to measure physical performance. Self-rated health was assessed with one single five-point question. Linear trends between SPPB scores and self-rated health were tested separately for men and women at each of the five international study sites. Poor physical performance (independent variable) (SPPB less than 8) was used in logistic regression models of self-rated health (dependent variable), adjusting for potential covariates. All analyses were stratified by gender and site of origin. Results A significant linear association was found between the mean scores of the Short Physical Performance Battery and ordinal categories of self-rated health across research sites and gender groups. After extensive control for objective physical and mental health indicators and socio-demographic variables, these graded associations became non-significant in some research sites. Conclusion These findings further confirm the validity of SRH as a measure of overall health status in older adults. PMID:27089219
In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

PubMed

Chiddarwar, Rucha K; Rohrer, Sebastian G; Wolf, Antje; Tresch, Stefan; Wollenhaupt, Sabrina; Bender, Andreas

2017-01-01

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals. Copyright © 2016 Elsevier Inc. All rights reserved.
WISC-R Types of Learning Disabilities: A Profile Analysis with Cross-Validation.

ERIC Educational Resources Information Center

Holcomb, William R.; And Others

1987-01-01

Profiles (Wechsler Intelligence Scale for Children - Revised) of 119 children in five learning disability programs were placed in six homogeneous groups using cluster analysis. One group showed superior intelligence quotient (IQ) with motor coordination deficits and severe emotional problems, while three groups represented children with low IQs…
Calculation of Five Thermodynamic Molecular Descriptors by Means of a General Computer Algorithm Based on the Group-Additivity Method: Standard Enthalpies of Vaporization, Sublimation and Solvation, and Entropy of Fusion of Ordinary Organic Molecules and Total Phase-Change Entropy of Liquid Crystals.

PubMed

Naef, Rudolf; Acree, William E

2017-06-25

The calculation of the standard enthalpies of vaporization, sublimation and solvation of organic molecules is presented using a common computer algorithm on the basis of a group-additivity method. The same algorithm is also shown to enable the calculation of their entropy of fusion as well as the total phase-change entropy of liquid crystals. The present method is based on the complete breakdown of the molecules into their constituting atoms and their immediate neighbourhood; the respective calculations of the contribution of the atomic groups by means of the Gauss-Seidel fitting method is based on experimental data collected from literature. The feasibility of the calculations for each of the mentioned descriptors was verified by means of a 10-fold cross-validation procedure proving the good to high quality of the predicted values for the three mentioned enthalpies and for the entropy of fusion, whereas the predictive quality for the total phase-change entropy of liquid crystals was poor. The goodness of fit ( Q ²) and the standard deviation (σ) of the cross-validation calculations for the five descriptors was as follows: 0.9641 and 4.56 kJ/mol ( N = 3386 test molecules) for the enthalpy of vaporization, 0.8657 and 11.39 kJ/mol ( N = 1791) for the enthalpy of sublimation, 0.9546 and 4.34 kJ/mol ( N = 373) for the enthalpy of solvation, 0.8727 and 17.93 J/mol/K ( N = 2637) for the entropy of fusion and 0.5804 and 32.79 J/mol/K ( N = 2643) for the total phase-change entropy of liquid crystals. The large discrepancy between the results of the two closely related entropies is discussed in detail. Molecules for which both the standard enthalpies of vaporization and sublimation were calculable, enabled the estimation of their standard enthalpy of fusion by simple subtraction of the former from the latter enthalpy. For 990 of them the experimental enthalpy-of-fusion values are also known, allowing their comparison with predictions, yielding a correlation coefficient R ² of 0.6066.
The association of eight potentially functional polymorphisms in five adrenergic receptor-encoding genes with myocardial infarction risk in Han Chinese.

PubMed

Xia, Kun; Ding, Rongjing; Zhang, Zhiyong; Li, Weiming; Shang, Xiaoming; Yang, Xinchun; Wang, Lefeng; Zhang, Qi

2017-08-15

Adrenergic receptors play a key role in activating the sympathetic nervous system, which often accompanies with the development of myocardial infarction (MI). Here, we aimed to test the association of eight potentially functional polymorphisms in five adrenergic receptor-encoding genes with MI risk. Genotypes were available for 717 MI patients and 612 controls. There were no detectable deviations from the Hardy-Weinberg equilibrium for all study polymorphisms. Allele frequencies differed remarkably for ADRA2B D/I (P<0.001), ADRB1 Ser49Gly (P=0.002), ADRB2 Gln27Glu (P=0.005), and ADRB3 Trp64Arg (P<0.001) polymorphisms, even after the Bonferroni correction. Systolic blood pressure was significantly lower in ADRA2B II genotype carriers than in the DD genotype carriers (P=0.006), while plasma high-density lipoprotein cholesterol was significantly higher in patients carrying ADRA2B I allele and ADRB1 49Ser allele than in patients with the DD genotype and 49Gly/49Gly genotype, respectively (P=0.018 and 0.033). Overall best interaction model consisted of ADRA2B D/I, ADRB1 Ser49Gly, dyslipidemia and hypertension, with the highest testing accuracy of 0.627 and the maximal 10-fold cross-validation consistency (P=0.017). Finally, a nomogram was depicted based on four significant polymorphisms and metabolic risk factors, and it had a better predictive utility and was internally validated with a discrimination C-index of 0.723 (P<0.001). Altogether, we identified two polymorphisms, ADRA2B D/I and ADRB1 Ser49Arg, which not only altered genetic susceptibility to MI, but also impacted on blood pressure and plasma lipid changes, and their combination with metabolic risk factors constituted the overall best interaction model. Copyright © 2017. Published by Elsevier B.V.
Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

PubMed

Zhang, Yi; Li, Peiyang; Zhu, Xuyang; Su, Steven W; Guo, Qing; Xu, Peng; Yao, Dezhong

2017-01-01

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.
GRMDA: Graph Regression for MiRNA-Disease Association Prediction

PubMed Central

Chen, Xing; Yang, Jing-Ru; Guan, Na-Na; Li, Jian-Qiang

2018-01-01

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction. PMID:29515453

GRMDA: Graph Regression for MiRNA-Disease Association Prediction.

PubMed

Chen, Xing; Yang, Jing-Ru; Guan, Na-Na; Li, Jian-Qiang

2018-01-01

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.
An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.
A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases.

PubMed

Chen, Xing; Huang, Yu-An; You, Zhu-Hong; Yan, Gui-Ying; Wang, Xue-Song

2017-03-01

Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now. In this work, we constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe-Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe-disease association prediction. The reliable prediction performance could be attributed to the use of KATZ measurement, and the introduction of Gaussian interaction profile kernel similarity for microbes and diseases. LOOCV and k-fold cross validation were implemented to evaluate the effectiveness of this novel computational model based on known microbe-disease associations obtained from HMDAD database. As a result, KATZHMDA achieved reliable performance with average AUCs of 0.8130 ± 0.0054, 0.8301 ± 0.0033 and 0.8382 in 2-fold and 5-fold cross validation and LOOCV framework, respectively. It is anticipated that KATZHMDA could be used to obtain more novel microbes associated with important noninfectious human diseases and therefore benefit drug discovery and human medical improvement. Matlab codes and dataset explored in this work are available at http://dwz.cn/4oX5mS . xingchen@amss.ac.cn or zhuhongyou@gmail.com or wangxuesongcumt@163.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Translation, Validation, and Adaptation of the Time Use Diary from English into the Malay Language for Use in Malaysia.

PubMed

Asmuri, Siti Noraini; Brown, Ted; Broom, Lisa J

2016-07-01

Valid translations of time use scales are needed by occupational therapists for use in different cross-cultural contexts to gather relevant data to inform practice and research. The purpose of this study was to describe the process of translating, adapting, and validating the Time Use Diary from its current English language edition into a Malay language version. Five steps of the cross-cultural adaptation process were completed: (i) translation from English into the Malay language by a qualified translator, (ii) synthesis of the translated Malay version, (iii) backtranslation from Malay to English by three bilingual speakers, (iv) expert committee review and discussion, and (v) pilot testing of the Malay language version with two participant groups. The translated version was found to be a reliable and valid tool identifying changes and potential challenges in the time use of older adults. This provides Malaysian occupational therapists with a useful tool for gathering time use data in practice settings and for research purposes.
A Decision Tree for Nonmetric Sex Assessment from the Skull.

PubMed

Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

2018-01-01

This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.
Cross-cultural equivalence of the patient- and parent-reported quality of life in short stature youth (QoLISSY) questionnaire.

PubMed

Bullinger, Monika; Quitmann, Julia; Silva, Neuza; Rohenkohl, Anja; Chaplin, John E; DeBusk, Kendra; Mimoun, Emmanuelle; Feigerlova, Eva; Herdman, Michael; Sanz, Dolores; Wollmann, Hartmut; Pleil, Andreas; Power, Michael

2014-01-01

Testing cross-cultural equivalence of patient-reported outcomes requires sufficiently large samples per country, which is difficult to achieve in rare endocrine paediatric conditions. We describe a novel approach to cross-cultural testing of the Quality of Life in Short Stature Youth (QoLISSY) questionnaire in five countries by sequentially taking one country out (TOCO) from the total sample and iteratively comparing the resulting psychometric performance. Development of the QoLISSY proceeded from focus group discussions through pilot testing to field testing in 268 short-statured patients and their parents. To explore cross-cultural equivalence, the iterative TOCO technique was used to examine and compare the validity, reliability, and convergence of patient and parent responses on QoLISSY in the field test dataset, and to predict QoLISSY scores from clinical, socio-demographic and psychosocial variables. Validity and reliability indicators were satisfactory for each sample after iteratively omitting one country. Comparisons with the total sample revealed cross-cultural equivalence in internal consistency and construct validity for patients and parents, high inter-rater agreement and a substantial proportion of QoLISSY variance explained by predictors. The TOCO technique is a powerful method to overcome problems of country-specific testing of patient-reported outcome instruments. It provides an empirical support to QoLISSY's cross-cultural equivalence and is recommended for future research.
The use of a gas chromatography-sensor system combined with advanced statistical methods, towards the diagnosis of urological malignancies

PubMed Central

Aggio, Raphael B. M.; de Lacy Costello, Ben; White, Paul; Khalid, Tanzeela; Ratcliffe, Norman M.; Persad, Raj; Probert, Chris S. J.

2016-01-01

Prostate cancer is one of the most common cancers. Serum prostate-specific antigen (PSA) is used to aid the selection of men undergoing biopsies. Its use remains controversial. We propose a GC-sensor algorithm system for classifying urine samples from patients with urological symptoms. This pilot study includes 155 men presenting to urology clinics, 58 were diagnosed with prostate cancer, 24 with bladder cancer and 73 with haematuria and or poor stream, without cancer. Principal component analysis (PCA) was applied to assess the discrimination achieved, while linear discriminant analysis (LDA) and support vector machine (SVM) were used as statistical models for sample classification. Leave-one-out cross-validation (LOOCV), repeated 10-fold cross-validation (10FoldCV), repeated double cross-validation (DoubleCV) and Monte Carlo permutations were applied to assess performance. Significant separation was found between prostate cancer and control samples, bladder cancer and controls and between bladder and prostate cancer samples. For prostate cancer diagnosis, the GC/SVM system classified samples with 95% sensitivity and 96% specificity after LOOCV. For bladder cancer diagnosis, the SVM reported 96% sensitivity and 100% specificity after LOOCV, while the DoubleCV reported 87% sensitivity and 99% specificity, with SVM showing 78% and 98% sensitivity between prostate and bladder cancer samples. Evaluation of the results of the Monte Carlo permutation of class labels obtained chance-like accuracy values around 50% suggesting the observed results for bladder cancer and prostate cancer detection are not due to over fitting. The results of the pilot study presented here indicate that the GC system is able to successfully identify patterns that allow classification of urine samples from patients with urological cancers. An accurate diagnosis based on urine samples would reduce the number of negative prostate biopsies performed, and the frequency of surveillance cystoscopy for bladder cancer patients. Larger cohort studies are planned to investigate the potential of this system. Future work may lead to non-invasive breath analyses for diagnosing urological conditions. PMID:26865331
Multiplexed quantification of nucleic acids with large dynamic range using multivolume digital RT-PCR on a rotational SlipChip tested with HIV and hepatitis C viral load.

PubMed

Shen, Feng; Sun, Bing; Kreutz, Jason E; Davydova, Elena K; Du, Wenbin; Reddy, Poluru L; Joseph, Loren J; Ismagilov, Rustem F

2011-11-09

In this paper, we are working toward a problem of great importance to global health: determination of viral HIV and hepatitis C (HCV) loads under point-of-care and resource limited settings. While antiretroviral treatments are becoming widely available, viral load must be evaluated at regular intervals to prevent the spread of drug resistance and requires a quantitative measurement of RNA concentration over a wide dynamic range (from 50 up to 10(6) molecules/mL for HIV and up to 10(8) molecules/mL for HCV). "Digital" single molecule measurements are attractive for quantification, but the dynamic range of such systems is typically limited or requires excessive numbers of compartments. Here we designed and tested two microfluidic rotational SlipChips to perform multivolume digital RT-PCR (MV digital RT-PCR) experiments with large and tunable dynamic range. These designs were characterized using synthetic control RNA and validated with HIV viral RNA and HCV control viral RNA. The first design contained 160 wells of each of four volumes (125 nL, 25 nL, 5 nL, and 1 nL) to achieve a dynamic range of 5.2 × 10(2) to 4.0 × 10(6) molecules/mL at 3-fold resolution. The second design tested the flexibility of this approach, and further expanded it to allow for multiplexing while maintaining a large dynamic range by adding additional wells with volumes of 0.2 nL and 625 nL and dividing the SlipChip into five regions to analyze five samples each at a dynamic range of 1.8 × 10(3) to 1.2 × 10(7) molecules/mL at 3-fold resolution. No evidence of cross-contamination was observed. The multiplexed SlipChip can be used to analyze a single sample at a dynamic range of 1.7 × 10(2) to 2.0 × 10(7) molecules/mL at 3-fold resolution with limit of detection of 40 molecules/mL. HIV viral RNA purified from clinical samples were tested on the SlipChip, and viral load results were self-consistent and in good agreement with results determined using the Roche COBAS AmpliPrep/COBAS TaqMan HIV-1 Test. With further validation, this SlipChip should become useful to precisely quantify viral HIV and HCV RNA for high-performance diagnostics in resource-limited settings. These microfluidic designs should also be valuable for other diagnostic and research applications, including detecting rare cells and rare mutations, prenatal diagnostics, monitoring residual disease, and quantifying copy number variation and gene expression patterns. The theory for the design and analysis of multivolume digital PCR experiments is presented in other work by Kreutz et al.
Development of the Brazilian Portuguese version of the Achilles Tendon Total Rupture Score (ATRS BrP): a cross-cultural adaptation with reliability and construct validity evaluation.

PubMed

Zambelli, Roberto; Pinto, Rafael Z; Magalhães, João Murilo Brandão; Lopes, Fernando Araujo Silva; Castilho, Rodrigo Simões; Baumfeld, Daniel; Dos Santos, Thiago Ribeiro Teles; Maffulli, Nicola

2016-01-01

There is a need for a patient-relevant instrument to evaluate outcome after treatment in patients with a total Achilles tendon rupture. The purpose of this study was to undertake a cross-cultural adaptation of the Achilles Tendon Total Rupture Score (ATRS) into Brazilian Portuguese, determining the test-retest reliability and construct validity of the instrument. A five-step approach was used in the cross-cultural adaptation process: initial translation (two bilingual Brazilian translators), synthesis of translation, back-translation (two native English language translators), consensus version and evaluation (expert committee), and testing phase. A total of 46 patients were recruited to evaluate the test-retest reproducibility and construct validity of the Brazilian Portuguese version of the ATRS. Test-retest reproducibility was performed by assessing each participant on two separate occasions. The construct validity was determined by the correlation index between the ATRS and the Orthopedic American Foot and Ankle Society (AOFAS) questionnaires. The final version of the Brazilian Portuguese ATRS had the same number of questions as the original ATRS. For the reliability analysis, an ICC(2,1) of 0.93 (95 % CI: 0.88 to 0.96) with SEM of 1.56 points and MDC of 4.32 was observed, indicating excellent reliability. The construct validity showed excellent correlation with R = 0.76 (95 % CI: 0.52 to 0.89, P < 0.001). The ATRS was successfully cross-culturally validated into Brazilian Portuguese. This version was a reliable and valid measure of function in patients who suffered complete rupture of the Achilles Tendon.
Development and validation of a 6-point grading scale in patients undergoing correction of nasolabial folds with a collagen implant.

PubMed

Monheit, Gary D; Gendler, Ellen C; Poff, Bradley; Fleming, Laura; Bachtell, Nathan; Garcia, Emily; Burkholder, David

2010-11-01

Various scoring techniques prone to subjective interpretation have been used to evaluate soft tissue augmentation of nasolabial folds (NLFs). To design and validate a reliable wrinkle assessment scoring scale. Six photographed wrinkles of varying severity were electronically copied onto the same facial image to become a 6-point grading scale (GGS). A pilot training program (13 investigators) determined reliability, and a 12-week multicenter survey study validated the GGS scoring method. Pilot study inter- and intrarater scoring reliability were high (weighted kappa scores of 0.85 and 0.86, respectively). Seventy-five percent of survey investigators and independent review panel (IRP) members considered a GGS score difference of 0.5 to be a minimally perceivable difference. Interrater weighted kappa scores were 0.91 for the IRP and 0.80 for investigators. Intrarater agreements after repeat testing were 0.91 and 0.89, respectively. The baseline "live" assessment GGS mean score was 3.34, and the baseline blinded photographic assessment GGS mean score was 2.00 for the IRP and 2.16 for the investigators. The GGS is a reproducible method of grading the severity of NLF wrinkles. Treatment effectiveness of a dermal filler can be reliably evaluated using the GGS by comparing "live" assessments with the standard GGS photographic panel. © 2010 by the American Society for Dermatologic Surgery, Inc.
An Exploratory Study of Pre-Admission Predictors of Hardiness and Retention for United States Military Academy Cadets Using Regression Modeling

DTIC Science & Technology

2013-06-01

Character in Sports Index CV Cross Validation FAS Faculty Appraisal Score FFM Five-Factor Model, also known as the “Big Five” GAM... FFM ). USMA does not allow personality testing as a selection tool. However, perhaps we may discover whether pre-admission information can predict...characteristic, and personality factors as described by the Five Factor Model ( FFM ) to determine their effect on one’s academic performance at USMA (Clark
Clarifying and Measuring Filial Concepts across Five Cultural Groups

PubMed Central

Jones, Patricia S.; Lee, Jerry W.; Zhang, Xinwei E.

2011-01-01

Literature on responsibility of adult children for aging parents reflects lack of conceptual clarity. We examined filial concepts across five cultural groups: African-, Asian-, Euro-, Latino-, and Native Americans. Data were randomly divided for scale development (n = 285) and cross-validation (n = 284). Exploratory factor analysis on 59 items identified three filial concepts: Responsibility, Respect, and Care. Confirmatory factor analysis on a 12-item final scale showed data fit the three-factor model better than the single factor solution despite substantial correlations between the factors (.82, .82 for Care with Responsibility and Respect, and .74 for Responsibility with Respect). The scale can be used in cross-cultural research to test hypotheses that predict associations among filial values, filial caregiving, and caregiver health outcomes. PMID:21618557
A Spanish-language patient safety questionnaire to measure medical and nursing students' attitudes and knowledge.

PubMed

Mira, José J; Navarro, Isabel M; Guilabert, Mercedes; Poblete, Rodrigo; Franco, Astolfo L; Jiménez, Pilar; Aquino, Margarita; Fernández-Trujillo, Francisco J; Lorenzo, Susana; Vitaller, Julián; de Valle, Yohana Díaz; Aibar, Carlos; Aranaz, Jesús M; De Pedro, José A

2015-08-01

To design and validate a questionnaire for assessing attitudes and knowledge about patient safety using a sample of medical and nursing students undergoing clinical training in Spain and four countries in Latin America. In this cross-sectional study, a literature review was carried out and total of 786 medical and nursing students were surveyed at eight universities from five countries (Chile, Colombia, El Salvador, Guatemala, and Spain) to develop and refine a Spanish-language questionnaire on knowledge and attitudes about patient safety. The scope of the questionnaire was based on five dimensions (factors) presented in studies related to patient safety culture found in PubMed and Scopus. Based on the five factors, 25 reactive items were developed. Composite reliability indexes and Cronbach's alpha statistics were estimated for each factor, and confirmatory factor analysis was conducted to assess validity. After a pilot test, the questionnaire was refined using confirmatory models, maximum-likelihood estimation, and the variance-covariance matrix (as input). Multiple linear regression models were used to confirm external validity, considering variables related to patient safety culture as dependent variables and the five factors as independent variables. The final instrument was a structured five-point Likert self-administered survey (the "Latino Student Patient Safety Questionnaire") consisting of 21 items grouped into five factors. Compound reliability indexes (Cronbach's alpha statistic) calculated for the five factors were about 0.7 or higher. The results of the multiple linear regression analyses indicated good model fit (goodness-of-fit index: 0.9). Item-total correlations were higher than 0.3 in all cases. The convergent-discriminant validity was adequate. The questionnaire designed and validated in this study assesses nursing and medical students' attitudes and knowledge about patient safety. This instrument could be used to indirectly evaluate whether or not students in health disciplines are acquiring and thus likely to put into practice the professional skills currently considered most appropriate for patient safety.
Cross-cultural adaptation of the Individual Work Performance Questionnaire.

PubMed

Koopmans, Linda; Bernaards, Claire M; Hildebrandt, Vincent H; Lerner, Debra; de Vet, Henrica C W; van der Beek, Allard J

2015-01-01

The Individual Work Performance Questionnaire (IWPQ), measuring task performance, contextual performance, and counterproductive work behavior, was developed in The Netherlands. To cross-culturally adapt the IWPQ from the Dutch to the American-English language, and assess the questionnaire's internal consistency and content validity in the American-English context. A five stage translation and adaptation process was used: forward translation, synthesis, back-translation, expert committee review, and pilot-testing. During the pilot-testing, cognitive interviews with 40 American workers were performed, to examine the comprehensibility, applicability, and completeness of the American-English IWPQ. Questionnaire instructions were slightly modified to aid interpretation in the American-English language. Inconsistencies with verb tense were identified, and it was decided to consistently use simple past tense. The wording of five items was modified to better suit the American-English language. In general, participants were positive on the comprehensibility, applicability and completeness of the questionnaire during the pilot-testing phase. Furthermore, the study showed positive results concerning the internal consistency (Cronbach's alphas for the scales between 0.79-0.89) and content validity of the American-English IWPQ. The results indicate that the cross-cultural adaptation of the American-English IWPQ was successful and that the measurement properties of the translated version are promising.
77 FR 42762 - Scheduling of an Expedited Five-Year Review Concerning the Antidumping Duty Order on Folding Gift...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-20

... INTERNATIONAL TRADE COMMISSION [Investigation No. 731-TA-921 (Second Review)] Scheduling of an Expedited Five-Year Review Concerning the Antidumping Duty Order on Folding Gift Boxes From China AGENCY... folding gift boxes from China would be likely to lead to continuation or recurrence of material injury...
Design and 4D Printing of Cross-Folded Origami Structures: A Preliminary Investigation.

PubMed

Teoh, Joanne Ee Mei; An, Jia; Feng, Xiaofan; Zhao, Yue; Chua, Chee Kai; Liu, Yong

2018-03-03

In 4D printing research, different types of complex structure folding and unfolding have been investigated. However, research on cross-folding of origami structures (defined as a folding structure with at least two overlapping folds) has not been reported. This research focuses on the investigation of cross-folding structures using multi-material components along different axes and different horizontal hinge thickness with single homogeneous material. Tensile tests were conducted to determine the impact of multi-material components and horizontal hinge thickness. In the case of multi-material structures, the hybrid material composition has a significant impact on the overall maximum strain and Young's modulus properties. In the case of single material structures, the shape recovery speed is inversely proportional to the horizontal hinge thickness, while the flexural or bending strength is proportional to the horizontal hinge thickness. A hinge with a thickness of 0.5 mm could be folded three times prior to fracture whilst a hinge with a thickness of 0.3 mm could be folded only once prior to fracture. A hinge with a thickness of 0.1 mm could not even be folded without cracking. The introduction of a physical hole in the center of the folding/unfolding line provided stress relief and prevented fracture. A complex flower petal shape was used to successfully demonstrate the implementation of overlapping and non-overlapping folding lines using both single material segments and multi-material segments. Design guidelines for establishing cross-folding structures using multi-material components along different axes and different horizontal hinge thicknesses with single or homogeneous material were established. These guidelines can be used to design and implement complex origami structures with overlapping and non-overlapping folding lines. Combined overlapping folding structures could be implemented and allocating specific hole locations in the overall designs could be further explored. In addition, creating a more precise prediction by investigating sets of in between hinge thicknesses and comparing the folding times before fracture, will be the subject of future work.
Comparative pharmacokinetics of rhein in normal and loperamide-induced constipated rats and microarray analysis of drug-metabolizing genes.

PubMed

Hou, Mei-Ling; Chang, Li-Wen; Lin, Chi-Hung; Lin, Lie-Chwen; Tsai, Tung-Hu

2014-09-11

Rhein is a pharmacological active component found in Rheum palmatum L. that is the major herb of the San-Huang-Xie-Xin-Tang (SHXXT), a medicinal herbal product used as a remedy for constipation. Here we have investigated the comparative pharmacokinetics of rhein in normal and constipated rats. Microarray analysis was used to explore whether drug-metabolizing genes will be altered after SHXXT treatment. The comparative pharmacokinetics of rhein in normal and loperamide-induced constipated rats was studied by liquid chromatography with electrospray ionization tandem mass spectrometry (LC-MS/MS). Gene expression profiling in drug-metabolizing genes after SHXXT treatment was investigated by microarray analysis and real-time polymerase chain reaction (RT-PCR). A validated LC-MS/MS method was applied to investigate the comparative pharmacokinetics of rhein in normal and loperamide-induced constipated rats. The pharmacokinetic results demonstrate that the loperamide-induced constipation reduced the absorption of rhein. Cmax significantly reduced by 2.5-fold, the AUC decreased by 27.8%; however, the elimination half-life (t1/2) was prolonged by 1.6-fold. Tmax and mean residence time (MRT) were significantly prolonged by 2.8-fold, and 1.7-fold, respectively. The volume of distribution (Vss) increased by 2.2-fold. The data of microarray analysis on gene expression indicate that five drug-metabolizing genes, including Cyp7a1, Cyp2c6, Ces2e, Atp1b1, and Slc7a2 were significantly altered by the SHXXT (0.5 g/kg) treatment. The loperamide-induced constipation reduced the absorption of rhein. Since among the 25,338 genes analyzed, there were five genes significantly altered by SHXXT treatment. Thus, information on minor drug-metabolizing genes altered by SHXXT treatment indicates that SHXXT is relatively safe for clinical application. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM.

PubMed

Gu, Bin; Sheng, Victor S; Tay, Keng Yeow; Romano, Walter; Li, Shuo

2017-06-01

Model selection plays an important role in cost-sensitive SVM (CS-SVM). It has been proven that the global minimum cross validation (CV) error can be efficiently computed based on the solution path for one parameter learning problems. However, it is a challenge to obtain the global minimum CV error for CS-SVM based on one-dimensional solution path and traditional grid search, because CS-SVM is with two regularization parameters. In this paper, we propose a solution and error surfaces based CV approach (CV-SES). More specifically, we first compute a two-dimensional solution surface for CS-SVM based on a bi-parameter space partition algorithm, which can fit solutions of CS-SVM for all values of both regularization parameters. Then, we compute a two-dimensional validation error surface for each CV fold, which can fit validation errors of CS-SVM for all values of both regularization parameters. Finally, we obtain the CV error surface by superposing K validation error surfaces, which can find the global minimum CV error of CS-SVM. Experiments are conducted on seven datasets for cost sensitive learning and on four datasets for imbalanced learning. Experimental results not only show that our proposed CV-SES has a better generalization ability than CS-SVM with various hybrids between grid search and solution path methods, and than recent proposed cost-sensitive hinge loss SVM with three-dimensional grid search, but also show that CV-SES uses less running time.
Mechanisms of Increased Resistance to Chlorhexidine and Cross-Resistance to Colistin following Exposure of Klebsiella pneumoniae Clinical Isolates to Chlorhexidine

PubMed Central

Bock, Lucy J.; Bonney, Laura C.

2016-01-01

ABSTRACT Klebsiella pneumoniae is an opportunistic pathogen that is often difficult to treat due to its multidrug resistance (MDR). We have previously shown that K. pneumoniae strains are able to “adapt” (become more resistant) to the widely used bisbiguanide antiseptic chlorhexidine. Here, we investigated the mechanisms responsible for and the phenotypic consequences of chlorhexidine adaptation, with particular reference to antibiotic cross-resistance. In five of six strains, adaptation to chlorhexidine also led to resistance to the last-resort antibiotic colistin. Here, we show that chlorhexidine adaptation is associated with mutations in the two-component regulator phoPQ and a putative Tet repressor gene (smvR) adjacent to the major facilitator superfamily (MFS) efflux pump gene, smvA. Upregulation of smvA (10- to 27-fold) was confirmed in smvR mutant strains, and this effect and the associated phenotype were suppressed when a wild-type copy of smvR was introduced on plasmid pACYC. Upregulation of phoPQ (5- to 15-fold) and phoPQ-regulated genes, pmrD (6- to 19-fold) and pmrK (18- to 64-fold), was confirmed in phoPQ mutant strains. In contrast, adaptation of K. pneumoniae to colistin did not result in increased chlorhexidine resistance despite the presence of mutations in phoQ and elevated phoPQ, pmrD, and pmrK transcript levels. Insertion of a plasmid containing phoPQ from chlorhexidine-adapted strains into wild-type K. pneumoniae resulted in elevated expression levels of phoPQ, pmrD, and pmrK and increased resistance to colistin, but not chlorhexidine. The potential risk of colistin resistance emerging in K. pneumoniae as a consequence of exposure to chlorhexidine has important clinical implications for infection prevention procedures. PMID:27799211
Cross Validity of the Behavior Style Questionnaire and Child Personality Scale in Nursery School Children.

ERIC Educational Resources Information Center

Simonds, John F.; Simonds, M. Patricia

1982-01-01

Mothers of 182 nursery school children completed the Behavior Style Questionnaire (BSQ) and the Child Personality Scale (CPS). Intercorrelational analyses showed many significantly correlated items. Scores of the five CPS factors clearly distinguished between subjects in easy and difficult BSQ clusters. Found boys significantly more introverted…

The Development and Testing of a Tool for Analysis of Computer-Mediated Conferencing Transcripts.

ERIC Educational Resources Information Center

Fahy, Patrick J.; Crawford, Gail; Ally, Mohamed; Cookson, Peter; Keller, Verna; Prosser, Frank

2000-01-01

The Zhu model for analyzing computer mediated communications was further developed by an Athabasca University (Alberta) distance education research team based on ease of use, reliability, validity, theoretical support, and cross-discipline utility. Five classification categories of the new model are vertical questioning, horizontal questioning,…
The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Heyse, E-mail: heyse.li@mail.utoronto.ca; Becker, Nathan; Raman, Srinivas

2015-08-15

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derivedmore » from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman’s rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew’s correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse.« less
Microarray analysis of gene expression patterns of high lycopene tomato generated from seeds after long-term space flight

NASA Astrophysics Data System (ADS)

Lu, Jinying; Ren, Chunxiao; Pan, Yi; Nechitailo, Galina S.; Liu, Min

Lycopene content is a most vital trait of tomatoes due to the role of lycopene in reducing the risk of some kinds of cancers. In this experiment, we gained a high lycopene (hl) tomato (named HY-2), after seven generations of self-cross selection, from seeds Russian MNP-1 carried in Russia MIR space station for six years. HPLC result showed that the lycopene content was 1.6 times more than that in Russian MNP-1 (the wild type). Microarray analysis presented the general profile of differential expressed genes at the tomato developmental stage of 7DPB (days post breaker). One hundred and forty three differential expression genes were identified according to the following criterion: the average changes were no less than 1.5 folds with q-value (similar to FDR) less than 0.05 or changes were no less than 1.5 folds in all three biological replications. Most of the differential expressed genes were mainly involved in metabolism, response to stimulus, biosynthesis, development and regulation. Particularly, we discussed the genes involved in protein metabolism, response to unfolded protein, carotenoid biosynthesis and photosynthesis that might be related to the fruit development and the accumulation of lycopene. What's more, we conducted QRT-PCR validation of five key genes (Fps, CrtL-b, CrtR-b, Zep and Nxs) in the lycopene biosynthesis pathway through time courses and that provided the direct molecular evidence for the hl phenotype. Our results demonstrate that long-term space flight, as a rarely used tool, can positively cause some beneficial mutations in the seeds and thus to help to generate a high quality variety, combined with ground selections.
Cross-Cultural Adaptation and Validation of the Italian Version of SWAL-QOL.

PubMed

Ginocchio, Daniela; Alfonsi, Enrico; Mozzanica, Francesco; Accornero, Anna Rosa; Bergonzoni, Antonella; Chiarello, Giulia; De Luca, Nicoletta; Farneti, Daniele; Marilia, Simonelli; Calcagno, Paola; Turroni, Valentina; Schindler, Antonio

2016-10-01

The aim of the study was to evaluate the reliability and validity of the Italian SWAL-QOL (I-SWAL-QOL). The study consisted of five phases: item generation, reliability analysis, normative data generation, validity analysis, and responsiveness analysis. The item generation phase followed the five-step, cross-cultural, adaptation process of translation and back-translation. A group of 92 dysphagic patients was enrolled for the internal consistency analysis. Seventy-eight patients completed the I-SWAL-QOL twice, 2 weeks apart, for test-retest reliability analysis. A group of 200 asymptomatic subjects completed the I-SWAL-QOL for normative data generation. I-SWAL-QOL scores obtained by both the group of dysphagic subjects and asymptomatic ones were compared for validity analysis. I-SWAL-QOL scores were correlated with SF-36 scores in 67 patients with dysphagia for concurrent validity analysis. Finally, I-SWAL-QOL scores obtained in a group of 30 dysphagic patients before and after successful rehabilitation treatment were compared for responsiveness analysis. All the enrolled patients managed to complete the I-SWAL-QOL without needing any assistance, within 20 min. Internal consistency was acceptable for all I-SWAL-QOL subscales (α > 0.70). Test-retest reliability was also satisfactory for all subscales (ICC > 0.7). A significant difference between the dysphagic group and the control group was found in all I-SWAL-QOL subscales (p < 0.05). Mild to moderate correlations between I-SWAL-QOL and SF-36 subscales were observed. I-SWAL-QOL scores obtained in the pre-treatment condition were significantly lower than those obtained after swallowing rehabilitation. I-SWAL-QOL is reliable, valid, responsive to changes in QOL, and recommended for clinical practice and outcome research.
NoFold: RNA structure clustering without folding or alignment.

PubMed

Middleton, Sarah A; Kim, Junhyong

2014-11-01

Structures that recur across multiple different transcripts, called structure motifs, often perform a similar function-for example, recruiting a specific RNA-binding protein that then regulates translation, splicing, or subcellular localization. Identifying common motifs between coregulated transcripts may therefore yield significant insight into their binding partners and mechanism of regulation. However, as most methods for clustering structures are based on folding individual sequences or doing many pairwise alignments, this results in a tradeoff between speed and accuracy that can be problematic for large-scale data sets. Here we describe a novel method for comparing and characterizing RNA secondary structures that does not require folding or pairwise alignment of the input sequences. Our method uses the idea of constructing a distance function between two objects by their respective distances to a collection of empirical examples or models, which in our case consists of 1973 Rfam family covariance models. Using this as a basis for measuring structural similarity, we developed a clustering pipeline called NoFold to automatically identify and annotate structure motifs within large sequence data sets. We demonstrate that NoFold can simultaneously identify multiple structure motifs with an average sensitivity of 0.80 and precision of 0.98 and generally exceeds the performance of existing methods. We also perform a cross-validation analysis of the entire set of Rfam families, achieving an average sensitivity of 0.57. We apply NoFold to identify motifs enriched in dendritically localized transcripts and report 213 enriched motifs, including both known and novel structures. © 2014 Middleton and Kim; Published by Cold Spring Harbor Laboratory Press for the RNA Society.
Addressing Participant Validity in a Small Internet Health Survey (The Restore Study): Protocol and Recommendations for Survey Response Validation.

PubMed

Dewitt, James; Capistrant, Benjamin; Kohli, Nidhi; Rosser, B R Simon; Mitteldorf, Darryl; Merengwa, Enyinnaya; West, William

2018-04-24

While deduplication and cross-validation protocols have been recommended for large Web-based studies, protocols for survey response validation of smaller studies have not been published. This paper reports the challenges of survey validation inherent in a small Web-based health survey research. The subject population was North American, gay and bisexual, prostate cancer survivors, who represent an under-researched, hidden, difficult-to-recruit, minority-within-a-minority population. In 2015-2016, advertising on a large Web-based cancer survivor support network, using email and social media, yielded 478 completed surveys. Our manual deduplication and cross-validation protocol identified 289 survey submissions (289/478, 60.4%) as likely spam, most stemming from advertising on social media. The basic components of this deduplication and validation protocol are detailed. An unexpected challenge encountered was invalid survey responses evolving across the study period. This necessitated the static detection protocol be augmented with a dynamic one. Five recommendations for validation of Web-based samples, especially with smaller difficult-to-recruit populations, are detailed. ©James Dewitt, Benjamin Capistrant, Nidhi Kohli, B R Simon Rosser, Darryl Mitteldorf, Enyinnaya Merengwa, William West. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 24.04.2018.
Cross-cultural adaptation and validation of the Protective Nursing Advocacy Scale for Brazilian nurses 1

PubMed Central

Tomaschewski-Barlem, Jamila Geri; Lunardi, Valéria Lerch; Barlem, Edison Luiz Devos; da Silveira, Rosemary Silva; Dalmolin, Graziele de Lima; Ramos, Aline Marcelino

2015-01-01

Abstract Objective: to adapt culturally and validate the Protective Nursing Advocacy Scale for Brazilian nurses. Method: methodological study carried out with 153 nurses from two hospitals in the South region of Brazil, one public and the other philanthropic. The cross-cultural adaptation of the Protective Nursing Advocacy Scale was performed according to international standards, and its validation was carried out for use in the Brazilian context, by means of factor analysis and Cronbach's alpha as measure of internal consistency. Results: by means of evaluation by a committee of experts and application of pre-test, face validity and content validity of the instrument were considered satisfactory. From the factor analysis, five constructs were identified: negative implications of the advocacy practice, advocacy actions, facilitators of the advocacy practice, perceptions that favor practice advocacy and barriers to advocacy practice. The instrument showed satisfactory internal consistency, with Cronbach's alpha values ranging from 0.70 to 0.87. Conclusion: it was concluded that the Protective Nursing Advocacy Scale - Brazilian version, is a valid and reliable instrument for use in the evaluation of beliefs and actions of health advocacy, performed by Brazilian nurses in their professional practice environment. PMID:26444169
Inattention in primary school is not good for your future school achievement—A pattern classification study

PubMed Central

Bøe, Tormod; Lundervold, Arvid

2017-01-01

Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers’ reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7–9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers’ reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure. PMID:29182663
Inattention in primary school is not good for your future school achievement-A pattern classification study.

PubMed

Lundervold, Astri J; Bøe, Tormod; Lundervold, Arvid

2017-01-01

Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers' reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7-9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers' reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure.
Rigid Origami via Optical Programming and Deferred Self-Folding of a Two-Stage Photopolymer.

PubMed

Glugla, David J; Alim, Marvin D; Byars, Keaton D; Nair, Devatha P; Bowman, Christopher N; Maute, Kurt K; McLeod, Robert R

2016-11-02

We demonstrate the formation of shape-programmed, glassy origami structures using a single-layer photopolymer with two mechanically distinct phases. The latent origami pattern consisting of rigid, high cross-link density panels and flexible, low cross-link density creases is fabricated using a series of photomask exposures. Strong optical absorption of the polymer formulation creates depth-wise gradients in the cross-link density of the creases, enforcing directed folding which enables programming of both mountain and valley folds within the same sheet. These multiple photomask patterns can be sequentially applied because the sheet remains flat until immersed into a photopolymerizable monomer solution that differentially swells the polymer to fold and form the origami structure. After folding, a uniform photoexposure polymerizes the absorbed solution, permanently fixing the shape of the folded structure while simultaneously increasing the modulus of the folds. This approach creates sharp folds by mimicking the stiff panels and flexible creases of paper origami while overcoming the traditional trade-off of self-actuated materials that require low modulus for folding and high modulus for mechanical robustness. Using this process, we demonstrate a waterbomb base capable of supporting 1500 times its own weight.
The Pain Self-Efficacy Questionnaire: Cross-Cultural Adaptation into Italian and Assessment of Its Measurement Properties.

PubMed

Chiarotto, Alessandro; Vanti, Carla; Ostelo, Raymond W; Ferrari, Silvano; Tedesco, Giuseppe; Rocca, Barbara; Pillastrini, Paolo; Monticone, Marco

2015-11-01

The Pain Self-Efficacy Questionnaire (PSEQ) is a patient self-reported measurement instrument that evaluates pain self-efficacy beliefs in patients with chronic pain. The measurement properties of the PSEQ have been tested in its original and translated versions, showing satisfactory results for validity and reliability. The aims of this study were 2 fold as follows: (1) to translate the PSEQ into Italian through a process of cross-cultural adaptation, (2) to test the measurement properties of the Italian PSEQ (PSEQ-I). The cross-cultural adaptation was completed in 5 months without omitting any item of the original PSEQ. Measurement properties were tested in 165 patients with chronic low back pain (CLBP) (65% women, mean age 49.9 years). Factor analysis confirmed the one-factor structure of the questionnaire. Internal consistency (Cronbach's α = 0.94) and test-retest reliability (ICCagreement = 0.82) of the PSEQ-I showed good results. The smallest detectable change was equal to 15.69 scale points. The PSEQ-I displayed a high construct validity by meeting more than 75% of a priori hypotheses on correlations with measurement instruments assessing pain intensity, disability, anxiety, depression, pain catastrophizing, fear of movement, and coping strategies. Additionally, the PSEQ-I differentiated patients taking pain medication or not. The results of this study suggest that the PSEQ-I can be used as a valid and reliable tool in Italian patients with CLBP. © 2014 World Institute of Pain.
Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset

PubMed Central

Lipps, David; Devineni, Sree

2016-01-01

MiRNAs are short non-coding RNAs of about 22 nucleotides, which play critical roles in gene expression regulation. The biogenesis of miRNAs is largely determined by the sequence and structural features of their parental RNA molecules. Based on these features, multiple computational tools have been developed to predict if RNA transcripts contain miRNAs or not. Although being very successful, these predictors started to face multiple challenges in recent years. Many predictors were optimized using datasets of hundreds of miRNA samples. The sizes of these datasets are much smaller than the number of known miRNAs. Consequently, the prediction accuracy of these predictors in large dataset becomes unknown and needs to be re-tested. In addition, many predictors were optimized for either high sensitivity or high specificity. These optimization strategies may bring in serious limitations in applications. Moreover, to meet continuously raised expectations on these computational tools, improving the prediction accuracy becomes extremely important. In this study, a meta-predictor mirMeta was developed by integrating a set of non-linear transformations with meta-strategy. More specifically, the outputs of five individual predictors were first preprocessed using non-linear transformations, and then fed into an artificial neural network to make the meta-prediction. The prediction accuracy of meta-predictor was validated using both multi-fold cross-validation and independent dataset. The final accuracy of meta-predictor in newly-designed large dataset is improved by 7% to 93%. The meta-predictor is also proved to be less dependent on datasets, as well as has refined balance between sensitivity and specificity. This study has two folds of importance: First, it shows that the combination of non-linear transformations and artificial neural networks improves the prediction accuracy of individual predictors. Second, a new miRNA predictor with significantly improved prediction accuracy is developed for the community for identifying novel miRNAs and the complete set of miRNAs. Source code is available at: https://github.com/xueLab/mirMeta PMID:28002428
Sub-classification of Advanced-Stage Hepatocellular Carcinoma: A Cohort Study Including 612 Patients Treated with Sorafenib.

PubMed

Yoo, Jeong-Ju; Chung, Goh Eun; Lee, Jeong-Hoon; Nam, Joon Yeul; Chang, Young; Lee, Jeong Min; Lee, Dong Ho; Kim, Hwi Young; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-04-01

Advanced hepatocellular carcinoma (HCC) is associated with various clinical conditions including major vessel invasion, metastasis, and poor performance status. The aim of this study was to establish a prognostic scoring system and to propose a sub-classification of the Barcelona-Clinic Liver Cancer (BCLC) stage C. This retrospective study included consecutive patientswho received sorafenib for BCLC stage C HCC at a single tertiary hospital in Korea. A Cox proportional hazard model was used to develop a scoring system, and internal validationwas performed by a 5-fold cross-validation. The performance of the model in predicting risk was assessed by the area under the curve and the Hosmer-Lemeshow test. A total of 612 BCLC stage C HCC patients were sub- classified into strata depending on their performance status. Five independent prognostic factors (Child-Pugh score, α-fetoprotein, tumor type, extrahepatic metastasis, and portal vein invasion) were identified and used in the prognostic scoring system. This scoring system showed good discrimination (area under the receiver operating characteristic curve, 0.734 to 0.818) and calibration functions (both p < 0.05 by the Hosmer-Lemeshow test at 1 month and 12 months, respectively). The differences in survival among the different risk groups classified by the total score were significant (p < 0.001 by the log-rank test in both the Eastern Cooperative Oncology Group 0 and 1 strata). The heterogeneity of patientswith BCLC stage C HCC requires sub-classification of advanced HCC. A prognostic scoring system with five independent factors is useful in predicting the survival of patients with BCLC stage C HCC.
GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping

PubMed Central

Diaz-Garcia, Luis; Covarrubias-Pazaran, Giovanny; Schlautman, Brandon; Zalapa, Juan

2016-01-01

Traditional methods for trait phenotyping have been a bottleneck for research in many crop species due to their intensive labor, high cost, complex implementation, lack of reproducibility and propensity to subjective bias. Recently, multiple high-throughput phenotyping platforms have been developed, but most of them are expensive, species-dependent, complex to use, and available only for major crops. To overcome such limitations, we present the open-source software GiNA, which is a simple and free tool for measuring horticultural traits such as shape- and color-related parameters of fruits, vegetables, and seeds. GiNA is multiplatform software available in both R and MATLAB® programming languages and uses conventional images from digital cameras with minimal requirements. It can process up to 11 different horticultural morphological traits such as length, width, two-dimensional area, volume, projected skin, surface area, RGB color, among other parameters. Different validation tests produced highly consistent results under different lighting conditions and camera setups making GiNA a very reliable platform for high-throughput phenotyping. In addition, five-fold cross validation between manually generated and GiNA measurements for length and width in cranberry fruits were 0.97 and 0.92. In addition, the same strategy yielded prediction accuracies above 0.83 for color estimates produced from images of cranberries analyzed with GiNA compared to total anthocyanin content (TAcy) of the same fruits measured with the standard methodology of the industry. Our platform provides a scalable, easy-to-use and affordable tool for massive acquisition of phenotypic data of fruits, seeds, and vegetables. PMID:27529547
GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping.

PubMed

Diaz-Garcia, Luis; Covarrubias-Pazaran, Giovanny; Schlautman, Brandon; Zalapa, Juan

2016-01-01

Traditional methods for trait phenotyping have been a bottleneck for research in many crop species due to their intensive labor, high cost, complex implementation, lack of reproducibility and propensity to subjective bias. Recently, multiple high-throughput phenotyping platforms have been developed, but most of them are expensive, species-dependent, complex to use, and available only for major crops. To overcome such limitations, we present the open-source software GiNA, which is a simple and free tool for measuring horticultural traits such as shape- and color-related parameters of fruits, vegetables, and seeds. GiNA is multiplatform software available in both R and MATLAB® programming languages and uses conventional images from digital cameras with minimal requirements. It can process up to 11 different horticultural morphological traits such as length, width, two-dimensional area, volume, projected skin, surface area, RGB color, among other parameters. Different validation tests produced highly consistent results under different lighting conditions and camera setups making GiNA a very reliable platform for high-throughput phenotyping. In addition, five-fold cross validation between manually generated and GiNA measurements for length and width in cranberry fruits were 0.97 and 0.92. In addition, the same strategy yielded prediction accuracies above 0.83 for color estimates produced from images of cranberries analyzed with GiNA compared to total anthocyanin content (TAcy) of the same fruits measured with the standard methodology of the industry. Our platform provides a scalable, easy-to-use and affordable tool for massive acquisition of phenotypic data of fruits, seeds, and vegetables.
Folding free energy surfaces of three small proteins under crowding: validation of the postprocessing method by direct simulation

NASA Astrophysics Data System (ADS)

Qin, Sanbo; Mittal, Jeetain; Zhou, Huan-Xiang

2013-08-01

We have developed a ‘postprocessing’ method for modeling biochemical processes such as protein folding under crowded conditions (Qin and Zhou 2009 Biophys. J. 97 12-19). In contrast to the direct simulation approach, in which the protein undergoing folding is simulated along with crowders, the postprocessing method requires only the folding simulation without crowders. The influence of the crowders is then obtained by taking conformations from the crowder-free simulation and calculating the free energies of transferring to the crowders. This postprocessing yields the folding free energy surface of the protein under crowding. Here the postprocessing results for the folding of three small proteins under ‘repulsive’ crowding are validated by those obtained previously by the direct simulation approach (Mittal and Best 2010 Biophys. J. 98 315-20). This validation confirms the accuracy of the postprocessing approach and highlights its distinct advantages in modeling biochemical processes under cell-like crowded conditions, such as enabling an atomistic representation of the test proteins.
Validation of RNAi Silencing Efficiency Using Gene Array Data shows 18.5% Failure Rate across 429 Independent Experiments.

PubMed

Munkácsy, Gyöngyi; Sztupinszki, Zsófia; Herman, Péter; Bán, Bence; Pénzváltó, Zsófia; Szarvas, Nóra; Győrffy, Balázs

2016-09-27

No independent cross-validation of success rate for studies utilizing small interfering RNA (siRNA) for gene silencing has been completed before. To assess the influence of experimental parameters like cell line, transfection technique, validation method, and type of control, we have to validate these in a large set of studies. We utilized gene chip data published for siRNA experiments to assess success rate and to compare methods used in these experiments. We searched NCBI GEO for samples with whole transcriptome analysis before and after gene silencing and evaluated the efficiency for the target and off-target genes using the array-based expression data. Wilcoxon signed-rank test was used to assess silencing efficacy and Kruskal-Wallis tests and Spearman rank correlation were used to evaluate study parameters. All together 1,643 samples representing 429 experiments published in 207 studies were evaluated. The fold change (FC) of down-regulation of the target gene was above 0.7 in 18.5% and was above 0.5 in 38.7% of experiments. Silencing efficiency was lowest in MCF7 and highest in SW480 cells (FC = 0.59 and FC = 0.30, respectively, P = 9.3E-06). Studies utilizing Western blot for validation performed better than those with quantitative polymerase chain reaction (qPCR) or microarray (FC = 0.43, FC = 0.47, and FC = 0.55, respectively, P = 2.8E-04). There was no correlation between type of control, transfection method, publication year, and silencing efficiency. Although gene silencing is a robust feature successfully cross-validated in the majority of experiments, efficiency remained insufficient in a significant proportion of studies. Selection of cell line model and validation method had the highest influence on silencing proficiency.
Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

PubMed

Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

2016-06-07

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .
Towards computer-assisted TTTS: Laser ablation detection for workflow segmentation from fetoscopic video.

PubMed

Vasconcelos, Francisco; Brandão, Patrick; Vercauteren, Tom; Ourselin, Sebastien; Deprest, Jan; Peebles, Donald; Stoyanov, Danail

2018-06-27

Intrauterine foetal surgery is the treatment option for several congenital malformations. For twin-to-twin transfusion syndrome (TTTS), interventions involve the use of laser fibre to ablate vessels in a shared placenta. The procedure presents a number of challenges for the surgeon, and computer-assisted technologies can potentially be a significant support. Vision-based sensing is the primary source of information from the intrauterine environment, and hence, vision approaches present an appealing approach for extracting higher level information from the surgical site. In this paper, we propose a framework to detect one of the key steps during TTTS interventions-ablation. We adopt a deep learning approach, specifically the ResNet101 architecture, for classification of different surgical actions performed during laser ablation therapy. We perform a two-fold cross-validation using almost 50 k frames from five different TTTS ablation procedures. Our results show that deep learning methods are a promising approach for ablation detection. To our knowledge, this is the first attempt at automating photocoagulation detection using video and our technique can be an important component of a larger assistive framework for enhanced foetal therapies. The current implementation does not include semantic segmentation or localisation of the ablation site, and this would be a natural extension in future work.
Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

Comparison of Grouping Schemes for Exposure to Total Dust in Cement Factories in Korea.

PubMed

Koh, Dong-Hee; Kim, Tae-Woo; Jang, Seung Hee; Ryu, Hyang-Woo; Park, Donguk

2015-08-01

The purpose of this study was to evaluate grouping schemes for exposure to total dust in cement industry workers using non-repeated measurement data. In total, 2370 total dust measurements taken from nine Portland cement factories in 1995-2009 were analyzed. Various grouping schemes were generated based on work process, job, factory, or average exposure. To characterize variance components of each grouping scheme, we developed mixed-effects models with a B-spline time trend incorporated as fixed effects and a grouping variable incorporated as a random effect. Using the estimated variance components, elasticity was calculated. To compare the prediction performances of different grouping schemes, 10-fold cross-validation tests were conducted, and root mean squared errors and pooled correlation coefficients were calculated for each grouping scheme. The five exposure groups created a posteriori by ranking job and factory combinations according to average dust exposure showed the best prediction performance and highest elasticity among various grouping schemes. Our findings suggest a grouping method based on ranking of job, and factory combinations would be the optimal choice in this population. Our grouping method may aid exposure assessment efforts in similar occupational settings, minimizing the misclassification of exposures. © The Author 2015. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.
Distant failure prediction for early stage NSCLC by analyzing PET with sparse representation

NASA Astrophysics Data System (ADS)

Hao, Hongxia; Zhou, Zhiguo; Wang, Jing

2017-03-01

Positron emission tomography (PET) imaging has been widely explored for treatment outcome prediction. Radiomicsdriven methods provide a new insight to quantitatively explore underlying information from PET images. However, it is still a challenging problem to automatically extract clinically meaningful features for prognosis. In this work, we develop a PET-guided distant failure predictive model for early stage non-small cell lung cancer (NSCLC) patients after stereotactic ablative radiotherapy (SABR) by using sparse representation. The proposed method does not need precalculated features and can learn intrinsically distinctive features contributing to classification of patients with distant failure. The proposed framework includes two main parts: 1) intra-tumor heterogeneity description; and 2) dictionary pair learning based sparse representation. Tumor heterogeneity is initially captured through anisotropic kernel and represented as a set of concatenated vectors, which forms the sample gallery. Then, given a test tumor image, its identity (i.e., distant failure or not) is classified by applying the dictionary pair learning based sparse representation. We evaluate the proposed approach on 48 NSCLC patients treated by SABR at our institute. Experimental results show that the proposed approach can achieve an area under the characteristic curve (AUC) of 0.70 with a sensitivity of 69.87% and a specificity of 69.51% using a five-fold cross validation.
Concurrent Validity of the International Family Quality of Life Survey.

PubMed

Samuel, Preethy S; Pociask, Fredrick D; DiZazzo-Miller, Rosanne; Carrellas, Ann; LeRoy, Barbara W

2016-01-01

The measurement of the social construct of Family Quality of Life (FQOL) is a parsimonious alternative to the current approach of measuring familial outcomes using a battery of tools related to individual-level outcomes. The purpose of this study was to examine the internal consistency and concurrent validity of the International FQOL Survey (FQOLS-2006), using cross-sectional data collected from 65 family caregivers of children with developmental disabilities. It shows a moderate correlation between the total FQOL scores of the FQOLS-2006 and the Beach Center's FQOL scale. The validity of five FQOLS-2006 domains was supported by the correlations between conceptually related domains.
Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

PubMed

Sun, Jiangming; Carlsson, Lars; Ahlberg, Ernst; Norinder, Ulf; Engkvist, Ola; Chen, Hongming

2017-07-24

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.
Cross-cultural adaptation of the Disability of Arm, Shoulder, and Hand questionnaire: Spanish for Puerto Rico Version

PubMed Central

Mulero-Portela, Ana L.; Colón-Santaella, Carmen L.; Cruz-Gomez, Cynthia

2010-01-01

The purpose of this study was to perform a cross-cultural adaptation of the Disability of Arm, Shoulder, and Hand (DASH) questionnaire to Spanish for Puerto Rico. Five steps were followed for the cross-cultural adaptation: forward translations into Spanish for Puerto Rico, synthesis of the translations, back translations into English, revision by an expert committee, and field test of the prefinal version. Psychometric characteristics of reliability and construct validity were evaluated for the final version. Internal consistency of the final version was high (Cronbach's α = 0.97) and item-to-total correlations were moderate (range from 0.44 to 0.85). Construct validity was evaluated by correlating the DASH with the scales of the Functional Assessment of Cancer Therapy - Breast. Fair to moderate correlations found in this study between the DASH and most scales of the Functional Assessment of Cancer Therapy - Breast support the construct validity of the Puerto Rico-Spanish DASH. The final version of the questionnaire was revised and approved by the Institute for Work and Health of Canada. Revisions to the original DASH English version are recommended. This version of the DASH is valid and reliable, and it can be used to evaluate outcomes in both clinical and research settings. PMID:19901616
Cross-Study Homogeneity of Psoriasis Gene Expression in Skin across a Large Expression Range

PubMed Central

Kerkof, Keith; Timour, Martin; Russell, Christopher B.

2013-01-01

Background In psoriasis, only limited overlap between sets of genes identified as differentially expressed (psoriatic lesional vs. psoriatic non-lesional) was found using statistical and fold-change cut-offs. To provide a framework for utilizing prior psoriasis data sets we sought to understand the consistency of those sets. Methodology/Principal Findings Microarray expression profiling and qRT-PCR were used to characterize gene expression in PP and PN skin from psoriasis patients. cDNA (three new data sets) and cRNA hybridization (four existing data sets) data were compared using a common analysis pipeline. Agreement between data sets was assessed using varying qualitative and quantitative cut-offs to generate a DEG list in a source data set and then using other data sets to validate the list. Concordance increased from 67% across all probe sets to over 99% across more than 10,000 probe sets when statistical filters were employed. The fold-change behavior of individual genes tended to be consistent across the multiple data sets. We found that genes with <2-fold change values were quantitatively reproducible between pairs of data-sets. In a subset of transcripts with a role in inflammation changes detected by microarray were confirmed by qRT-PCR with high concordance. For transcripts with both PN and PP levels within the microarray dynamic range, microarray and qRT-PCR were quantitatively reproducible, including minimal fold-changes in IL13, TNFSF11, and TNFRSF11B and genes with >10-fold changes in either direction such as CHRM3, IL12B and IFNG. Conclusions/Significance Gene expression changes in psoriatic lesions were consistent across different studies, despite differences in patient selection, sample handling, and microarray platforms but between-study comparisons showed stronger agreement within than between platforms. We could use cut-offs as low as log10(ratio) = 0.1 (fold-change = 1.26), generating larger gene lists that validate on independent data sets. The reproducibility of PP signatures across data sets suggests that different sample sets can be productively compared. PMID:23308107
TU-D-207B-01: A Prediction Model for Distinguishing Radiation Necrosis From Tumor Progression After Gamma Knife Radiosurgery Based On Radiomics Features From MR Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Z; MD Anderson Cancer Center, Houston, TX; Ho, A

Purpose: To develop and validate a prediction model using radiomics features extracted from MR images to distinguish radiation necrosis from tumor progression for brain metastases treated with Gamma knife radiosurgery. Methods: The images used to develop the model were T1 post-contrast MR scans from 71 patients who had had pathologic confirmation of necrosis or progression; 1 lesion was identified per patient (17 necrosis and 54 progression). Radiomics features were extracted from 2 images at 2 time points per patient, both obtained prior to resection. Each lesion was manually contoured on each image, and 282 radiomics features were calculated for eachmore » lesion. The correlation for each radiomics feature between two time points was calculated within each group to identify a subset of features with distinct values between two groups. The delta of this subset of radiomics features, characterizing changes from the earlier time to the later one, was included as a covariate to build a prediction model using support vector machines with a cubic polynomial kernel function. The model was evaluated with a 10-fold cross-validation. Results: Forty radiomics features were selected based on consistent correlation values of approximately 0 for the necrosis group and >0.2 for the progression group. In performing the 10-fold cross-validation, we narrowed this number down to 11 delta radiomics features for the model. This 11-delta-feature model showed an overall prediction accuracy of 83.1%, with a true positive rate of 58.8% in predicting necrosis and 90.7% for predicting tumor progression. The area under the curve for the prediction model was 0.79. Conclusion: These delta radiomics features extracted from MR scans showed potential for distinguishing radiation necrosis from tumor progression. This tool may be a useful, noninvasive means of determining the status of an enlarging lesion after radiosurgery, aiding decision-making regarding surgical resection versus conservative medical management.« less
Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.
Resistance of green lacewing, Chrysoperla carnea Stephens to nitenpyram: Cross-resistance patterns, mechanism, stability, and realized heritability.

PubMed

Mansoor, Muhammad Mudassir; Raza, Abu Bakar Muhammad; Abbas, Naeem; Aqueel, Muhammad Anjum; Afzal, Muhammad

2017-01-01

The green lacewing, Chrysoperla carnea Stephens (Neuroptera: Chrysopidae) is a major generalist predator employed in integrated pest management (IPM) plans for pest control on many crops. Nitenpyram, a neonicotinoid insecticide has widely been used against the sucking pests of cotton in Pakistan. Therefore, a field green lacewing strain was exposed to nitenpyram for five generations to investigate resistance evolution, cross-resistance pattern, stability, realized heritability, and mechanisms of resistance. Before starting the selection with nitenpyram, a field collected strain showed 22.08-, 23.09-, 484.69- and 602.90-fold resistance to nitenpyram, buprofezin, spinosad and acetamiprid, respectively compared with the Susceptible strain. After continuous selection for five generations (G1-G5) with nitenpyram in the laboratory, the Field strain (Niten-SEL) developed a resistance ratio of 423.95 at G6. The Niten-SEL strain at G6 showed no cross-resistance to buprofezin and acetamiprid and negative cross-resistance to spinosad compared with the Field strain (G1). For resistance stability, the Niten-SEL strain was left unexposed to any insecticide for four generations (G6-G9) and bioassay results at G10 showed that resistance to nitenpyram, buprofezin and spinosad was stable, while resistance to acetamiprid was unstable. The realized heritability values were 0.97, 0.16, 0.03, and -0.16 to nitenpyram, buprofezin, acetamiprid and spinosad, respectively, after five generations of selection. Moreover, the enzyme inhibitors (PBO or DEF) significantly decreased the nitenpyram resistance in the resistant strain, suggesting that resistance was due to microsomal oxidases and esterases. These results are very helpful for integration of green lacewings in IPM programs. Copyright © 2016 Elsevier B.V. All rights reserved.
Real Alerts and Artifact Classification in Archived Multi-signal Vital Sign Monitoring Data—Implications for Mining Big Data — Implications for Mining Big Data

PubMed Central

Hravnak, Marilyn; Chen, Lujie; Dubrawski, Artur; Bose, Eliezer; Clermont, Gilles; Pinsky, Michael R.

2015-01-01

PURPOSE Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby “cleaning” such data for future modeling. METHODS 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral arterial oxygen saturation [SpO2] at 1/20Hz., and noninvasive oscillometric blood pressure [BP]) Time data were across stability thresholds defined VS event epochs. Data were divided Block 1 as the ML training/cross-validation set and Block 2 the test set. Expert clinicians annotated Block 1 events as perceived real or artifact. After feature extraction, ML algorithms were trained to create and validate models automatically classifying events as real or artifact. The models were then tested on Block 2. RESULTS Block 1 yielded 812 VS events, with 214 (26%) judged by experts as artifact (RR 43%, SpO2 40%, BP 15%, HR 2%). ML algorithms applied to the Block 1 training/cross-validation set (10-fold cross-validation) gave area under the curve (AUC) scores of 0.97 RR, 0.91 BP and 0.76 SpO2. Performance when applied to Block 2 test data was AUC 0.94 RR, 0.84 BP and 0.72 SpO2). CONCLUSIONS ML-defined algorithms applied to archived multi-signal continuous VS monitoring data allowed accurate automated classification of VS alerts as real or artifact, and could support data mining for future model building. PMID:26438655
Polymer Uncrossing and Knotting in Protein Folding, and Their Role in Minimal Folding Pathways

PubMed Central

Mohazab, Ali R.; Plotkin, Steven S.

2013-01-01

We introduce a method for calculating the extent to which chain non-crossing is important in the most efficient, optimal trajectories or pathways for a protein to fold. This involves recording all unphysical crossing events of a ghost chain, and calculating the minimal uncrossing cost that would have been required to avoid such events. A depth-first tree search algorithm is applied to find minimal transformations to fold , , , and knotted proteins. In all cases, the extra uncrossing/non-crossing distance is a small fraction of the total distance travelled by a ghost chain. Different structural classes may be distinguished by the amount of extra uncrossing distance, and the effectiveness of such discrimination is compared with other order parameters. It was seen that non-crossing distance over chain length provided the best discrimination between structural and kinetic classes. The scaling of non-crossing distance with chain length implies an inevitable crossover to entanglement-dominated folding mechanisms for sufficiently long chains. We further quantify the minimal folding pathways by collecting the sequence of uncrossing moves, which generally involve leg, loop, and elbow-like uncrossing moves, and rendering the collection of these moves over the unfolded ensemble as a multiple-transformation “alignment”. The consensus minimal pathway is constructed and shown schematically for representative cases of an , , and knotted protein. An overlap parameter is defined between pathways; we find that proteins have minimal overlap indicating diverse folding pathways, knotted proteins are highly constrained to follow a dominant pathway, and proteins are somewhere in between. Thus we have shown how topological chain constraints can induce dominant pathway mechanisms in protein folding. PMID:23365638
Combined Sensory Impairment (Deaf-Blindness) in Five Percent of Adults with Intellectual Disabilities

ERIC Educational Resources Information Center

Meuwese-Jongejeugd, Anneke; van Splunder, Jacques; Vink, Marianne; Stilma, Jan Sietse; van Zanten, Bert; Verschuure, Hans; Bernsen, Roos; Evenhuis, Heleen

2008-01-01

Our purpose in this cross-sectional study with 1,598 adult clients who had intellectual disabilities was to obtain valid prevalences of sensory impairments and to identify associations. The diagnoses were made through ophthalmologic and audiometric assessments, applying WHO/IASSID definitions. Re-weighted prevalences were 5.0% (95% CI 3.9-6.2%)…
Estimation of personal PM2.5 and BC exposure by a modeling approach - Results of a panel study in Shanghai, China.

PubMed

Chen, Chen; Cai, Jing; Wang, Cuicui; Shi, Jingjin; Chen, Renjie; Yang, Changyuan; Li, Huichu; Lin, Zhijing; Meng, Xia; Zhao, Ang; Liu, Cong; Niu, Yue; Xia, Yongjie; Peng, Li; Zhao, Zhuohui; Chillrud, Steven; Yan, Beizhan; Kan, Haidong

2018-06-06

Epidemiologic studies of PM 2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and black carbon (BC) typically use ambient measurements as exposure proxies given that individual measurement is infeasible among large populations. Failure to account for variation in exposure will bias epidemiologic study results. The ability of ambient measurement as a proxy of exposure in regions with heavy pollution is untested. We aimed to investigate effects of potential determinants and to estimate PM 2.5 and BC exposure by a modeling approach. We collected 417 24 h personal PM 2.5 and 130 72 h personal BC measurements from a panel of 36 nonsmoking college students in Shanghai, China. Each participant underwent 4 rounds of three consecutive 24-h sampling sessions through December 2014 to July 2015. We applied backwards regression to construct mixed effect models incorporating all accessible variables of ambient pollution, climate and time-location information for exposure prediction. All models were evaluated by marginal R 2 and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV) and a 10-fold cross-validation (10-fold CV). Personal PM 2.5 was 47.6% lower than ambient level, with mean (±Standard Deviation, SD) level of 39.9 (±32.1) μg/m 3 ; whereas personal BC (6.1 (±2.8) μg/m 3 ) was about one-fold higher than the corresponding ambient concentrations. Ambient levels were the most significant determinants of PM 2.5 and BC exposure. Meteorological and season indicators were also important predictors. Our final models predicted 75% of the variance in 24 h personal PM 2.5 and 72 h personal BC. LOOCV analysis showed an R 2 (RMSE) of 0.73 (0.40) for PM 2.5 and 0.66 (0.27) for BC. Ten-fold CV analysis showed a R 2 (RMSE) of 0.73 (0.41) for PM 2.5 and 0.68 (0.26) for BC. We used readily accessible data and established intuitive models that can predict PM 2.5 and BC exposure. This modeling approach can be a feasible solution for PM exposure estimation in epidemiological studies. Copyright © 2018 Elsevier Ltd. All rights reserved.
Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

NASA Astrophysics Data System (ADS)

Wang, Leilei; Cheng, Jinyong

2018-03-01

Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.
Large scale wind tunnel investigation of a folding tilt rotor

NASA Technical Reports Server (NTRS)

1972-01-01

A twenty-five foot diameter folding tilt rotor was tested in a large scale wind tunnel to determine its aerodynamic characteristics in unfolded, partially folded, and fully folded configurations. During the tests, the rotor completed over forty start/stop sequences. After completing the sequences in a stepwise manner, smooth start/stop transitions were made in approximately two seconds. Wind tunnel speeds up through seventy-five knots were used, at which point the rotor mast angle was increased to four degrees, corresponding to a maneuver condition of one and one-half g.
Design and 4D Printing of Cross-Folded Origami Structures: A Preliminary Investigation

PubMed Central

Teoh, Joanne Ee Mei; Feng, Xiaofan; Zhao, Yue; Liu, Yong

2018-01-01

In 4D printing research, different types of complex structure folding and unfolding have been investigated. However, research on cross-folding of origami structures (defined as a folding structure with at least two overlapping folds) has not been reported. This research focuses on the investigation of cross-folding structures using multi-material components along different axes and different horizontal hinge thickness with single homogeneous material. Tensile tests were conducted to determine the impact of multi-material components and horizontal hinge thickness. In the case of multi-material structures, the hybrid material composition has a significant impact on the overall maximum strain and Young’s modulus properties. In the case of single material structures, the shape recovery speed is inversely proportional to the horizontal hinge thickness, while the flexural or bending strength is proportional to the horizontal hinge thickness. A hinge with a thickness of 0.5 mm could be folded three times prior to fracture whilst a hinge with a thickness of 0.3 mm could be folded only once prior to fracture. A hinge with a thickness of 0.1 mm could not even be folded without cracking. The introduction of a physical hole in the center of the folding/unfolding line provided stress relief and prevented fracture. A complex flower petal shape was used to successfully demonstrate the implementation of overlapping and non-overlapping folding lines using both single material segments and multi-material segments. Design guidelines for establishing cross-folding structures using multi-material components along different axes and different horizontal hinge thicknesses with single or homogeneous material were established. These guidelines can be used to design and implement complex origami structures with overlapping and non-overlapping folding lines. Combined overlapping folding structures could be implemented and allocating specific hole locations in the overall designs could be further explored. In addition, creating a more precise prediction by investigating sets of in between hinge thicknesses and comparing the folding times before fracture, will be the subject of future work. PMID:29510503
QSPR for predicting chloroform formation in drinking water disinfection.

PubMed

Luilo, G B; Cabaniss, S E

2011-01-01

Chlorination is the most widely used technique for water disinfection, but may lead to the formation of chloroform (trichloromethane; TCM) and other by-products. This article reports the first quantitative structure-property relationship (QSPR) for predicting the formation of TCM in chlorinated drinking water. Model compounds (n = 117) drawn from 10 literature sources were divided into training data (n = 90, analysed by five-way leave-many-out internal cross-validation) and external validation data (n = 27). QSPR internal cross-validation had Q² = 0.94 and root mean square error (RMSE) of 0.09 moles TCM per mole compound, consistent with external validation Q2 of 0.94 and RMSE of 0.08 moles TCM per mole compound, and met criteria for high predictive power and robustness. In contrast, log TCM QSPR performed poorly and did not meet the criteria for predictive power. The QSPR predictions were consistent with experimental values for TCM formation from tannic acid and for model fulvic acid structures. The descriptors used are consistent with a relatively small number of important TCM precursor structures based upon 1,3-dicarbonyls or 1,3-diphenols.
Development and validation of a gene expression oligo microarray for the gilthead sea bream (Sparus aurata).

PubMed

Ferraresso, Serena; Vitulo, Nicola; Mininni, Alba N; Romualdi, Chiara; Cardazzo, Barbara; Negrisolo, Enrico; Reinhardt, Richard; Canario, Adelino V M; Patarnello, Tomaso; Bargelloni, Luca

2008-12-03

Aquaculture represents the most sustainable alternative of seafood supply to substitute for the declining marine fisheries, but severe production bottlenecks remain to be solved. The application of genomic technologies offers much promise to rapidly increase our knowledge on biological processes in farmed species and overcome such bottlenecks. Here we present an integrated platform for mRNA expression profiling in the gilthead sea bream (Sparus aurata), a marine teleost of great importance for aquaculture. A public data base was constructed, consisting of 19,734 unique clusters (3,563 contigs and 16,171 singletons). Functional annotation was obtained for 8,021 clusters. Over 4,000 sequences were also associated with a GO entry. Two 60mer probes were designed for each gene and in-situ synthesized on glass slides using Agilent SurePrint technology. Platform reproducibility and accuracy were assessed on two early stages of sea bream development (one-day and four days old larvae). Correlation between technical replicates was always > 0.99, with strong positive correlation between paired probes. A two class SAM test identified 1,050 differentially expressed genes between the two developmental stages. Functional analysis suggested that down-regulated transcripts (407) in older larvae are mostly essential/housekeeping genes, whereas tissue-specific genes are up-regulated in parallel with the formation of key organs (eye, digestive system). Cross-validation of microarray data was carried out using quantitative qRT-PCR on 11 target genes, selected to reflect the whole range of fold-change and both up-regulated and down-regulated genes. A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates. Good concordance between qRT-PCR and microarray data was observed between 2- and 7-fold change, while fold-change compression in the microarray was present for differences greater than 10-fold in the qRT-PCR. A highly reliable oligo-microarray platform was developed and validated for the gilthead sea bream despite the presently limited knowledge of the species transcriptome. Because of the flexible design this array will be able to accommodate additional probes as soon as novel unique transcripts are available.
Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

PubMed

Wu, J; Awate, S P; Licht, D J; Clouchoux, C; du Plessis, A J; Avants, B B; Vossough, A; Gee, J C; Limperopoulos, C

2015-07-01

Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates. © 2015 by American Journal of Neuroradiology.
Discrimination among populations of sockeye salmon fry with Fourier analysis of otolith banding patterns formed during incubation

USGS Publications Warehouse

Finn, James E.; Burger, Carl V.; Holland-Bartels, Leslie E.

1997-01-01

We used otolith banding patterns formed during incubation to discriminate among hatchery- and wild-incubated fry of sockeye salmon Oncorhynchus nerka from Tustumena Lake, Alaska. Fourier analysis of otolith luminance profiles was used to describe banding patterns: the amplitudes of individual Fourier harmonics were discriminant variables. Correct classification of otoliths to either hatchery or wild origin was 83.1% (cross-validation) and 72.7% (test data) with the use of quadratic discriminant function analysts on 10 Fourier amplitudes. Overall classification rates among the six test groups (one hatchery and five wild groups) were 46.5% (cross-validation) and 39.3% (test data) with the use of linear discriminant function analysis on 16 Fourier amplitudes. Although classification rates for wild-incubated fry from any one site never exceeded 67% (cross-validation) or 60% (test data), location-specific information was evident for all groups because the probability of classifying an individual to its true incubation location was significantly greater than chance. Results indicate phenotypic differences in otolith microstructure among incubation sites separated by less than 10 km. Analysis of otolith luminance profiles is a potentially useful technique for discriminating among and between various populations of hatchery and wild fish.

Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia.

PubMed

Płoński, Piotr; Gradkowski, Wojciech; Altarelli, Irene; Monzalvo, Karla; van Ermingen-Marbach, Muna; Grande, Marion; Heim, Stefan; Marchewka, Artur; Bogorodzki, Piotr; Ramus, Franck; Jednoróg, Katarzyna

2017-02-01

Despite decades of research, the anatomical abnormalities associated with developmental dyslexia are still not fully described. Studies have focused on between-group comparisons in which different neuroanatomical measures were generally explored in isolation, disregarding potential interactions between regions and measures. Here, for the first time a multivariate classification approach was used to investigate grey matter disruptions in children with dyslexia in a large (N = 236) multisite sample. A variety of cortical morphological features, including volumetric (volume, thickness and area) and geometric (folding index and mean curvature) measures were taken into account and generalizability of classification was assessed with both 10-fold and leave-one-out cross validation (LOOCV) techniques. Classification into control vs. dyslexic subjects achieved above chance accuracy (AUC = 0.66 and ACC = 0.65 in the case of 10-fold CV, and AUC = 0.65 and ACC = 0.64 using LOOCV) after principled feature selection. Features that discriminated between dyslexic and control children were exclusively situated in the left hemisphere including superior and middle temporal gyri, subparietal sulcus and prefrontal areas. They were related to geometric properties of the cortex, with generally higher mean curvature and a greater folding index characterizing the dyslexic group. Our results support the hypothesis that an atypical curvature pattern with extra folds in left hemispheric perisylvian regions characterizes dyslexia. Hum Brain Mapp 38:900-908, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
More than the sum of its parts: Coarse-grained peptide-lipid interactions from a simple cross-parametrization

NASA Astrophysics Data System (ADS)

Bereau, Tristan; Wang, Zun-Jing; Deserno, Markus

2014-03-01

Interfacial systems are at the core of fascinating phenomena in many disciplines, such as biochemistry, soft-matter physics, and food science. However, the parametrization of accurate, reliable, and consistent coarse-grained (CG) models for systems at interfaces remains a challenging endeavor. In the present work, we explore to what extent two independently developed solvent-free CG models of peptides and lipids—of different mapping schemes, parametrization methods, target functions, and validation criteria—can be combined by only tuning the cross-interactions. Our results show that the cross-parametrization can reproduce a number of structural properties of membrane peptides (for example, tilt and hydrophobic mismatch), in agreement with existing peptide-lipid CG force fields. We find encouraging results for two challenging biophysical problems: (i) membrane pore formation mediated by the cooperative action of several antimicrobial peptides, and (ii) the insertion and folding of the helix-forming peptide WALP23 in the membrane.
Mechanical restoration of large-scale folded multilayers using the finite element method: Application to the Zagros Simply Folded Belt, N-Iraq

NASA Astrophysics Data System (ADS)

Frehner, Marcel; Reif, Daniel; Grasemann, Bernhard

2010-05-01

There are a large number of numerical finite element studies concerned with modeling the evolution of folded geological layers through time. This body of research includes many aspects of folding and many different approaches, such as two- and three-dimensional studies, single-layer folding, detachment folding, development of chevron folds, Newtonian, power-law viscous and more complex rheologies, influence of anisotropy, pure-shear, simple-shear and other boundary conditions and so forth. In recent years, studies of multilayer folding emerged, thanks to more advanced mesh generator software and increased computational power. Common to all of these studies is the fact that they consider a forward directed time evolution, as in nature. Very few studies use the finite element method for reverse-time simulations. In such studies, folded geological layers are taken as initial conditions for the numerical simulation. The folding process is reversed by changing the signs of the boundary conditions that supposedly drove the folding process. In such studies, the geometry of the geological layers before the folding process is searched and the amount of shortening necessary for the final folded geometry can be calculated. In contrast to a kinematic or geometric fold restoration procedure, the described approach takes the mechanical behavior of the geological layers into account, such as rheology and the relative strength of the individual layers. This approach is therefore called mechanical restoration of folds. In this study, the concept of mechanical restoration is applied to a two-dimensional 50km long NE-SW-cross-section through the Zagros Simply Folded Belt in Iraqi Kurdistan, NE from the city of Erbil. The Simply Folded Belt is dominated by gentle to open folding and faults are either absent or record only minor offset. Therefore, this region is ideal for testing the concept of mechanical restoration. The profile used is constructed from structural field measurements and digital elevation models using the dip-domain method for balancing the cross-section. The lithology consists of Cretaceous to Cenozoic sediments. Massive carbonate rock units act as the competent layers compared to the incompetent behavior of siltstone, claystone and marl layers. We show the first results of the mechanical restoration of the Zagros cross-section and we discuss advantages and disadvantages, as well as some technical aspects of the applied method. First results indicate that a shortening of at least 50% was necessary to create the present-day folded cross-section. This value is higher than estimates of the amount of shortening solely based on kinematic or geometric restoration. One particular problem that is discussed is the presence of (unnaturally) sharp edges in a balanced cross-section produced using the dip-domain method, which need to be eliminated for mechanical restoration calculations to get reasonable results.
3D Bragg coherent diffractive imaging of five-fold multiply twinned gold nanoparticle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Jong Woo; Ulvestad, Andrew; Manna, Sohini

The formation mechanism of five-fold multiply twinned nanoparticles has been a long-term topic because of their geometrical incompatibility. So, various models have been proposed to explain how the internal structure of the multiply twinned nanoparticles accommodates the constraints of the solid-angle deficiency. Here, we investigate the internal structure, strain field and strain energy density of 600 nm sized five-fold multiply twinned gold nanoparticles quantitatively using Bragg coherent diffractive imaging, which is suitable for the study of buried defects and three-dimensional strain distribution with great precision. Our study reveals that the strain energy density in five-fold multiply twinned gold nanoparticles ismore » an order of magnitude higher than that of the single nanocrystals such as an octahedron and triangular plate synthesized under the same conditions. This result indicates that the strain developed while accommodating an angular misfit, although partially released through the introduction of structural defects, is still large throughout the crystal.« less
3D Bragg coherent diffractive imaging of five-fold multiply twinned gold nanoparticle

DOE PAGES

Kim, Jong Woo; Ulvestad, Andrew; Manna, Sohini; ...

2017-08-11

The formation mechanism of five-fold multiply twinned nanoparticles has been a long-term topic because of their geometrical incompatibility. So, various models have been proposed to explain how the internal structure of the multiply twinned nanoparticles accommodates the constraints of the solid-angle deficiency. Here, we investigate the internal structure, strain field and strain energy density of 600 nm sized five-fold multiply twinned gold nanoparticles quantitatively using Bragg coherent diffractive imaging, which is suitable for the study of buried defects and three-dimensional strain distribution with great precision. Our study reveals that the strain energy density in five-fold multiply twinned gold nanoparticles ismore » an order of magnitude higher than that of the single nanocrystals such as an octahedron and triangular plate synthesized under the same conditions. This result indicates that the strain developed while accommodating an angular misfit, although partially released through the introduction of structural defects, is still large throughout the crystal.« less
Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

PubMed

Lopes, F B; Wu, X-L; Li, H; Xu, J; Perkins, T; Genho, J; Ferretti, R; Tait, R G; Bauck, S; Rosa, G J M

2018-02-01

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle. © 2018 Blackwell Verlag GmbH.
Estimating energy expenditure from heart rate in older adults: a case for calibration.

PubMed

Schrack, Jennifer A; Zipunnikov, Vadim; Goldsmith, Jeff; Bandeen-Roche, Karen; Crainiceanu, Ciprian M; Ferrucci, Luigi

2014-01-01

Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear. To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration. Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance. Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min). These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.
Automatic classification of tissue malignancy for breast carcinoma diagnosis.

PubMed

Fondón, Irene; Sarmiento, Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo

2018-05-01

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.
Dual-time point scanning of integrated FDG PET/CT for the evaluation of mediastinal and hilar lymph nodes in non-small cell lung cancer diagnosed as operable by contrast-enhanced CT.

PubMed

Kasai, Takami; Motoori, Ken; Horikoshi, Takuro; Uchiyama, Katsuhiro; Yasufuku, Kazuhiro; Takiguchi, Yuichi; Takahashi, Fumiaki; Kuniyasu, Yoshio; Ito, Hisao

2010-08-01

To evaluate whether dual-time point scanning with integrated fluorine-18 fluorodeoxyglucose ((18)F-FDG) positron emission tomography and computed tomography (PET/CT) is useful for evaluation of mediastinal and hilar lymph nodes in non-small cell lung cancer diagnosed as operable by contrast-enhanced CT. PET/CT data and pathological findings of 560 nodal stations in 129 patients with pathologically proven non-small cell lung cancer diagnosed as operable by contrast-enhanced CT were reviewed retrospectively. Standardized uptake values (SUVs) on early scans (SUVe) 1h, and on delayed scans (SUVd) 2h after FDG injection of each nodal station were measured. Retention index (RI) (%) was calculated by subtracting SUVe from SUVd and dividing by SUVe. Logistic regression analysis was performed with seven kinds of models, consisting of (1) SUVe, (2) SUVd, (3) RI, (4) SUVe and SUVd, (5) SUVe and RI, (6) SUVd and RI, and (7) SUVe, SUVd and RI. The seven derived models were compared by receiver-operating characteristic (ROC) analysis. k-Fold cross-validation was performed with k values of 5 and 10. p<0.05 was considered statistically significant. Model (1) including the term of SUVe showed the largest area under the ROC curve among the seven models. The cut-off probability of metastasis of 3.5% with SUVe of 2.5 revealed a sensitivity of 78% and a specificity of 81% on ROC analysis, and approximately 60% and 80% on k-fold cross-validation. Single scanning of PET/CT is sufficiently useful for evaluating mediastinal and hilar nodes for metastasis. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.
Development and Psychometric Evaluation of an Instrument to Assess Cross-Cultural Competence of Healthcare Professionals (CCCHP)

PubMed Central

Bernhard, Gerda; Knibbe, Ronald A.; von Wolff, Alessa; Dingoyan, Demet; Schulz, Holger; Mösko, Mike

2015-01-01

Background Cultural competence of healthcare professionals (HCPs) is recognized as a strategy to reduce cultural disparities in healthcare. However, standardised, valid and reliable instruments to assess HCPs’ cultural competence are notably lacking. The present study aims to 1) identify the core components of cultural competence from a healthcare perspective, 2) to develop a self-report instrument to assess cultural competence of HCPs and 3) to evaluate the psychometric properties of the new instrument. Methods The conceptual model and initial item pool, which were applied to the cross-cultural competence instrument for the healthcare profession (CCCHP), were derived from an expert survey (n = 23), interviews with HCPs (n = 12), and a broad narrative review on assessment instruments and conceptual models of cultural competence. The item pool was reduced systematically, which resulted in a 59-item instrument. A sample of 336 psychologists, in advanced psychotherapeutic training, and 409 medical students participated, in order to evaluate the construct validity and reliability of the CCCHP. Results Construct validity was supported by principal component analysis, which led to a 32-item six-component solution with 50% of the total variance explained. The different dimensions of HCPs’ cultural competence are: Cross-Cultural Motivation/Curiosity, Cross-Cultural Attitudes, Cross-Cultural Skills, Cross-Cultural Knowledge/Awareness and Cross-Cultural Emotions/Empathy. For the total instrument, the internal consistency reliability was .87 and the dimension’s Cronbach’s α ranged from .54 to .84. The discriminating power of the CCCHP was indicated by statistically significant mean differences in CCCHP subscale scores between predefined groups. Conclusions The 32-item CCCHP exhibits acceptable psychometric properties, particularly content and construct validity to examine HCPs’ cultural competence. The CCCHP with its five dimensions offers a comprehensive assessment of HCPs’ cultural competence, and has the ability to distinguish between groups that are expected to differ in cultural competence. This instrument can foster professional development through systematic self-assessment and thus contributes to improve the quality of patient care. PMID:26641876
Development and Psychometric Evaluation of an Instrument to Assess Cross-Cultural Competence of Healthcare Professionals (CCCHP).

PubMed

Bernhard, Gerda; Knibbe, Ronald A; von Wolff, Alessa; Dingoyan, Demet; Schulz, Holger; Mösko, Mike

2015-01-01

Cultural competence of healthcare professionals (HCPs) is recognized as a strategy to reduce cultural disparities in healthcare. However, standardised, valid and reliable instruments to assess HCPs' cultural competence are notably lacking. The present study aims to 1) identify the core components of cultural competence from a healthcare perspective, 2) to develop a self-report instrument to assess cultural competence of HCPs and 3) to evaluate the psychometric properties of the new instrument. The conceptual model and initial item pool, which were applied to the cross-cultural competence instrument for the healthcare profession (CCCHP), were derived from an expert survey (n = 23), interviews with HCPs (n = 12), and a broad narrative review on assessment instruments and conceptual models of cultural competence. The item pool was reduced systematically, which resulted in a 59-item instrument. A sample of 336 psychologists, in advanced psychotherapeutic training, and 409 medical students participated, in order to evaluate the construct validity and reliability of the CCCHP. Construct validity was supported by principal component analysis, which led to a 32-item six-component solution with 50% of the total variance explained. The different dimensions of HCPs' cultural competence are: Cross-Cultural Motivation/Curiosity, Cross-Cultural Attitudes, Cross-Cultural Skills, Cross-Cultural Knowledge/Awareness and Cross-Cultural Emotions/Empathy. For the total instrument, the internal consistency reliability was .87 and the dimension's Cronbach's α ranged from .54 to .84. The discriminating power of the CCCHP was indicated by statistically significant mean differences in CCCHP subscale scores between predefined groups. The 32-item CCCHP exhibits acceptable psychometric properties, particularly content and construct validity to examine HCPs' cultural competence. The CCCHP with its five dimensions offers a comprehensive assessment of HCPs' cultural competence, and has the ability to distinguish between groups that are expected to differ in cultural competence. This instrument can foster professional development through systematic self-assessment and thus contributes to improve the quality of patient care.
Viscoelastic properties of rabbit vocal folds after augmentation.

PubMed

Hertegård, Stellan; Dahlqvist, Ake; Laurent, Claude; Borzacchiello, Assunta; Ambrosio, Luigi

2003-03-01

Vocal fold function is closely related to tissue viscoelasticity. Augmentation substances may alter the viscoelastic properties of vocal fold tissues and hence their vibratory capacity. We sought to investigate the viscoelastic properties of rabbit vocal folds in vitro after injections of various augmentation substances. Polytetrafluoroethylene (Teflon), cross-linked collagen (Zyplast), and cross-linked hyaluronan, hylan b gel (Hylaform) were injected into the lamina propria and the thyroarytenoid muscle of rabbit vocal folds. Dynamic viscosity of the injected vocal fold as a function of frequency was measured with a Bohlin parallel-plate rheometer during small-amplitude oscillation. All injected vocal folds showed a decreasing dynamic viscosity with increasing frequency. Vocal fold samples injected with hylan b gel showed the lowest dynamic viscosity, quite close to noninjected control samples. Vocal folds injected with polytetrafluoroethylene showed the highest dynamic viscosity followed by the collagen samples. The data indicated that hylan b gel in short-term renders the most natural viscoelastic properties to the vocal fold among the substances tested. This is of importance to restore/preserve the vibratory capacity of the vocal folds when glottal insufficiency is treated with injections.
Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

PubMed

Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

2014-11-01

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of our knowledge, this is the first attempt to predict protein-binding nucleotides in a given DNA sequence from the sequence data alone. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Thiamethoxam resistance selected in the western flower thrips Frankliniella occidentalis (Thysanoptera: Thripidae): cross-resistance patterns, possible biochemical mechanisms and fitness costs analysis.

PubMed

Gao, Cong-Fen; Ma, Shao-Zhi; Shan, Cai-Hui; Wu, Shun-Fan

2014-09-01

The western flower thrips (WFT) Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), an important pest of various crops in the world, has invaded China since 2003. To understand the risks and to determine possible mechanisms of resistance to thiamethoxam in WFT, a resistant strain was selected under the laboratory conditions. Cross-resistance and the possible biochemical resistance mechanisms were investigated in this study. A 15.1-fold thiamethoxam-resistant WFT strain (TH-R) was established after selection for 55 generations. Compared with the susceptible strain (TH-S), the selected TH-R strain showed extremely high level cross-resistance to imidaclothiz (392.1-fold) and low level cross-resistance to dinotefuran (5.7-fold), acetamiprid (2.9-fold) and emamectin benzoate (2.1-fold), respectively. No cross-resistance to other fourteen insecticides was detected. Synergism tests showed that piperonyl butoxide (PBO) and triphenyl phosphate (TPP) produced a high synergism of thiamethoxam effects in the TH-R strain (2.6- and 2.6-fold respectively). However, diethyl maleate (DEM) did not act synergistically with thiamethoxam. Biochemical assays showed that mixed function oxidase (MFO) activities and carboxylesterase (CarE) activity of the TH-R strain were 2.8- and 1.5-fold higher than that of the TH-S strain, respectively. When compared with the TH-S strain, the TH-R strain had a relative fitness of 0.64. The results show that WFT develops resistance to thiamethoxam after continuous application and thiamethoxam resistance had considerable fitness costs in the WFT. It appears that enhanced metabolism mediated by cytochrome P450 monooxygenases and CarE was a major mechanism for thiamethoxam resistance in the WFT. The use of cross-resistance insecticides, including imidaclothiz and dinotefuran, should be avoided for sustainable resistance management. Copyright © 2014 Elsevier Inc. All rights reserved.
Geomorphology of the Southwest Coast of County Cork, Ireland: A Look into the Rocks, Folds, and Glacial Scours

NASA Astrophysics Data System (ADS)

Bowden, S.; Wireman, R.; Sautter, L.; Beutel, E. K.

2015-12-01

Bathymetric data were collected off the southwest coast of County Cork, Ireland by the joint INFOMAR project between the Marine Institute of Ireland and the Geologic Survey of Ireland. Data were collected using a Kongsberg EM2040 multibeam sonar on the R/V Celtic Voyager, in August and September 2014, and were post-processed with CARIS HIPS and SIPS 8.1 and 9.0 software to create 2D and 3D bathymetric surfaces. From the computer generated images, some of the lithologic formations were relatively aged and observed. The studied regions range in depth from 20 to 118 m, with shallower areas to the northeast. Several large rock outcrops occur, the larger of which shows a vertical rise of nearly 20 m. These outcrops are oriented in a northeast-southwest direction, and exhibit significant bed folding, regional folding, tilted beds, and cross joints. The folds studied are plunging chevron folds. These folds have a northeast-southwest fold axis orthogonal to the cross joints and are older relative to the jointing systems. The NE-SW joints are older than the NW-SE joints due to their correlation with drainage and erosion patterns. Regional folding is the youngest feature due to its superposition on the chevron folding and jointing systems. The interaction of cross jointing and folding is consistent with the geologic history of the area, and creates a unique bathymetry worthy of further study.
PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

PubMed

Araki, Tadashi; Ikeda, Nobutaka; Shukla, Devarshi; Jain, Pankaj K; Londhe, Narendra D; Shrivastava, Vimal K; Banchhor, Sumit K; Saba, Luca; Nicolaides, Andrew; Shafique, Shoaib; Laird, John R; Suri, Jasjit S

2016-05-01

Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Mechanical versus kinematical shortening reconstructions of the Zagros High Folded Zone (Kurdistan region of Iraq)

NASA Astrophysics Data System (ADS)

Frehner, Marcel; Reif, Daniel; Grasemann, Bernhard

2012-06-01

This paper compares kinematical and mechanical techniques for the palinspastic reconstruction of folded cross sections in collision orogens. The studied area and the reconstructed NE-SW trending, 55.5 km long cross section is located in the High Folded Zone of the Zagros fold-and-thrust belt in the Kurdistan region of Iraq. The present-day geometry of the cross section has been constructed from field as well as remote sensing data. In a first step, the structures and the stratigraphy are simplified and summarized in eight units trying to identify the main geometric and mechanical parameters. In a second step, the shortening is kinematically estimated using the dip domain method to 11%-15%. Then the same cross section is used in a numerical finite element model to perform dynamical unfolding simulations taking various rheological parameters into account. The main factor allowing for an efficient dynamic unfolding is the presence of interfacial slip conditions between the mechanically strong units. Other factors, such as Newtonian versus power law viscous rheology or the presence of a basement, affect the numerical simulations much less strongly. If interfacial slip is accounted for, fold amplitudes are reduced efficiently during the dynamical unfolding simulations, while welded layer interfaces lead to unrealistic shortening estimates. It is suggested that interfacial slip and decoupling of the deformation along detachment horizons is an important mechanical parameter that controlled the folding processes in the Zagros High Folded Zone.
Mechanical versus kinematical shortening reconstructions of the Zagros High Folded Zone (Kurdistan Region of Iraq)

NASA Astrophysics Data System (ADS)

Frehner, M.; Reif, D.; Grasemann, B.

2012-04-01

Our study compares kinematical and mechanical techniques for the palinspastic reconstruction of folded cross-sections in collision orogens. The studied area and the reconstructed NE-SW-trending, 55.5 km long cross-section is located in the High Folded Zone of the Zagros fold-and-thrust belt in the Kurdistan Region of Iraq. The present-day geometry of the cross-section has been constructed from field, as well as remote sensing data. In a first step, the structures and the stratigraphy are simplified and summarized in eight units trying to identify the main geometric and mechanical parameters. In a second step, the shortening is kinematically estimated using the dip-domain method to 11%-15%. Then the same cross-section is used in a numerical finite-element model to perform dynamical unfolding simulations taking various rheological parameters into account. The main factor allowing for an efficient dynamic unfolding is the presence of interfacial slip conditions between the mechanically strong units. Other factors, such as Newtonian vs. power-law viscous rheology or the presence of a basement affect the numerical simulations much less strongly. If interfacial slip is accounted for, fold amplitudes are reduced efficiently during the dynamical unfolding simulations, while welded layer interfaces lead to unrealistic shortening estimates. It is suggested that interfacial slip and decoupling of the deformation along detachment horizons is an important mechanical parameter that controlled the folding processes in the Zagros High Folded Zone.
Cross-cultural validation and psychometric testing of the Norwegian version of the TeamSTEPPS® teamwork perceptions questionnaire.

PubMed

Ballangrud, Randi; Husebø, Sissel Eikeland; Hall-Lord, Marie Louise

2017-12-02

Teamwork is an integrated part of today's specialized and complex healthcare and essential to patient safety, and is considered as a core competency to improve twenty-first century healthcare. Teamwork measurements and evaluations show promising results to promote good team performance, and are recommended for identifying areas for improvement. The validated TeamSTEPPS® Teamwork Perception Questionnaire (T-TPQ) was found suitable for cross-cultural validation and testing in a Norwegian context. T-TPQ is a self-report survey that examines five dimensions of perception of teamwork within healthcare settings. The aim of the study was to translate and cross-validate the T-TPQ into Norwegian, and test the questionnaire for psychometric properties among healthcare personnel. The T-TPQ was translated and adapted to a Norwegian context according to a model of a back-translation process. A total of 247 healthcare personnel representing different professionals and hospital settings responded to the questionnaire. A confirmatory factor analysis was carried out to test the factor structure. Cronbach's alpha was used to establish internal consistency, and an Intraclass Correlation Coefficient was used to assess the test - retest reliability. A confirmatory factor analysis showed an acceptable fitting model (χ 2 (df) 969.46 (546), p < 0.001, Root Mean Square Error of Approximation (RMSEA) = 0.056, Tucker-Lewis Index (TLI) = 0.88, Comparative fit index (CFI) = 0.89, which indicates that each set of the items that was supposed to accompany each teamwork dimension clearly represents that specific construct. The Cronbach's alpha demonstrated acceptable values on the five subscales (0.786-0.844), and test-retest showed a reliability parameter, with Intraclass Correlation Coefficient scores from 0.672 to 0.852. The Norwegian version of T-TPQ was considered to be acceptable regarding the validity and reliability for measuring Norwegian individual healthcare personnel's perception of group level teamwork within their unit. However, it needs to be further tested, preferably in a larger sample and in different clinical settings.

A history of concussions is associated with symptoms of common mental disorders in former male professional athletes across a range of sports.

PubMed

Gouttebarge, Vincent; Aoki, Haruhito; Lambert, Michael; Stewart, William; Kerkhoffs, Gino

2017-11-01

Recent reports suggest that exposure to repetitive concussions in sports is associated with an increased risk of symptoms of distress, anxiety and depression, sleep disturbance or substance abuse/dependence (typically referred as symptoms of common mental disorders[CMD]) and of later development of neurodegenerative disease, in particular chronic traumatic encephalopathy (CTE). The primary aim of this study was to explore the relationship between sports career-related concussions and the subsequent occurrence of symptoms of CMD among former male professional athletes retired from football (soccer), ice hockey and rugby (union). Cross-sectional analyses were performed on baseline electronic questionnaires from three prospective cohort studies among former male professional athletes retired from football (soccer), ice hockey and rugby (union). The number of confirmed concussions was examined through a single question, while symptoms of distress, anxiety and depression, sleep disturbance and adverse alcohol use were assessed using validated questionnaires. From 1,957 former professional athletes contacted, a total of 576 (29%) completed the questionnaire. Of these, 23% had not incurred a concussion during their career, 34% had two or three, 18% four or five, and 11% six or more concussions. The number of sports career-related concussions was a predictor for all outcome measures (β = 0.072-0.109; P ≤ 0.040). Specifically, former professional athletes who reported a history of four or five concussions were approximately 1.5 times more likely to report symptoms of CMD, rising to a two- to five-fold increase in those reporting a history of six or more sports career-related concussions. These data demonstrate an association between exposure to sports concussion and subsequent risk of symptoms of CMD in former professional athletes across a range of contact sports. Further work to explore the association between sports concussion and symptoms of CMD is required; in the meanwhile, strategies for effective risk reduction and improved management appear indicated.
Nearly Seamless Vacuum-Insulated Boxes

NASA Technical Reports Server (NTRS)

Stepanian, Christopher J.; Ou, Danny; Hu, Xiangjun

2010-01-01

A design concept, and a fabrication process that would implement the design concept, have been proposed for nearly seamless vacuum-insulated boxes that could be the main structural components of a variety of controlled-temperature containers, including common household refrigerators and insulating containers for shipping foods. In a typical case, a vacuum-insulated box would be shaped like a rectangular parallelepiped conventional refrigerator box having five fully closed sides and a hinged door on the sixth side. Although it is possible to construct the five-closed-side portion of the box as an assembly of five unitary vacuum-insulated panels, it is not desirable to do so because the relatively high thermal conductances of the seams between the panels would contribute significant amounts of heat leakage, relative to the leakage through the panels themselves. In contrast, the proposal would make it possible to reduce heat leakage by constructing the five-closed-side portion of the box plus the stationary portion (if any) of the sixth side as a single, seamless unit; the only remaining seam would be the edge seal around the door. The basic cross-sectional configuration of each side of a vacuum-insulated box according to the proposal would be that of a conventional vacuum-insulated panel: a low-density, porous core material filling a partially evacuated space between face sheets. However, neither the face sheets nor the core would be conventional. The face sheets would be opposite sides of a vacuum bag. The core material would be a flexible polymer-modified silica aerogel of the type described in Silica/Polymer and Silica/Polymer/Fiber Composite Aero - gels (MSC-23736) in this issue of NASA Tech Briefs. As noted in that article, the stiffness of this core material against compression is greater than that of prior aerogels. This is an important advantage because it translates to greater retention of thickness and, hence, of insulation performance when pressure is applied across the thickness, in particular, when the space between the face sheets is evacuated, causing the core material to be squeezed between the face sheets by atmospheric pressure. Fabrication of a typical vacuum-insulated box according to the proposal would begin with fabrication of a cross-shaped polymer-modified aerogel blanket. The dimensions of the cross would be chosen so that (1) the central rectangular portion of the cross would form the core for the back of the box and (2) the arms of the cross could be folded 90 from the back plane to form the cores of the adjacent four sides of the box. Optionally, the blanket could include tabs for joining the folded sides of the blanket along mating edges and tabs that could serve as hinges for the door. Vacuum bags in the form of similar five-sided boxes would be made of a suitable polymeric film, one bag to fit the outer core surface, the other to fit the inner core surface. By use of commercially available film-sealing equipment, these box-shaped bags would be seamed together to form a single vacuum bag encasing the box-shaped core. Also, a one-way valve would be sealed to the bag. Through this valve, the interior of the bag would be evacuated to a pressure between 1 and 10 torr (approximately between 0.13 and 1.3 kPa). The polymer-modified aerogel core material is known to perform well as a thermal insulator in such a partial vacuum.
Determination of scattering properties and damage thresholds in tissue using ultrafast laser ablation

NASA Astrophysics Data System (ADS)

Martin, Chris; Ben-Yakar, Adela

2016-11-01

Ultrafast laser surgery of tissue requires precise knowledge of the tissue's optical properties to control the extent of subsurface ablation. Here, we present a method to determine the scattering lengths, ℓs, and fluence thresholds, Fth, in multilayered and turbid tissue by finding the input energies required to initiate ablation at various depths in each tissue layer. We validated the method using tissue-mimicking phantoms and applied it to porcine vocal folds, which consist of an epithelial (ep) layer and a superficial lamina propia (SLP) layer. Across five vocal fold samples, we found ℓ=51.0±3.9 μm, F=1.78±0.08 J/cm2, ℓ=26.5±1.6 μm, and F=1.14±0.12 J/cm2. Our method can enable personalized determination of tissue optical properties in a clinical setting, leading to less patient-to-patient variability and more favorable outcomes in operations, such as femto-LASIK surgery.
A QSAR Model for Thyroperoxidase Inhibition and Screening ...

EPA Pesticide Factsheets

Thyroid hormones (THs) are critical modulators of a wide range of biological processes from neurodevelopment to metabolism. Well regulated levels of THs are critical during development and even moderate changes in maternal or fetal TH levels produce irreversible neurological deficits in children. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs. Inhibition of TPO by xenobiotics leads to decreased TH synthesis and, depending on the degree of synthesis inhibition, may result in adverse developmental outcomes. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to screen the ToxCast Phase I and II chemicals. In the present study, we used the results from the AUR-TPO screening to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition in Leadscope®. The training set consisted of 898 discrete organic chemicals: 134 positive and 764 negative for TPO inhibition. A 10 times two-fold 50% cross-validation of the model was performed, yielding a balanced accuracy of 78.7% within its defined applicability domain. More recently, an additional ~800 chemicals from the US EPA Endocrine Disruption Screening Program (EDSP21) were screened using the AUR-TPO assay. This data was used for external validation of the QSAR model, demonstrating a balanced accuracy of 85.7% within its applicability domain. Overall, the cross- and external validations indicate a model with a high predictiv
Budget Online Learning Algorithm for Least Squares SVM.

PubMed

Jian, Ling; Shen, Shuqian; Li, Jundong; Liang, Xijun; Li, Lei

2017-09-01

Batch-mode least squares support vector machine (LSSVM) is often associated with unbounded number of support vectors (SVs'), making it unsuitable for applications involving large-scale streaming data. Limited-scale LSSVM, which allows efficient updating, seems to be a good solution to tackle this issue. In this paper, to train the limited-scale LSSVM dynamically, we present a budget online LSSVM (BOLSSVM) algorithm. Methodologically, by setting a fixed budget for SVs', we are able to update the LSSVM model according to the updated SVs' set dynamically without retraining from scratch. In particular, when a new small chunk of SVs' substitute for the old ones, the proposed algorithm employs a low rank correction technology and the Sherman-Morrison-Woodbury formula to compute the inverse of saddle point matrix derived from the LSSVM's Karush-Kuhn-Tucker (KKT) system, which, in turn, updates the LSSVM model efficiently. In this way, the proposed BOLSSVM algorithm is especially useful for online prediction tasks. Another merit of the proposed BOLSSVM is that it can be used for k -fold cross validation. Specifically, compared with batch-mode learning methods, the computational complexity of the proposed BOLSSVM method is significantly reduced from O(n 4 ) to O(n 3 ) for leave-one-out cross validation with n training samples. The experimental results of classification and regression on benchmark data sets and real-world applications show the validity and effectiveness of the proposed BOLSSVM algorithm.
Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

PubMed

Li, Xiang; Xu, Youjun; Lai, Luhua; Pei, Jianfeng

2018-05-30

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .
O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique.

PubMed

Jia, Cangzhi; Zuo, Yun; Zou, Quan; Hancock, John

2018-02-06

Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves multiple molecular and cellular processes. Recent studies have suggested that abnormal O-G1cNAcylation causes many diseases, such as cancer and various neurodegenerative diseases. With the available protein O-G1cNAcylation sites experimentally verified, it is highly desired to develop automated methods to rapidly and effectively identify O-G1cNAcylation sites. Although some computational methods have been proposed, their performance has been unsatisfactory, particularly in terms of prediction sensitivity. In this study, we developed an ensemble model O-GlcNAcPRED-II to identify potential O-G1cNAcylation sites. A K-means principal component analysis oversampling technique (KPCA) and fuzzy undersampling method (FUS) were first proposed and incorporated to reduce the proportion of the original positive and negative training samples. Then, rotation forest, a type of classifier-integrated system, was adopted to divide the eight types of feature space into several subsets using four sub-classifiers: random forest, k-nearest neighbour, naive Bayesian and support vector machine. We observed that O-GlcNAcPRED-II achieved a sensitivity of 81.05%, specificity of 95.91%, accuracy of 91.43% and Matthew's correlation coefficient of 0.7928 for five-fold cross-validation run 10 times. Additionally, the results obtained by O-GlcNAcPRED-II on two independent datasets also indicated that the proposed predictor outperformed five published prediction tools. http://121.42.167.206/OGlcPred/. cangzhijia@dlmu.edu.cn or zouquan@nclab.net. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Population Pharmacokinetics of Fentanyl in the Critically Ill

PubMed Central

Choi, Leena; Ferrell, Benjamin A; Vasilevskis, Eduard E; Pandharipande, Pratik P; Heltsley, Rebecca; Ely, E Wesley; Stein, C Michael; Girard, Timothy D

2016-01-01

Objective To characterize fentanyl population pharmacokinetics in patients with critical illness and identify patient characteristics associated with altered fentanyl concentrations. Design Prospective cohort study. Setting Medical and surgical ICUs in a large tertiary care hospital in the United States. Patients Patients with acute respiratory failure and/or shock who received fentanyl during the first five days of their ICU stay. Measurements and Main Results We collected clinical and hourly drug administration data and measured fentanyl concentrations in plasma collected once daily for up to five days after enrollment. Among 337 patients, the mean duration of infusion was 58 hours at a median rate of 100 µg/hr. Using a nonlinear mixed-effects model implemented by NONMEM, we found fentanyl pharmacokinetics were best described by a two-compartment model in which weight, severe liver disease, and congestive heart failure most affected fentanyl concentrations. For a patient population with a mean weight of 92 kg and no history of severe liver disease or congestive heart failure, the final model, which performed well in repeated 10-fold cross-validation, estimated total clearance (CL), intercompartmental clearance (Q), and volumes of distribution for the central (V1) and peripheral compartments (V2) to be 35 (95% confidence interval: 32 to 39) L/hr, 55 (42 to 68) L/hr, 203 (140 to 266) L, and 523 (428 to 618) L, respectively. Severity of illness was marginally associated with fentanyl pharmacokinetics but did not improve the model fit after liver and heart disease were included. Conclusions In this study, fentanyl pharmacokinetics during critical illness were strongly influenced by severe liver disease, congestive heart failure, and weight, factors that should be considered when dosing fentanyl in the ICU. Future studies are needed to determine if data-driven fentanyl dosing algorithms can improve outcomes for ICU patients. PMID:26491862
Simultaneous Detection and Identification of Aspergillus and Mucorales Species in Tissues Collected from Patients with Fungal Rhinosinusitis▿

PubMed Central

Zhao, Zuotao; Li, Lili; Wan, Zhe; Chen, Wei; Liu, Honggang; Li, Ruoyu

2011-01-01

Rapid detection and differentiation of Aspergillus and Mucorales species in fungal rhinosinusitis diagnosis are desirable, since the clinical management and prognosis associated with the two taxa are fundamentally different. We describe an assay based on a combination of broad-range PCR amplification and reverse line blot hybridization (PCR/RLB) to detect and differentiate the pathogens causing fungal rhinosinusitis, which include five Aspergillus species (A. fumigatus, A. flavus, A. niger, A. terreus, and A. nidulans) and seven Mucorales species (Mucor heimalis, Mucor racemosus, Mucor cercinelloidea, Rhizopus arrhizus, Rhizopus microsporus, Rhizomucor pusillus, and Absidia corymbifera). The assay was validated with 98 well-characterized clinical isolates and 41 clinical tissue specimens. PCR/RLB showed high sensitivity and specificity, with 100% correct identifications of 98 clinical isolates and no cross-hybridization between the species-specific probes. Results for five control isolates, Candida albicans, Fusarium solani, Scedosporium apiospermum, Penicillium marneffei, and Exophiala verrucosa, were negative as judged by PCR/RLB. The analytical sensitivity of PCR/RLB was found to be 1.8 × 10−3 ng/μl by 10-fold serial dilution of Aspergillus genomic DNA. The assay identified 35 of 41 (85.4%) clinical specimens, exhibiting a higher sensitivity than fungal culture (22 of 41; 53.7%) and direct sequencing (18 of 41; 43.9%). PCR/RLB similarly showed high specificity, with correct identification 16 of 18 specimens detected by internal transcribed spacer (ITS) sequencing and 16 of 22 detected by fungal culture, but it also has the additional advantage of being able to detect mixed infection in a single clinical specimen. The PCR/RLB assay thus provides a rapid and reliable option for laboratory diagnosis of fungal rhinosinusitis. PMID:21325541
Simultaneous detection and identification of Aspergillus and mucorales species in tissues collected from patients with fungal rhinosinusitis.

PubMed

Zhao, Zuotao; Li, Lili; Wan, Zhe; Chen, Wei; Liu, Honggang; Li, Ruoyu

2011-04-01

Rapid detection and differentiation of Aspergillus and Mucorales species in fungal rhinosinusitis diagnosis are desirable, since the clinical management and prognosis associated with the two taxa are fundamentally different. We describe an assay based on a combination of broad-range PCR amplification and reverse line blot hybridization (PCR/RLB) to detect and differentiate the pathogens causing fungal rhinosinusitis, which include five Aspergillus species (A. fumigatus, A. flavus, A. niger, A. terreus, and A. nidulans) and seven Mucorales species (Mucor heimalis, Mucor racemosus, Mucor cercinelloidea, Rhizopus arrhizus, Rhizopus microsporus, Rhizomucor pusillus, and Absidia corymbifera). The assay was validated with 98 well-characterized clinical isolates and 41 clinical tissue specimens. PCR/RLB showed high sensitivity and specificity, with 100% correct identifications of 98 clinical isolates and no cross-hybridization between the species-specific probes. Results for five control isolates, Candida albicans, Fusarium solani, Scedosporium apiospermum, Penicillium marneffei, and Exophiala verrucosa, were negative as judged by PCR/RLB. The analytical sensitivity of PCR/RLB was found to be 1.8 × 10(-3) ng/μl by 10-fold serial dilution of Aspergillus genomic DNA. The assay identified 35 of 41 (85.4%) clinical specimens, exhibiting a higher sensitivity than fungal culture (22 of 41; 53.7%) and direct sequencing (18 of 41; 43.9%). PCR/RLB similarly showed high specificity, with correct identification 16 of 18 specimens detected by internal transcribed spacer (ITS) sequencing and 16 of 22 detected by fungal culture, but it also has the additional advantage of being able to detect mixed infection in a single clinical specimen. The PCR/RLB assay thus provides a rapid and reliable option for laboratory diagnosis of fungal rhinosinusitis.
SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less
Joint optic disc and cup boundary extraction from monocular fundus images.

PubMed

Chakravarty, Arunava; Sivaswamy, Jayanthi

2017-08-01

Accurate segmentation of optic disc and cup from monocular color fundus images plays a significant role in the screening and diagnosis of glaucoma. Though optic cup is characterized by the drop in depth from the disc boundary, most existing methods segment the two structures separately and rely only on color and vessel kink based cues due to the lack of explicit depth information in color fundus images. We propose a novel boundary-based Conditional Random Field formulation that extracts both the optic disc and cup boundaries in a single optimization step. In addition to the color gradients, the proposed method explicitly models the depth which is estimated from the fundus image itself using a coupled, sparse dictionary trained on a set of image-depth map (derived from Optical Coherence Tomography) pairs. The estimated depth achieved a correlation coefficient of 0.80 with respect to the ground truth. The proposed segmentation method outperformed several state-of-the-art methods on five public datasets. The average dice coefficient was in the range of 0.87-0.97 for disc segmentation across three datasets and 0.83 for cup segmentation on the DRISHTI-GS1 test set. The method achieved a good glaucoma classification performance with an average AUC of 0.85 for five fold cross-validation on RIM-ONE v2. We propose a method to jointly segment the optic disc and cup boundaries by modeling the drop in depth between the two structures. Since our method requires a single fundus image per eye during testing it can be employed in the large-scale screening of glaucoma where expensive 3D imaging is unavailable. Copyright © 2017 Elsevier B.V. All rights reserved.
Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

PubMed Central

Motwani, Manish; Dey, Damini; Berman, Daniel S.; Germano, Guido; Achenbach, Stephan; Al-Mallah, Mouaz H.; Andreini, Daniele; Budoff, Matthew J.; Cademartiri, Filippo; Callister, Tracy Q.; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J.W.; Cury, Ricardo C.; Delago, Augustin; Gomez, Millie; Gransar, Heidi; Hadamitzky, Martin; Hausleiter, Joerg; Hindoyan, Niree; Feuchtner, Gudrun; Kaufmann, Philipp A.; Kim, Yong-Jin; Leipsic, Jonathon; Lin, Fay Y.; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert; Rubinshtein, Ronen; Shaw, Leslee J.; Stehli, Julia; Villines, Todd C.; Dunning, Allison; Min, James K.; Slomka, Piotr J.

2017-01-01

Aims Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. Methods and results The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). Conclusions Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone. PMID:27252451
Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.

PubMed

Motwani, Manish; Dey, Damini; Berman, Daniel S; Germano, Guido; Achenbach, Stephan; Al-Mallah, Mouaz H; Andreini, Daniele; Budoff, Matthew J; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J W; Cury, Ricardo C; Delago, Augustin; Gomez, Millie; Gransar, Heidi; Hadamitzky, Martin; Hausleiter, Joerg; Hindoyan, Niree; Feuchtner, Gudrun; Kaufmann, Philipp A; Kim, Yong-Jin; Leipsic, Jonathon; Lin, Fay Y; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert; Rubinshtein, Ronen; Shaw, Leslee J; Stehli, Julia; Villines, Todd C; Dunning, Allison; Min, James K; Slomka, Piotr J

2017-02-14

Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.
Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

PubMed Central

2013-01-01

Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313
COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

PubMed

Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

2016-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.
COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

PubMed Central

REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

2015-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170
An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.

PubMed

Zhou, Yan; Cao, Hui

2013-01-01

We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.
Assessment of local friction in protein folding dynamics using a helix cross-linker.

PubMed

Markiewicz, Beatrice N; Jo, Hyunil; Culik, Robert M; DeGrado, William F; Gai, Feng

2013-11-27

Internal friction arising from local steric hindrance and/or the excluded volume effect plays an important role in controlling not only the dynamics of protein folding but also conformational transitions occurring within the native state potential well. However, experimental assessment of such local friction is difficult because it does not manifest itself as an independent experimental observable. Herein, we demonstrate, using the miniprotein trp-cage as a testbed, that it is possible to selectively increase the local mass density in a protein and hence the magnitude of local friction, thus making its effect directly measurable via folding kinetic studies. Specifically, we show that when a helix cross-linker, m-xylene, is placed near the most congested region of the trp-cage it leads to a significant decrease in both the folding rate (by a factor of 3.8) and unfolding rate (by a factor of 2.5 at 35 °C) but has little effect on protein stability. Thus, these results, in conjunction with those obtained with another cross-linked trp-cage and two uncross-linked variants, demonstrate the feasibility of using a nonperturbing cross-linker to help quantify the effect of internal friction. In addition, we estimate that a m-xylene cross-linker could lead to an increase in the roughness of the folding energy landscape by as much as 0.4-1.0k(B)T.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Monaghan, P; Shneor, R; Subedi, R

The five-fold differential cross section for the 12C(e,e'p)11B reaction was determined over a missing momentum range of 200-400 MeV/c, in a kinematics regime with Bjorken x > 1 and Q2 = 2.0 (GeV/c)2. A comparison of the results and theoretical models and previous lower missing momentum data is shown. The theoretical calculations agree well with the data up to a missing momentum value of 325 MeV/c and then diverge for larger missing momenta. The extracted distorted momentum distribution is shown to be consistent with previous data and extends the range of available data up to 400 MeV/c.

Mexican sign language recognition using normalized moments and artificial neural networks

NASA Astrophysics Data System (ADS)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.
Medical application of artificial immune recognition system (AIRS): diagnosis of atherosclerosis from carotid artery Doppler signals.

PubMed

Latifoğlu, Fatma; Kodaz, Halife; Kara, Sadik; Güneş, Salih

2007-08-01

This study was conducted to distinguish between atherosclerosis and healthy subjects. Hence, we have employed the maximum envelope of the carotid artery Doppler sonograms derived from Fast Fourier Transformation-Welch method and Artificial Immune Recognition System (AIRS). The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and sometimes causes false diagnosis. Our technique gets around this problem using AIRS to decide and assist the physician to make the final judgment in confidence. AIRS has reached 99.29% classification accuracy using 10-fold cross validation. Results show that the proposed method classified Doppler signals successfully.
Large outbreak of Salmonella enteritidis PT8 in Portsmouth, UK, associated with a restaurant.

PubMed

Severi, E; Booth, L; Johnson, S; Cleary, P; Rimington, M; Saunders, D; Cockcroft, P; Ihekweazu, C

2012-10-01

Seventy-five individuals with Salmonella infection were identified in the Portsmouth area during August and September 2009, predominantly Salmonella Enteritidis phage type 8. Five patients were admitted to hospital. A case-case comparison study showed that a local restaurant was the most likely source of the infection with a risk of illness among its customers 25-fold higher than that of those who did not attend the restaurant. A case-control study conducted to investigate specific risk factors for infection at the restaurant showed that eating salad was associated with a threefold increase in probability of illness. Changing from using ready washed lettuces to lettuces requiring washing and not adhering strictly to the 48 hours exclusion policy for food handlers with diarrhoea were likely to have contributed to the initiation and propagation of this outbreak. Possibilities for cross-contamination and environmental contamination were identified in the restaurant.
Nursing Home Administrator Quality Improvement Self-Efficacy Scale.

PubMed

Siegel, Elena O; Zisberg, Anna; Bakerjian, Debra; Zysberg, Leehu

Nursing home (NH) quality improvement (QI) is challenging. The critical role of NH leaders in successful QI is well established; however, current options for assessing the QI capabilities of leaders such as the licensed NH administrator are limited. This article presents the development and preliminary validation of an instrument to measure NH administrator self-efficacy in QI. We used a mixed-methods cross-sectional design to develop and test the measure. For item generation, 39 NH leaders participated in qualitative interviews. Item reduction and content validity were established with a sample of eight subject matter experts. A random sample of 211 administrators from NHs with the lowest and highest Centers for Medicare and Medicaid Services Five-Star Quality ratings completed the measure. We conducted exploratory and confirmatory factor analyses and tested the measure for internal reliability and convergent, discriminant, and known group validity. The final measure included five subscales and 32 items. Confirmatory factor analysis reaffirmed the factorial structure with good fit indices. The new measure's subscales correlated with valid measures of self-efficacy and locus of control, supporting the measure's convergent and discriminant validity. Significant differences in most of the subscales were found between the objective (Centers for Medicare and Medicaid Services Five-Star Quality rating) and subjective (Self-Rated Facility QI Index) quality outcomes, supporting the measure's known group validity. The instrument has usefulness to both NH organizations and individual NH administrators as a diagnostic tool to identify administrators with higher/lower chances of successfully implementing QI. Organizations and individuals can use this diagnostic to identify the administrator's professional development needs for QI, in general, and specific to the instrument's five subscales, informing directions for in-house training, mentoring, and outside professional development. Attending to NH administrators' QI professional development needs prior to implementing QI holds promise to enhance the chances for successful implementation of QI, which is urgently needed in many NHs.
Simultaneous dual modality optical and MR imaging of mouse dorsal skin-fold window chamber

NASA Astrophysics Data System (ADS)

Salek, Mir Farrokh; Pagel, Mark D.; Gmitro, Arthur F.

2011-02-01

Optical imaging and MRI have both been used extensively to study tumor microenvironment. The two imaging modalities are complementary and can be used to cross-validate one another for specific measurements. We have developed a modular platform that is capable of doing optical microscopy inside an MRI instrument. To do this, an optical relay system transfers the image to outside of the MR bore to a commercial grade CCD camera. This enables simultaneous optical and MR imaging of the same tissue and thus creates the ideal situation for comparative or complementary studies using both modalities. Initial experiments have been done using GFP labeled prostate cancer cells implanted in mouse dorsal skin fold window chamber. Vascular hemodynamics and vascular permeability were studied using our imaging system. Towards this goal, we developed a dual MR-Optical contrast agent by labeling BSA with both Gd-DTPA and Alexa Fluor. Overall system design and results of these preliminary vascular studies are presented.
Implicit leadership theories in applied settings: factor structure, generalizability, and stability over time.

PubMed

Epitropaki, Olga; Martin, Robin

2004-04-01

The present empirical investigation had a 3-fold purpose: (a) to cross-validate L. R. Offermann, J. K. Kennedy, and P. W. Wirtz's (1994) scale of Implicit Leadership Theories (ILTs) in several organizational settings and to further provide a shorter scale of ILTs in organizations; (b) to assess the generalizability of ILTs across different employee groups, and (c) to evaluate ILTs' change over time. Two independent samples were used for the scale validation (N1 = 500 and N2 = 439). A 6-factor structure (Sensitivity, Intelligence, Dedication, Dynamism, Tyranny, and Masculinity) was found to most accurately represent ELTs in organizational settings. Regarding the generalizability of ILTs, although the 6-factor structure was consistent across different employee groups, there was only partial support for total factorial invariance. Finally, evaluation of gamma, beta, and alpha change provided support for ILTs' stability over time.
Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

PubMed

Kusumoto, Dai; Lachmann, Mark; Kunihiro, Takeshi; Yuasa, Shinsuke; Kishino, Yoshikazu; Kimura, Mai; Katsuki, Toshiomi; Itoh, Shogo; Seki, Tomohisa; Fukuda, Keiichi

2018-06-05

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
Validity of the impact on participation and autonomy questionnaire: a comparison between two countries.

PubMed

Kersten, Paula; Cardol, Mieke; George, Steve; Ward, Christopher; Sibley, Andrew; White, Barney

2007-10-15

To evaluate the cross-cultural validity of the five subscales of the Impact on Participation and Autonomy (IPA) measure and the full 31-item scale. Data from two validation studies (Dutch and English) were pooled (n = 106). Participants (aged 18-75), known to rehabilitation services or GP practices, had conditions ranging from minor ailments to significant disability. Validity of the five subscales and the total scale was examined using Rasch analysis (Partial Credit Model). P values smaller than 0.01 were employed to allow for multiple testing. A number of items in all the subscales except 'Outdoor Autonomy' needed rescoring. One 'Indoor Autonomy' item showed uniform DIF by country and was split by country. One 'Work and Education' item displayed uniform and non-uniform DIF by gender. All the subscales fitted the Rasch model and were invariant across country. A 30-item IPA also fitted the Rasch model. The IPA subscales and a 30-item scale are invariant across the two cultures and gender. The IPA can be used validly to assess participation and autonomy in these populations. Further analyses are required to examine whether the IPA is invariant across differing levels of disability and other disease groups not included in this study.
RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.

PubMed

An, Ji-Yong; You, Zhu-Hong; Meng, Fan-Rong; Xu, Shu-Juan; Wang, Yin

2016-05-18

Protein-Protein Interactions (PPIs) play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed a freely available web server called RVMAB-PPI in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/ppi_ab/.
Development and Validation of the Elder Learning Barriers Scale Among Older Chinese Adults.

PubMed

Wang, Renfeng; De Donder, Liesbeth; De Backer, Free; He, Tao; Van Regenmortel, Sofie; Li, Shihua; Lombaerts, Koen

2017-12-01

This study describes the development and validation of the Elder Learning Barriers (ELB) scale, which seeks to identify the obstacles that affect the level of educational participation of older adults. The process of item pool design and scale development is presented, as well as the testing and scale refinement procedure. The data were collected from a sample of 579 older Chinese adults (aged over 55) in the Xi'an region of China. After randomly splitting the sample for cross-validation purposes, the construct validity of the ELB scale was confirmed containing five dimensions: dispositional, informational, physical, situational, and institutional barriers. Furthermore, developmental differences in factor structure have been examined among older age groups. The results indicated that the scale demonstrated good reliability and validity. We conclude in general that the ELB scale appears to be a valuable instrument for examining the learning barriers that older Chinese citizens experience for participating in organized educational activities.
Psychological collectivism: a measurement validation and linkage to group member performance.

PubMed

Jackson, Christine L; Colquitt, Jason A; Wesson, Michael J; Zapata-Phelan, Cindy P

2006-07-01

The 3 studies presented here introduce a new measure of the individual-difference form of collectivism. Psychological collectivism is conceptualized as a multidimensional construct with the following 5 facets: preference for in-groups, reliance on in-groups, concern for in-groups, acceptance of in-group norms, and prioritization of in-group goals. Study 1 developed and tested the new measure in a sample of consultants. Study 2 cross-validated the measure using an alumni sample of a Southeastern university, assessing its convergent validity with other collectivism measures. Study 3 linked scores on the measure to 4 dimensions of group member performance (task performance, citizenship behavior, counterproductive behavior, and withdrawal behavior) in a computer software firm and assessed discriminant validity using the Big Five. The results of the studies support the construct validity of the measure and illustrate the potential value of collectivism as a predictor of group member performance. ((c) 2006 APA, all rights reserved).
Numerical Study of Aeroacoustic Sound on Performance of Bladeless Fan

NASA Astrophysics Data System (ADS)

Jafari, Mohammad; Sojoudi, Atta; Hafezisefat, Parinaz

2017-03-01

Aeroacoustic performance of fans is essential due to their widespread application. Therefore, the original aim of this paper is to evaluate the generated noise owing to different geometric parameters. In current study, effect of five geometric parameters was investigated on well performance of a Bladeless fan. Airflow through this fan was analyzed simulating a Bladeless fan within a 2 m×2 m×4 m room. Analysis of the flow field inside the fan and evaluating its performance were obtained by solving conservations of mass and momentum equations for aerodynamic investigations and FW-H noise equations for aeroacoustic analysis. In order to design Bladeless fan Eppler 473 airfoil profile was used as the cross section of this fan. Five distinct parameters, namely height of cross section of the fan, outlet angle of the flow relative to the fan axis, thickness of airflow outlet slit, hydraulic diameter and aspect ratio for circular and quadratic cross sections were considered. Validating acoustic code results, we compared numerical solution of FW-H noise equations for NACA0012 with experimental results. FW-H model was selected to predict the noise generated by the Bladeless fan as the numerical results indicated a good agreement with experimental ones for NACA0012. To validate 3-D numerical results, the experimental results of a round jet showed good agreement with those simulation data. In order to indicate the effect of each mentioned parameter on the fan performance, SPL and OASPL diagrams were illustrated.
Non-detergent sulphobetaines: a new class of molecules that facilitate in vitro protein renaturation.

PubMed

Goldberg, M E; Expert-Bezançon, N; Vuillard, L; Rabilloud, T

1996-01-01

Attempts to renature proteins often yield aggregates rather than native protein. To minimize aggregation, low protein concentrations and/or solubilizing agents are used. Here, we test new solubilizing molecules, non-detergent sulphobetaines, to improve the renaturation of two very different enzymes, hen egg white lysozyme and bacterial beta-D-galactosidase. The renaturation was conducted in the presence of five different sulphobetaines and the yield of active enzyme was measured. The five sulphobetaines improved the yield of native lysozyme up to 12-fold. Some sulphobetaines improved the yield of galactosidase up to 80-fold, but one reduced it 100-fold. Non-detergent sulphobetaines strongly affect the balance between aggregation and folding. Their effect depends on their structure and on their interactions with folding intermediates. These results should serve as a basis for designing more efficient sulphobetaines; for designing improved renaturation protocols using existing sulphobetaines; and for characterizing folding intermediates that interact with sulphobetaines.
Self-efficacy in weight management.

PubMed

Clark, M M; Abrams, D B; Niaura, R S; Eaton, C A; Rossi, J S

1991-10-01

Self-efficacy is an important mediating mechanism in advancing understanding of the treatment of obesity. This study developed and validated the Weight Efficacy Life-Style Questionnaire (WEL), improving on previous studies by the use of clinical populations, cross-validation of the initial factor analysis, exploration of the best fitting theoretical model of self-efficacy, and examination of change in treatment. The resulting 20-item WEL consists of five situational factors: Negative Emotions, Availability, Social Pressure, Physical Discomfort, and Positive Activities. A hierarchical model was found to provide the best fit to the data. Results from two separate clinical treatment studies (total N = 382) show that the WEL is sensitive to changes in global scores as well as to a subset of the five situational factor scores. Treatment programs may be incomplete if they change only a subset of the situational dimensions of self-efficacy. Theoretical and clinical implications are discussed.
Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study.

PubMed

Zammit, Andrea R; Hall, Charles B; Lipton, Richard B; Katz, Mindy J; Muniz-Terrera, Graciela

2018-05-01

The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup's characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer's disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511-523).
Machine learning for the assessment of Alzheimer's disease through DTI

NASA Astrophysics Data System (ADS)

Lella, Eufemia; Amoroso, Nicola; Bellotti, Roberto; Diacono, Domenico; La Rocca, Marianna; Maggipinto, Tommaso; Monaco, Alfonso; Tangaro, Sabina

2017-09-01

Digital imaging techniques have found several medical applications in the development of computer aided detection systems, especially in neuroimaging. Recent advances in Diffusion Tensor Imaging (DTI) aim to discover biological markers for the early diagnosis of Alzheimer's disease (AD), one of the most widespread neurodegenerative disorders. We explore here how different supervised classification models provide a robust support to the diagnosis of AD patients. We use DTI measures, assessing the structural integrity of white matter (WM) fiber tracts, to reveal patterns of disrupted brain connectivity. In particular, we provide a voxel-wise measure of fractional anisotropy (FA) and mean diffusivity (MD), thus identifying the regions of the brain mostly affected by neurodegeneration, and then computing intensity features to feed supervised classification algorithms. In particular, we evaluate the accuracy of discrimination of AD patients from healthy controls (HC) with a dataset of 80 subjects (40 HC, 40 AD), from the Alzheimer's Disease Neurodegenerative Initiative (ADNI). In this study, we compare three state-of-the-art classification models: Random Forests, Naive Bayes and Support Vector Machines (SVMs). We use a repeated five-fold cross validation framework with nested feature selection to perform a fair comparison between these algorithms and evaluate the information content they provide. Results show that AD patterns are well localized within the brain, thus DTI features can support the AD diagnosis.
Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: an application to upper extremity amputation.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Schoen, Marco P; Subbaram Naidu, D; Urfer, Alex; Chiu, Steve

2013-11-01

Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed. The sEMG signals are pre-processed using five different filters: Butterworth, Chebychev Type II, Exponential, Half-Gaussian and Wavelet transforms. Dynamic models are extracted from the acquired data using Nonlinear Wiener Hammerstein (NLWH) models and Spectral Analysis Frequency Dependent Resolution (SPAFDR) models based system identification techniques. A detailed comparison is provided for the proposed filters and models using 18 healthy subjects. Wavelet transforms give higher mean correlation of 72.6 ± 1.7 (mean ± SD) and 70.4 ± 1.5 (mean ± SD) for NLWH and SPAFDR models, respectively, when compared to the other filters used in this work. Experimental verification of the fusion based hybrid models with wavelet transform shows a 96% mean correlation and 3.9% mean relative error with a standard deviation of ± 1.3 and ± 0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects' k-fold cross validation data. © 2013 Elsevier Ltd. All rights reserved.
CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

PubMed Central

Porto, William F.; Pires, Állan S.; Franco, Octavio L.

2012-01-01

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine. PMID:23240023
A New Scheme to Characterize and Identify Protein Ubiquitination Sites.

PubMed

Nguyen, Van-Nui; Huang, Kai-Yao; Huang, Chien-Hsun; Lai, K Robert; Lee, Tzong-Yi

2017-01-01

Protein ubiquitination, involving the conjugation of ubiquitin on lysine residue, serves as an important modulator of many cellular functions in eukaryotes. Recent advancements in proteomic technology have stimulated increasing interest in identifying ubiquitination sites. However, most computational tools for predicting ubiquitination sites are focused on small-scale data. With an increasing number of experimentally verified ubiquitination sites, we were motivated to design a predictive model for identifying lysine ubiquitination sites for large-scale proteome dataset. This work assessed not only single features, such as amino acid composition (AAC), amino acid pair composition (AAPC) and evolutionary information, but also the effectiveness of incorporating two or more features into a hybrid approach to model construction. The support vector machine (SVM) was applied to generate the prediction models for ubiquitination site identification. Evaluation by five-fold cross-validation showed that the SVM models learned from the combination of hybrid features delivered a better prediction performance. Additionally, a motif discovery tool, MDDLogo, was adopted to characterize the potential substrate motifs of ubiquitination sites. The SVM models integrating the MDDLogo-identified substrate motifs could yield an average accuracy of 68.70 percent. Furthermore, the independent testing result showed that the MDDLogo-clustered SVM models could provide a promising accuracy (78.50 percent) and perform better than other prediction tools. Two cases have demonstrated the effective prediction of ubiquitination sites with corresponding substrate motifs.
Optimization of dipeptidic inhibitors of cathepsin L for improved Toxoplasma gondii selectivity and CNS permeability.

PubMed

Zwicker, Jeffery D; Diaz, Nicolas A; Guerra, Alfredo J; Kirchhoff, Paul D; Wen, Bo; Sun, Duxin; Carruthers, Vern B; Larsen, Scott D

2018-06-01

The neurotropic protozoan Toxoplasma gondii is the second leading cause of death due to foodborne illness in the US, and has been designated as one of five neglected parasitic infections by the Center for Disease Control and Prevention. Currently, no treatment options exist for the chronic dormant-phase Toxoplasma infection in the central nervous system (CNS). T. gondii cathepsin L (TgCPL) has recently been implicated as a novel viable target for the treatment of chronic toxoplasmosis. In this study, we report the first body of SAR work aimed at developing potent inhibitors of TgCPL with selectivity vs the human cathepsin L. Starting from a known inhibitor of human cathepsin L, and guided by structure-based design, we were able to modulate the selectivity for Toxoplasma vs human CPL by nearly 50-fold while modifying physiochemical properties to be more favorable for metabolic stability and CNS penetrance. The overall potency of our inhibitors towards TgCPL was improved from 2 μM to as low as 110 nM and we successfully demonstrated that an optimized analog 18b is capable of crossing the BBB (0.5 brain/plasma). This work is an important first step toward development of a CNS-penetrant probe to validate TgCPL as a feasible target for the treatment of chronic toxoplasmosis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multi-Label Learning via Random Label Selection for Protein Subcellular Multi-Locations Prediction.

PubMed

Wang, Xiao; Li, Guo-Zheng

2013-03-12

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multi-location proteins to multiple proteins with single location, which doesn't take correlations among different subcellular locations into account. In this paper, a novel method named RALS (multi-label learning via RAndom Label Selection), is proposed to learn from multi-location proteins in an effective and efficient way. Through five-fold cross validation test on a benchmark dataset, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark datasets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multi-locations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.
NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation.

PubMed

Sakthivel, Seethalakshmi; S K M, Habeeb

2015-01-01

The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q3 but also validates every prediction with the homologous PDB entries. NNvPDB is based on the concept of Neural Network, with a new and different approach of training the network every time with five PDB structures that are similar to query sequence. The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66%. http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl.
Exploratory and Confirmatory Factor Analysis of the Decision Regret Scale in Recipients of Internal Cardioverter Defibrillators

PubMed Central

Hickman, Ronald L.; Pinto, Melissa D.; Lee, Eunsuk; Daly, Barbara J.

2015-01-01

The Decision Regret Scale (DRS) is a five-item instrument that captures an individual’s regret associated with a healthcare decision. Cross-sectional data were collected from 109 cardiac patients who decided to receive an internal cardioverter defibrillator (ICD). Exploratory and confirmatory factor analyses, assessments of the internal reliability consistency (α = .86), and discriminant validity established the DRS as a reliable and valid measure of decision regret in ICD recipients. The DRS, a psychometrically sound instrument, has relevance for clinicians and researchers vested in optimizing the decisional outcomes of ICD recipients. Future research is needed to examine the reliability and validity of the DRS in a larger and more diverse sample of ICD recipients. PMID:22679707
Communication: nanosecond folding dynamics of an alpha helix: time-dependent 2D-IR cross peaks observed using polarization-sensitive dispersed pump-probe spectroscopy.

PubMed

Panman, Matthijs R; van Dijk, Chris N; Meuzelaar, Heleen; Woutersen, S

2015-01-28

We present a simple method to measure the dynamics of cross peaks in time-resolved two-dimensional vibrational spectroscopy. By combining suitably weighted dispersed pump-probe spectra, we eliminate the diagonal contribution to the 2D-IR response, so that the dispersed pump-probe signal contains the projection of only the cross peaks onto one of the axes of the 2D-IR spectrum. We apply the method to investigate the folding dynamics of an alpha-helical peptide in a temperature-jump experiment and find characteristic folding and unfolding time constants of 260 ± 30 and 580 ± 70 ns at 298 K.
Validation of bioelectrical impedance analysis for total body water assessment against the deuterium dilution technique in Asian children.

PubMed

Liu, A; Byrne, N M; Ma, G; Nasreddine, L; Trinidad, T P; Kijboonchoo, K; Ismail, M N; Kagawa, M; Poh, B K; Hills, A P

2011-12-01

To develop and cross-validate bioelectrical impedance analysis (BIA) prediction equations of total body water (TBW) and fat-free mass (FFM) for Asian pre-pubertal children from China, Lebanon, Malaysia, Philippines and Thailand. Height, weight, age, gender, resistance and reactance measured by BIA were collected from 948 Asian children (492 boys and 456 girls) aged 8-10 years from the five countries. The deuterium dilution technique was used as the criterion method for the estimation of TBW and FFM. The BIA equations were developed using stepwise multiple regression analysis and cross-validated using the Bland-Altman approach. The BIA prediction equation for the estimation of TBW was as follows: TBW=0.231 × height(2)/resistance+0.066 × height+0.188 × weight+0.128 × age+0.500 × sex-0.316 × Thais-4.574 (R (2)=88.0%, root mean square error (RMSE)=1.3 kg), and for the estimation of FFM was as follows: FFM=0.299 × height(2)/resistance+0.086 × height+0.245 × weight+0.260 × age+0.901 × sex-0.415 × ethnicity (Thai ethnicity =1, others = 0)-6.952 (R (2)=88.3%, RMSE=1.7 kg). No significant difference between measured and predicted values for the whole cross-validation sample was found. However, the prediction equation for estimation of TBW/FFM tended to overestimate TBW/FFM at lower levels whereas underestimate at higher levels of TBW/FFM. Accuracy of the general equation for TBW and FFM was also valid at each body mass index category. Ethnicity influences the relationship between BIA and body composition in Asian pre-pubertal children. The newly developed BIA prediction equations are valid for use in Asian pre-pubertal children.
Etoxazole resistance in predatory mite Phytoseiulus persimilis A.-H. (Acari: Phytoseiidae): Cross-resistance, inheritance and biochemical resistance mechanisms.

PubMed

Yorulmaz Salman, Sibel; Aydınlı, Fatma; Ay, Recep

2015-07-01

Phytoseiulus persimilis of the family Phytoseiidae is an effective predatory mite species that is used to control pest mites. The LC50 and LC60 values of etoxazole were determined on P. persimilis using a leaf-disc method and spraying tower. A laboratory selection population designated ETO6 was found to have a 111.63-fold resistance to etoxazole following 6 selection cycles. This population developed low cross-resistance to spinosad, spiromesifen, acetamiprid, indoxacarb, chlorantraniliprole, milbemectin and moderate cross-resistance to deltamethrin. PBO, IBP and DEM synergised resistance 3.17-, 2.85- and 3.60-fold respectively. Crossing experiments revealed that etoxazole resistance in the ETO6 population was an intermediately dominant and polygenic. In addition, detoxifying enzyme activities were increased 2.71-fold for esterase, 3.09-fold for glutathione S-transferase (GST) and 2.76-fold for cytochrome P450 monooxygenase (P450) in the ETO6 population. Selection for etoxazole under laboratory conditions resulted in the development of etoxazole resistance in the predatory mite P. persimilis that are resistant to pesticides are considered valuable for use in resistance management programmes within integrated pest control strategies. Copyright © 2014 Elsevier Inc. All rights reserved.
Cross-cultural equivalence in translations of the oral health impact profile.

PubMed

MacEntee, Michael I; Brondani, Mario

2016-04-01

The Oral Health Impact Profile (OHIP) has been translated for comparisons across cultural boundaries. This report on a systematic search of literature published between 1994 and 2014 aims to identify an acceptable method of translating psychometric instruments for cross-cultural equivalence, and how they were used to translate the OHIP. An electronic search used the keywords 'cultural adaptation', 'validation', 'Oral Health Impact Profile' and 'OHIP' in MEDLINE and EMBASE databases supplemented by reference links and grey literature. It included papers on methods of cross-cultural translation and translations of the OHIP for dentulous adults and adolescents, and excluded papers without translational details or limited to specific disorders. The search identified eight steps to cross-cultural equivalence, and 36 (plus three supplemental) translations of the OHIP. The steps involve assessment of (i) forward/backward translation by committee, (ii) constructs, (iii) item interpretations, (iv) interval scales, (v) convergent validity, (vi) discriminant validity, (vii) responsiveness to clinical change and (viii) pilot tests. Most (>60%) of the translations involved forward/backward translation by committee, item interpretations, interval scales, convergence, discrimination and pilot tests, but fewer assessed the underlying theory (47%) or responsiveness to clinical change (28%). An acceptable method for translating quality of life-related psychometric instruments for cross-cultural equivalence has eight procedural steps, and most of the 36 OHIP translations involved at least five of the steps. Only translations to Saudi Arabian Arabic, Chinese Mandarin, German and Japanese used all eight steps to claim cultural equivalence with the original OHIP. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
[An instrument for assessing clinical aptitude in cervicovaginitis in the family medicine practice].

PubMed

Arrieta-Pérez, Raúl Tomás; Lona-Calixto, Beatriz

2011-01-01

the cervicovaginitis is one of the first twelve causes on demand at primary care medicine thus the family physician must be able to identify and treat it. The objective was to validate a constructed instrument for measuring the clinical aptitude on cervicovaginitis. cross-sectional, descriptive, prolective study was carried out. An instrument with five clinical cases was done. It has seven indicators, whose answers were true, false and I do not know. The validity content was done by three family physicians and a Gynecologist, with experience in education. The trustworthiness was determined by means of the test of Kuder-Richardson formula 20 with the results obtained in a pilot test in 50 family medicine residents. the instrument was constituted by five clinical cases with 140 Items distributed in seven indicators with 20 items for each indicator and a total of 70 true answers and 70 false answers; seven categories for the degree of clinical aptitude settled down. The trustworthiness of the instrument was 0.81. the instrument is valid and reliable to identify the clinical aptitude of the family physician on cervicovaginitis.
Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

PubMed

Luo, Heng; Ye, Hao; Ng, Hui; Shi, Leming; Tong, Weida; Mattes, William; Mendrick, Donna; Hong, Huixiao

2015-01-01

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.
Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

PubMed Central

2015-01-01

Background As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Methods Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Results Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Conclusions Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs. PMID:26424483
A Study into the Collision-induced Dissociation (CID) Behavior of Cross-Linked Peptides*

PubMed Central

Giese, Sven H.; Fischer, Lutz; Rappsilber, Juri

2016-01-01

Cross-linking/mass spectrometry resolves protein–protein interactions or protein folds by help of distance constraints. Cross-linkers with specific properties such as isotope-labeled or collision-induced dissociation (CID)-cleavable cross-linkers are in frequent use to simplify the identification of cross-linked peptides. Here, we analyzed the mass spectrometric behavior of 910 unique cross-linked peptides in high-resolution MS1 and MS2 from published data and validate the observation by a ninefold larger set from currently unpublished data to explore if detailed understanding of their fragmentation behavior would allow computational delivery of information that otherwise would be obtained via isotope labels or CID cleavage of cross-linkers. Isotope-labeled cross-linkers reveal cross-linked and linear fragments in fragmentation spectra. We show that fragment mass and charge alone provide this information, alleviating the need for isotope-labeling for this purpose. Isotope-labeled cross-linkers also indicate cross-linker-containing, albeit not specifically cross-linked, peptides in MS1. We observed that acquisition can be guided to better than twofold enrich cross-linked peptides with minimal losses based on peptide mass and charge alone. By help of CID-cleavable cross-linkers, individual spectra with only linear fragments can be recorded for each peptide in a cross-link. We show that cross-linked fragments of ordinary cross-linked peptides can be linearized computationally and that a simplified subspectrum can be extracted that is enriched in information on one of the two linked peptides. This allows identifying candidates for this peptide in a simplified database search as we propose in a search strategy here. We conclude that the specific behavior of cross-linked peptides in mass spectrometers can be exploited to relax the requirements on cross-linkers. PMID:26719564
An intercomparison of a large ensemble of statistical downscaling methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment

NASA Astrophysics Data System (ADS)

Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of downscaling experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open downscaling intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to downscale precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data (downscaled values) and metadata (characterizing different aspects of the downscaling methods). This constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods. Here, we present an overall validation, analyzing marginal and temporal aspects to assess the intrinsic performance and added value of statistical downscaling methods at both annual and seasonal levels. This validation takes into account the different properties/limitations of different approaches and techniques (as reported in the provided metadata) in order to perform a fair comparison. It is pointed out that this experiment alone is not sufficient to evaluate the limitations of (MOS) bias correction techniques. Moreover, it also does not fully validate PP since we don't learn whether we have the right predictors and whether the PP assumption is valid. These problems will be analyzed in the subsequent community-open VALUE experiments 2) and 3), which will be open for participation along the present year.
Fluorescence-based characterization of genetically encoded peptides that fold in live cells: progress toward a generic hairpin scaffold

NASA Astrophysics Data System (ADS)

Cheng, Zihao; Campbell, Robert E.

2007-02-01

Binding proteins suitable for expression and high affinity molecular recognition in the cytoplasm or nucleus of live cells have numerous applications in the biological sciences. In an effort to add a new minimal motif to the growing repertoire of validated non-immunoglobulin binding proteins, we have undertaken the development of a generic protein scaffold based on a single β-hairpin that can fold efficiently in the cytoplasm. We have developed a method, based on the measurement of fluorescence resonance energy transfer (FRET) between a genetically fused cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP), that allows the structural stability of recombinant β-hairpin peptides to be rapidly assessed both in vitro and in vivo. We have previously reported the validation of this method when applied to a 16mer tryptophan zipper β-hairpin. We now describe the use of this method to evaluate the potential of a designed 20mer β-hairpin peptide with a 3rd Trp/Trp cross-strand pair to function as a generic protein scaffold. Quantitative analysis of the FRET efficiency, resistance to proteolysis (assayed by loss of FRET), and circular dichroism spectra revealed that the 20mer peptide is significantly more tolerant of destabilizing mutations than the 16mer peptide. Furthermore, we experimentally demonstrate that the in vitro determined β-hairpin stabilities are well correlated with in vivo β-hairpin stabilities as determined by FRET measurements of colonies of live bacteria expressing the recombinant peptides flanked by CFP and YFP. Finally, we report on our progress to develop highly folded 24mer and 28mer β-hairpin peptides through the use of fluorescence-based library screening.
[Validation of the Portuguese version of an instrument to measure the degree of patient knowledge about their medication].

PubMed

Rubio, Joaquín Salmerón; García-Delgado, Pilar; Ferreira, Paula Iglésias; Santos, Henrique Mateus; Martínez-Martínez, Fernando

2014-04-01

The scope of this study was the validation of a cross-culturally adapted questionnaire into Portuguese in five community pharmacies in Portugal. The discriminatory power of items, content and construct validity and factor analysis of the main components and their reliability and stability were determined. A high degree of semantic equivalence between the original questionnaire and the cross-culturally adapted questionnaire into Portuguese was observed. A Kaiser-Meyer-Olkin index of 0.550 was obtained and the Bartlett sphericity test confirmed the adequacy of the data for the application of factor analysis (p <0.0001). Three factors which accounted for 52.6% of the total variability were considered. With respect to reliability the following results were obtained: 0.519 for Cronbach's alpha test; 0.89 for Cohen's kappa coefficient; and 0.756 (IC=0.598-0.963) for the CCI exam. In this work, the first adaptation for the Portuguese culture of a specific questionnaire was produced to measure the degree of knowledge patients have about their medication.
Tunable mechanical stability and deformation response of a resilin-based elastomer.

PubMed

Li, Linqing; Teller, Sean; Clifton, Rodney J; Jia, Xinqiao; Kiick, Kristi L

2011-06-13

Resilin, the highly elastomeric protein found in specialized compartments of most arthropods, possesses superior resilience and excellent high-frequency responsiveness. Enabled by biosynthetic strategies, we have designed and produced a modular, recombinant resilin-like polypeptide bearing both mechanically active and biologically active domains to create novel biomaterial microenvironments for engineering mechanically active tissues such as blood vessels, cardiovascular tissues, and vocal folds. Preliminary studies revealed that these recombinant materials exhibit promising mechanical properties and support the adhesion of NIH 3T3 fibroblasts. In this Article, we detail the characterization of the dynamic mechanical properties of these materials, as assessed via dynamic oscillatory shear rheology at various protein concentrations and cross-linking ratios. Simply by varying the polypeptide concentration and cross-linker ratios, the storage modulus G' can be easily tuned within the range of 500 Pa to 10 kPa. Strain-stress cycles and resilience measurements were probed via standard tensile testing methods and indicated the excellent resilience (>90%) of these materials, even when the mechanically active domains are intercepted by nonmechanically active biological cassettes. Further evaluation, at high frequencies, of the mechanical properties of these materials were assessed by a custom-designed torsional wave apparatus (TWA) at frequencies close to human phonation, indicating elastic modulus values from 200 to 2500 Pa, which is within the range of experimental data collected on excised porcine and human vocal fold tissues. The results validate the outstanding mechanical properties of the engineered materials, which are highly comparable to the mechanical properties of targeted vocal fold tissues. The ease of production of these biologically active materials, coupled to their outstanding mechanical properties over a range of compositions, suggests their potential in tissue regeneration applications.
Evaluating Cross-Cultural Acculturation Experiences Influencing International Black African Students' Academic Success in a United States University

ERIC Educational Resources Information Center

Macharia-Lowe, Josephine

2017-01-01

In 2013-2014, about 25,000 the International Black African Student (IBAS) were enrolled in colleges and universities in the United States. It represents an increase of five percent. There is inadequate research on the Participants were at least 18 years of age and holders of F-1 (academic visa) and/or J-1 (exchange visitors) visas. To validate the…
Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression.

PubMed

Leaver, Amber M; Wade, Benjamin; Vasavada, Megha; Hellemann, Gerhard; Joshi, Shantanu H; Espinoza, Randall; Narr, Katherine L

2018-01-01

Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n = 46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.
Circuit topology of self-interacting chains: implications for folding and unfolding dynamics.

PubMed

Mugler, Andrew; Tans, Sander J; Mashaghi, Alireza

2014-11-07

Understanding the relationship between molecular structure and folding is a central problem in disciplines ranging from biology to polymer physics and DNA origami. Topology can be a powerful tool to address this question. For a folded linear chain, the arrangement of intra-chain contacts is a topological property because rearranging the contacts requires discontinuous deformations. Conversely, the topology is preserved when continuously stretching the chain while maintaining the contact arrangement. Here we investigate how the folding and unfolding of linear chains with binary contacts is guided by the topology of contact arrangements. We formalize the topology by describing the relations between any two contacts in the structure, which for a linear chain can either be in parallel, in series, or crossing each other. We show that even when other determinants of folding rate such as contact order and size are kept constant, this 'circuit' topology determines folding kinetics. In particular, we find that the folding rate increases with the fractions of parallel and crossed relations. Moreover, we show how circuit topology constrains the conformational phase space explored during folding and unfolding: the number of forbidden unfolding transitions is found to increase with the fraction of parallel relations and to decrease with the fraction of series relations. Finally, we find that circuit topology influences whether distinct intermediate states are present, with crossed contacts being the key factor. The approach presented here can be more generally applied to questions on molecular dynamics, evolutionary biology, molecular engineering, and single-molecule biophysics.
Decorrelation of the true and estimated classifier errors in high-dimensional settings.

PubMed

Hanczar, Blaise; Hua, Jianping; Dougherty, Edward R

2007-01-01

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of cross-validation precision in high-dimensional settings where feature selection is used to mitigate the peaking phenomenon (overfitting). Because classifier design is based upon random samples, both the true and estimated errors are sample-dependent random variables, and one would expect a loss of precision if the estimated and true errors are not well correlated, so that natural questions arise as to the degree of correlation and the manner in which lack of correlation impacts error estimation. We demonstrate the effect of correlation on error precision via a decomposition of the variance of the deviation distribution, observe that the correlation is often severely decreased in high-dimensional settings, and show that the effect of high dimensionality on error estimation tends to result more from its decorrelating effects than from its impact on the variance of the estimated error. We consider the correlation between the true and estimated errors under different experimental conditions using both synthetic and real data, several feature-selection methods, different classification rules, and three error estimators commonly used (leave-one-out cross-validation, k-fold cross-validation, and .632 bootstrap). Moreover, three scenarios are considered: (1) feature selection, (2) known-feature set, and (3) all features. Only the first is of practical interest; however, the other two are needed for comparison purposes. We will observe that the true and estimated errors tend to be much more correlated in the case of a known feature set than with either feature selection or using all features, with the better correlation between the latter two showing no general trend, but differing for different models.
Translation and evaluation of the Cultural Awareness Scale for Korean nursing students.

PubMed

Oh, Hyunjin; Lee, Jung-ah; Schepp, Karen G

2015-02-20

To evaluate the effectiveness of a curriculum for achieving high levels of cultural competence, we need to be able to assess education intended to enhance cultural competency skills. We therefore translated the Cultural Awareness Scale (CAS) into Korean (CAS-K). The purpose of this study was to evaluate the cross-cultural applicability and psychometric properties of the CAS-K, specifically its reliability and validity. A cross-sectional descriptive design was used to conduct the evaluation. A convenience sample of 495 nursing students was recruited from four levels of nursing education within four universities in the city of Daejeon, South Korea. This study provided beginning evidence of the validity and reliability of the CAS-K and the cross-cultural applicability of the concepts underlying this instrument. Cronbach's alpha ranged between 0.59 and 0.86 (overall 0.89) in the tests of internal consistency. Cultural competency score prediction of the experience of travel abroad (r=0.084) and the perceived need for cultural education (r=0.223) suggested reasonable criterion validity. Five factors with eigenvalues >1.0 were extracted, accounting for 55.58% of the variance; two retained the same items previously identified for the CAS. The CAS-K demonstrated satisfactory validity and reliability in measuring cultural awareness in this sample of Korean nursing students. The revised CAS-K should be tested for its usability in curriculum evaluation and its applicability as a guide for teaching cultural awareness among groups of Korean nursing students.

Using boosted regression trees to predict the near-saturated hydraulic conductivity of undisturbed soils

NASA Astrophysics Data System (ADS)

Koestel, John; Bechtold, Michel; Jorda, Helena; Jarvis, Nicholas

2015-04-01

The saturated and near-saturated hydraulic conductivity of soil is of key importance for modelling water and solute fluxes in the vadose zone. Hydraulic conductivity measurements are cumbersome at the Darcy scale and practically impossible at larger scales where water and solute transport models are mostly applied. Hydraulic conductivity must therefore be estimated from proxy variables. Such pedotransfer functions are known to work decently well for e.g. water retention curves but rather poorly for near-saturated and saturated hydraulic conductivities. Recently, Weynants et al. (2009, Revisiting Vereecken pedotransfer functions: Introducing a closed-form hydraulic model. Vadose Zone Journal, 8, 86-95) reported a coefficients of determination of 0.25 (validation with an independent data set) for the saturated hydraulic conductivity from lab-measurements of Belgian soil samples. In our study, we trained boosted regression trees on a global meta-database containing tension-disk infiltrometer data (see Jarvis et al. 2013. Influence of soil, land use and climatic factors on the hydraulic conductivity of soil. Hydrology & Earth System Sciences, 17, 5185-5195) to predict the saturated hydraulic conductivity (Ks) and the conductivity at a tension of 10 cm (K10). We found coefficients of determination of 0.39 and 0.62 under a simple 10-fold cross-validation for Ks and K10. When carrying out the validation folded over the data-sources, i.e. the source publications, we found that the corresponding coefficients of determination reduced to 0.15 and 0.36, respectively. We conclude that the stricter source-wise cross-validation should be applied in future pedotransfer studies to prevent overly optimistic validation results. The boosted regression trees also allowed for an investigation of relevant predictors for estimating the near-saturated hydraulic conductivity. We found that land use and bulk density were most important to predict Ks. We also observed that Ks is large in fine and coarse textured soils and smaller in medium textured soils. Completely different predictors were important for appraising K10, where the soil macropore system is air-filled and therefore inactive. Here, the average annual temperature and precipitation where most important. The reasons for this are unclear and require further research. The clay content and the organic matter content were also important predictors of K10. We suggest that a larger and more complete database may help to improve the prediction of K10, whereas it may be more fruitful to estimate Ks statistics of sampling sites instead of individual values since the Ks is highly variable over very short distances.
Balancing cross-sections combining field work and remote sensing data using LithoTect software in the Zagros fold-and-thrust belt, N Iraq.

NASA Astrophysics Data System (ADS)

Reif, Daniel; Grasemann, Bernhard; Lockhart, Duncan

2010-05-01

The Zagros fold-and-thrust belt has formed in detached Phanerozoic sedimentary cover rocks above a shortened crystalline Precambrian basement and evolved through the Late Cretaceous to Miocene collision between the Arabian and Eurasian plate, during which the Neotethys oceanic basin was closed. Deformation is partitioned in SW directed folding and thrusting of the sediments and NW-SE to N-S trending dextral strike slip faults. The sub-cylindrical doubly-plunging fold trains with wavelengths of 5 - 10 km host more than half of the world's hydrocarbon reserves in mostly anticlinal traps. Generally the Zagros is divided into three NW-SE striking tectonic units: the Zagros Imbricate Zone, the Zagros Simply Folded Belt and the Zagros Foredeep. This work presents a balanced cross-section through the Simply Folded Belt, NE of the city of Erbil (Kurdistan, Iraq). The regional stratigraphy comprises mainly Cretaceous to Cenozoic folded sediments consisting of massive, carbonate rocks (limestones, dolomites), reacting as competent layers during folding compared to the incompetent behavior of interlayered siltstones, claystones and marls. Although the overall security situation in Kurdistan is much better than in the rest of Iraq, structural field mapping was restricted to asphalt streets, mainly because of the contamination of the area with landmines and unexploded ordnance. In order to extend the structural measurements statistically over the investigated area, we used a newly developed software tool (www.terramath.com) for interactive structural mapping of spatial orientations (i.e. dip direction and dip angles) of the sedimentary beddings from digital elevation models. Structural field data and computed measurements where integrated and projected in NE-SW striking balanced cross-sections perpendicular to the regional trend of the fold axes. We used the software LithoTect (www.geologicsystems.com) for the restoration of the cross-sections. Depending on the interpretation of the shape of the synclines, which are not exposed and covered by Neogene sediments, the shortening is in the order of 10-20%. The restoration confirms that large scale faulting is only of minor importance in the Simply Folded Belt.
Medication use and fall-risk assessment for falls in an acute care hospital.

PubMed

Chiu, Ming-Huang; Lee, Hsin-Dai; Hwang, Hei-Fen; Wang, Shih-Chieh; Lin, Mau-Roung

2015-07-01

A nested case-control study was carried out to examine relationships of a fall-risk score and the use of single medications and polypharmacy with falls among hospitalized patients aged 50 years and older in Taiwan. There were 83 patients who experienced a fall during hospitalization in an acute-care hospital. Matched by age and sex, five control patients for each case were randomly selected from all other inpatients who had not experienced any fall at the time of the index fall. Patients who took tricyclic antidepressants, diuretics, and narcotics were 3.36-, 1.83- and 2.09-fold, respectively, more likely to experience a fall than their counterparts. Conversely, patients who took beta-blockers were 0.34-fold more likely than those who did not take them to experience a fall. Patients taking ≥6 medications were 3.08-fold more likely than those taking fewer medications to experience a fall, whereas those with anxiety were 4.72-fold more likely to experience a fall than those without. A high fall-risk score was not significantly associated with the occurrence of falls. Among older hospitalized patients, tricyclic antidepressants, diuretics, narcotics, and polypharmacy should be mindfully prescribed and reviewed on a regular basis. A fall-risk scale developed from community-dwelling older people might not accurately predict falls in hospitalized patients. Further research to validate the negative effect of beta-blocker use on falls is required. © 2014 Japan Geriatrics Society.
Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

PubMed

Li, Yunhai; Lee, Kee Khoon; Walsh, Sean; Smith, Caroline; Hadingham, Sophie; Sorefan, Karim; Cawley, Gavin; Bevan, Michael W

2006-03-01

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.
Developing functional musculoskeletal tissues through hypoxia and lysyl oxidase-induced collagen cross-linking

PubMed Central

Makris, Eleftherios A.; Responte, Donald J.; Hu, Jerry C.; Athanasiou, Kyriacos A.

2014-01-01

The inability to recapitulate native tissue biomechanics, especially tensile properties, hinders progress in regenerative medicine. To address this problem, strategies have focused on enhancing collagen production. However, manipulating collagen cross-links, ubiquitous throughout all tissues and conferring mechanical integrity, has been underinvestigated. A series of studies examined the effects of lysyl oxidase (LOX), the enzyme responsible for the formation of collagen cross-links. Hypoxia-induced endogenous LOX was applied in multiple musculoskeletal tissues (i.e., cartilage, meniscus, tendons, ligaments). Results of these studies showed that both native and engineered tissues are enhanced by invoking a mechanism of hypoxia-induced pyridinoline (PYR) cross-links via intermediaries like LOX. Hypoxia was shown to enhance PYR cross-linking 1.4- to 6.4-fold and, concomitantly, to increase the tensile properties of collagen-rich tissues 1.3- to 2.2-fold. Direct administration of exogenous LOX was applied in native cartilage and neocartilage generated using a scaffold-free, self-assembling process of primary chondrocytes. Exogenous LOX was found to enhance native tissue tensile properties 1.9-fold. LOX concentration- and time-dependent increases in PYR content (∼16-fold compared with controls) and tensile properties (approximately fivefold compared with controls) of neocartilage were also detected, resulting in properties on par with native tissue. Finally, in vivo subcutaneous implantation of LOX-treated neocartilage in nude mice promoted further maturation of the neotissue, enhancing tensile and PYR content approximately threefold and 14-fold, respectively, compared with in vitro controls. Collectively, these results provide the first report, to our knowledge, of endogenous (hypoxia-induced) and exogenous LOX applications for promoting collagen cross-linking and improving the tensile properties of a spectrum of native and engineered tissues both in vitro and in vivo. PMID:25349395
Three-dimensional strain produced by >50 My of episodic extension, Horse Prairie basin area, SW Montana, U.S.A.

NASA Astrophysics Data System (ADS)

Vandenburg, Colby J.; Janecke, Susanne U.; McIntosh, William C.

1998-12-01

The Horse Prairie basin of southwestern Montana is a complex, east-dipping half-graben that contains three angular unconformity-bounded sequences of Tertiary sedimentary rocks overlying middle Eocene volcanic rocks. New mapping of the basin and its hanging wall indicate that five temporally and geometrically distinct phases of normal faulting and at least three generations of fault-related extensional folding affected the area during the late Mesozoic (?) to Cenozoic. All of these phases of extension are evident over regional or cordilleran-scale domains. The extension direction has rotated ˜90° four times in the Horse Prairie area resulting in a complex three-dimensional strain field with ≫60% east-west and >25% north-south bulk extension. Extensional folds with axes at high angles to the associated normal fault record most of the three-dimensional strain during individual phases of extension (phases 3a, 3b, and 4). Cross-cutting relationships between normal faults and Tertiary volcanic and sedimentary rocks constrain the ages of each distinct phase of deformation and show that extension continued episodically for more than 50 My. Gravitational collapse of the Sevier fold and thrust belt was the ultimate cause of most of the extension.
Human granulocyte colony-stimulating factor may improve outcome attributable to neonatal sepsis complicated by neutropenia.

PubMed

Kocherlakota, P; La Gamma, E F

1997-07-01

To determine whether adjunctive therapy with recombinant human granulocyte colony-stimulating factor (rhG-CSF) could reverse sepsis-associated neonatal neutropenia and improve neonatal survival compared with conventional therapy in a phase I/II-type trial. An intravenous infusion of rhG-CSF (10 microg/kg/d x 3 d) was administered to 14 septic neutropenic neonates. Neutrophilic responses and outcome of these neonates were compared with 11 concurrently treated, retrospectively selected, case-matched control septic patients identified by using a search of medical records coded for sepsis with neutropenia (>/=24 hours). Seven neonates with early-onset sepsis with neutropenia at birth and seven neonates with late-onset sepsis plus neutropenia (all with necrotizing enterocolitis) were entered in the rhG-CSF treatment group. Results were compared with a conventional therapy control group (five early onset, six late onset). No significant differences existed in the birth weight, gestational age, use of antibiotic therapy, magnitude of respiratory support, severity of metabolic acidosis, use of vasopressors, or other supportive therapy between the two groups. In the rhG-CSF-treated group and in the conventionally treated control group, the absolute neutrophil count (ANC) (mean +/- SEM) was 585 +/- 138 and 438 +/- 152, respectively. The ANC increased to more than baseline in the rhG-CSF-treated group by 10-fold versus 2-fold at 24 hours, 18-fold versus 4-fold at 48 hours, 24-fold versus 5-fold at 72 hours (significant by one-way analysis of variance in the rhG-CSF group only), and 29-fold versus 16-fold at 7 to 10 days when compared with the conventional therapy group. There were no nonresponders in the rhG-CSF group by 24 hours after the first dose of study drug. Monocyte cell counts also increased significantly in both groups by 7 days after entry into this protocol but remained within normal range for age. No clinically significant effect on lymphocytes, erythrocytes, or platelet counts was noted. Thirteen patients in the rhG-CSF-treated group (92%; 13 out of 14) and five in the conventionally treated group (55%; 5 out of 11) survived to 28 days after the onset of the signs of sepsis. No adverse effects were noted in the rhG-CSF-treated group. rhG-CSF can increase the neutrophil count in critically ill septic neutropenic neonates. This finding suggests that rhG-CSF may be effective in a therapeutically useful time frame to treat septic neonates with neonatal neutropenia attributable to bone marrow suppression or neutrophil consumption. Future randomized trials are needed to validate the beneficial effects of rhG-CSF and to determine whether any significant side effects of therapy exist.
Model selection for pion photoproduction

DOE PAGES

Landay, J.; Doring, M.; Fernandez-Ramirez, C.; ...

2017-01-12

Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory andmore » K-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. As a result, the principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (dσ/dΩ), photon-beam asymmetries (Σ), and target asymmetry differential cross sections (dσ T/d≡Tdσ/dΩ) in the low-energy regime.« less
Strengths and Difficulties Questionnaire: internal validity and reliability for New Zealand preschoolers.

PubMed

Kersten, Paula; Vandal, Alain C; Elder, Hinemoa; McPherson, Kathryn M

2018-04-21

This observational study examines the internal construct validity, internal consistency and cross-informant reliability of the Strengths and Difficulties Questionnaire (SDQ) in a New Zealand preschool population across four ethnicity strata (New Zealand European, Māori, Pasifika, Asian). Rasch analysis was employed to examine internal validity on a subsample of 1000 children. Internal consistency (n=29 075) and cross-informant reliability (n=17 006) were examined using correlations, intraclass correlation coefficients and Cronbach's alpha on the sample available for such analyses. Data were used from a national SDQ database provided by the funder, pertaining to New Zealand domiciled children aged 4 and 5 and scored by their parents and teachers. The five subscales do not fit the Rasch model (as indicated by the overall fit statistics), contain items that are biased (differential item functioning (DIF)) by key variables, suffer from a floor and ceiling effect and have unacceptable internal consistency. After dealing with DIF, the Total Difficulty scale does fit the Rasch model and has good internal consistency. Parent/teacher inter-rater reliability was unacceptably low for all subscales. The five SDQ subscales are not valid and not suitable for use in their own right in New Zealand. We have provided a conversion table for the Total Difficulty scale, which takes account of bias by ethnic group. Clinicians should use this conversion table in order to reconcile DIF by culture in final scores. It is advisable to use both parents and teachers' feedback when considering children's needs for referral of further assessment. Future work should examine whether validity is impacted by different language versions used in the same country. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
The psychometric validation of the Social Problem-Solving Inventory--Revised with UK incarcerated sexual offenders.

PubMed

Wakeling, Helen C

2007-09-01

This study examined the reliability and validity of the Social Problem-Solving Inventory--Revised (SPSI-R; D'Zurilla, Nezu, & Maydeu-Olivares, 2002) with a population of incarcerated sexual offenders. An availability sample of 499 adult male sexual offenders was used. The SPSI-R had good reliability measured by internal consistency and test-retest reliability, and adequate validity. Construct validity was determined via factor analysis. An exploratory factor analysis extracted a two-factor model. This model was then tested against the theory-driven five-factor model using confirmatory factor analysis. The five-factor model was selected as the better fitting of the two, and confirmed the model according to social problem-solving theory (D'Zurilla & Nezu, 1982). The SPSI-R had good convergent validity; significant correlations were found between SPSI-R subscales and measures of self-esteem, impulsivity, and locus of control. SPSI-R subscales were however found to significantly correlate with a measure of socially desirable responding. This finding is discussed in relation to recent research suggesting that impression management may not invalidate self-report measures (e.g. Mills & Kroner, 2005). The SPSI-R was sensitive to sexual offender intervention, with problem-solving improving pre to post-treatment in both rapists and child molesters. The study concludes that the SPSI-R is a reasonably internally valid and appropriate tool to assess problem-solving in sexual offenders. However future research should cross-validate the SPSI-R with other behavioural outcomes to examine the external validity of the measure. Furthermore, future research should utilise a control group to determine treatment impact.
Near-edge X-ray refraction fine structure microscopy

DOE PAGES

Farmand, Maryam; Celestre, Richard; Denes, Peter; ...

2017-02-06

We demonstrate a method for obtaining increased spatial resolution and specificity in nanoscale chemical composition maps through the use of full refractive reference spectra in soft x-ray spectro-microscopy. Using soft x-ray ptychography, we measure both the absorption and refraction of x-rays through pristine reference materials as a function of photon energy and use these reference spectra as the basis for decomposing spatially resolved spectra from a heterogeneous sample, thereby quantifying the composition at high resolution. While conventional instruments are limited to absorption contrast, our novel refraction based method takes advantage of the strongly energy dependent scattering cross-section and can seemore » nearly five-fold improved spatial resolution on resonance.« less
A multiscale decomposition approach to detect abnormal vasculature in the optic disc.

PubMed

Agurto, Carla; Yu, Honggang; Murray, Victor; Pattichis, Marios S; Nemeth, Sheila; Barriga, Simon; Soliz, Peter

2015-07-01

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%. Copyright © 2015 Elsevier Ltd. All rights reserved.
[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.
A new class of compact high sensitive tiltmeter based on the UNISA folded pendulum mechanical architecture

NASA Astrophysics Data System (ADS)

Barone, Fabrizio; Giordano, Gerardo

2018-02-01

We present the Extended Folded Pendulum Model (EFPM), a model developed for a quantitative description of the dynamical behavior of a folded pendulum generically oriented in space. This model, based on the Tait-Bryan angular reference system, highlights the relationship between the folded pendulum orientation in the gravitational field and its natural resonance frequency. Tis model validated by tests performed with a monolithic UNISA Folded Pendulum, highlights a new technique of implementation of folded pendulum based tiltmeters.
Predictive Models for the Free Energy of Hydrogen Bonded Complexes with Single and Cooperative Hydrogen Bonds.

PubMed

Glavatskikh, Marta; Madzhidov, Timur; Solov'ev, Vitaly; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2016-12-01

In this work, we report QSPR modeling of the free energy ΔG of 1 : 1 hydrogen bond complexes of different H-bond acceptors and donors. The modeling was performed on a large and structurally diverse set of 3373 complexes featuring a single hydrogen bond, for which ΔG was measured at 298 K in CCl 4 . The models were prepared using Support Vector Machine and Multiple Linear Regression, with ISIDA fragment descriptors. The marked atoms strategy was applied at fragmentation stage, in order to capture the location of H-bond donor and acceptor centers. Different strategies of model validation have been suggested, including the targeted omission of individual H-bond acceptors and donors from the training set, in order to check whether the predictive ability of the model is not limited to the interpolation of H-bond strength between two already encountered partners. Successfully cross-validating individual models were combined into a consensus model, and challenged to predict external test sets of 629 and 12 complexes, in which donor and acceptor formed single and cooperative H-bonds, respectively. In all cases, SVM models outperform MLR. The SVM consensus model performs well both in 3-fold cross-validation (RMSE=1.50 kJ/mol), and on the external test sets containing complexes with single (RMSE=3.20 kJ/mol) and cooperative H-bonds (RMSE=1.63 kJ/mol). © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Characterization of Asian Corn Borer Resistance to Bt Toxin Cry1Ie.

PubMed

Wang, Yueqin; Yang, Jing; Quan, Yudong; Wang, Zhenying; Cai, Wanzhi; He, Kanglai

2017-06-07

A strain of the Asian corn borer (ACB), Ostrinia furnacalis (Guenée), has evolved >800-fold resistance to Cry1Ie (ACB-IeR) after 49 generations of selection. The inheritance pattern of resistance to Cry1Ie in ACB-IeR strain and its cross-resistance to other Bt toxins were determined through bioassay by exposing neonates from genetic-crosses to toxins incorporated into the diet. The response of progenies from reciprocal F₁ crosses were similar (LC 50 s: 76.07 vs. 74.32 μg/g), which suggested the resistance was autosomal. The effective dominance ( h ) decreased as concentration of Cry1Ie increased. h was nearly recessive or incompletely recessive on Cry1Ie maize leaf tissue ( h = 0.02), but nearly dominant or incompletely dominant ( h = 0.98) on Cry1Ie maize silk. Bioassay of the backcross suggested that the resistance was controlled by more than one locus. In addition, the resistant strain did not perform cross-resistance to Cry1Ab (0.8-fold), Cry1Ac (0.8-fold), Cry1F (0.9-fold), and Cry1Ah (1.0-fold). The present study not only offers the manifestation for resistance management, but also recommends that Cry1Ie will be an appropriate candidate for expression with Cry1Ab, Cry1Ac, Cry1F, or Cry1Ah for the development of Bt maize.
Development and validation of a multi-dimensional measure of intellectual humility

PubMed Central

Alfano, Mark; Iurino, Kathryn; Stey, Paul; Robinson, Brian; Christen, Markus; Yu, Feng; Lapsley, Daniel

2017-01-01

This paper presents five studies on the development and validation of a scale of intellectual humility. This scale captures cognitive, affective, behavioral, and motivational components of the construct that have been identified by various philosophers in their conceptual analyses of intellectual humility. We find that intellectual humility has four core dimensions: Open-mindedness (versus Arrogance), Intellectual Modesty (versus Vanity), Corrigibility (versus Fragility), and Engagement (versus Boredom). These dimensions display adequate self-informant agreement, and adequate convergent, divergent, and discriminant validity. In particular, Open-mindedness adds predictive power beyond the Big Six for an objective behavioral measure of intellectual humility, and Intellectual Modesty is uniquely related to Narcissism. We find that a similar factor structure emerges in Germanophone participants, giving initial evidence for the model’s cross-cultural generalizability. PMID:28813478
Only Five of 10 Strictly Conserved Disulfide Bonds Are Essential for Folding and Eight for Function of the HIV-1 Envelope Glycoprotein

PubMed Central

van Anken, Eelco; Sanders, Rogier W.; Liscaljet, I. Marije; Land, Aafke; Bontjer, Ilja; Tillemans, Sonja; Nabatov, Alexey A.; Paxton, William A.; Berkhout, Ben

2008-01-01

Protein folding in the endoplasmic reticulum goes hand in hand with disulfide bond formation, and disulfide bonds are considered key structural elements for a protein's folding and function. We used the HIV-1 Envelope glycoprotein to examine in detail the importance of its 10 completely conserved disulfide bonds. We systematically mutated the cysteines in its ectodomain, assayed the mutants for oxidative folding, transport, and incorporation into the virus, and tested fitness of mutant viruses. We found that the protein was remarkably tolerant toward manipulation of its disulfide-bonded structure. Five of 10 disulfide bonds were dispensable for folding. Two of these were even expendable for viral replication in cell culture, indicating that the relevance of these disulfide bonds becomes manifest only during natural infection. Our findings refine old paradigms on the importance of disulfide bonds for proteins. PMID:18653472
Adaptation and validation of Mandarin Chinese version of the pediatric Voice Handicap Index (pVHI).

PubMed

Lu, Dan; Huang, Mengjie; Li, Zhen; Yiu, Edwin M-L; Cheng, Ivy K-Y; Yang, Hui; Ma, Estella P-M

2018-01-01

The aim of this study was to adapt and validate the English version of pediatric voice handicap index (pVHI) into Mandarin Chinese.  METHODS: A cross-sectional study was performed from May 2016 to April 2017. A total of 367 parents participated in this study, and 338 parents completed the translated questionnaire without missing data, including 213 parents of children with voice disorders (patients group), and 125 parents of children without voice disorders (control group). The internal consistency, test-retest reliability, contents validity, construct validity, clinical validity, and cutoff point were calculated. The most common voice disorder in the patients group was vocal fold nodules (77.9%), followed by chronic laryngitis (18.8%), and vocal fold polyps (3.3%). The prevalence for voice disorders was higher in boys (67.1%) than girls (32.9%). The most common vocal misuse and abuse habit was shouting loudly (n = 186, 87.3%), followed by speaking for a long time (n = 158, 74.2%), and crying loudly (n = 99, 46.5%). The internal consistency for the Mandarin Chinese version of pVHI was excellent in patients group (Cronbach α = 0.95). The inter-class correlation coefficient indicated strong test-retest reliability (ICC = 0.99). The principal-component analysis demonstrated three-factor eigenvalues greater than 1, and the cumulative proportion was 66.23%. The mean total scores and mean subscales scores were significantly higher in the patients group than the control group (p < 0.05). The physical domain had the highest mean score among the three subscales (functional, physical and emotional) in the patients group. The optimal cutoff point of the Mandarin Chinese version of pVHI was 9.5 points with a sensitivity of 80.3% and a specificity of 84.8%. The Mandarin Chinese version of pVHI was a reliable and valid tool to assess the parents' perception about their children's voice disorders. It is recommended that it can be used as a screening tool for discriminating between children with and without dysphonia. Copyright © 2017 Elsevier B.V. All rights reserved.
The Short Physical Performance Battery is a discriminative tool for identifying patients with COPD at risk of disability.

PubMed

Bernabeu-Mora, Roberto; Medina-Mirapeix, Françesc; Llamazares-Herrán, Eduardo; García-Guillamón, Gloria; Giménez-Giménez, Luz María; Sánchez-Nieto, Juan Miguel

2015-01-01

Limited mobility is a risk factor for developing chronic obstructive pulmonary disease (COPD)-related disabilities. Little is known about the validity of the Short Physical Performance Battery (SPPB) for identifying mobility limitations in patients with COPD. To determine the clinical validity of the SPPB summary score and its three components (standing balance, 4-meter gait speed, and five-repetition sit-to-stand) for identifying mobility limitations in patients with COPD. This cross-sectional study included 137 patients with COPD, recruited from a hospital in Spain. Muscle strength tests and SPPB were measured; then, patients were surveyed for self-reported mobility limitations. The validity of SPPB scores was analyzed by developing receiver operating characteristic curves to analyze the sensitivity and specificity for identifying patients with mobility limitations; by examining group differences in SPPB scores across categories of mobility activities; and by correlating SPPB scores to strength tests. Only the SPPB summary score and the five-repetition sit-to-stand components showed good discriminative capabilities; both showed areas under the receiver operating characteristic curves greater than 0.7. Patients with limitations had significantly lower SPPB scores than patients without limitations in nine different mobility activities. SPPB scores were moderately correlated with the quadriceps test (r>0.40), and less correlated with the handgrip test (r<0.30), which reinforced convergent and divergent validities. A SPPB summary score cutoff of 10 provided the best accuracy for identifying mobility limitations. This study provided evidence for the validity of the SPPB summary score and the five-repetition sit-to-stand test for assessing mobility in patients with COPD. These tests also showed potential as a screening test for identifying patients with COPD that have mobility limitations.

The Short Physical Performance Battery is a discriminative tool for identifying patients with COPD at risk of disability

PubMed Central

Bernabeu-Mora, Roberto; Medina-Mirapeix, Françesc; Llamazares-Herrán, Eduardo; García-Guillamón, Gloria; Giménez-Giménez, Luz María; Sánchez-Nieto, Juan Miguel

2015-01-01

Background Limited mobility is a risk factor for developing chronic obstructive pulmonary disease (COPD)-related disabilities. Little is known about the validity of the Short Physical Performance Battery (SPPB) for identifying mobility limitations in patients with COPD. Objective To determine the clinical validity of the SPPB summary score and its three components (standing balance, 4-meter gait speed, and five-repetition sit-to-stand) for identifying mobility limitations in patients with COPD. Methods This cross-sectional study included 137 patients with COPD, recruited from a hospital in Spain. Muscle strength tests and SPPB were measured; then, patients were surveyed for self-reported mobility limitations. The validity of SPPB scores was analyzed by developing receiver operating characteristic curves to analyze the sensitivity and specificity for identifying patients with mobility limitations; by examining group differences in SPPB scores across categories of mobility activities; and by correlating SPPB scores to strength tests. Results Only the SPPB summary score and the five-repetition sit-to-stand components showed good discriminative capabilities; both showed areas under the receiver operating characteristic curves greater than 0.7. Patients with limitations had significantly lower SPPB scores than patients without limitations in nine different mobility activities. SPPB scores were moderately correlated with the quadriceps test (r>0.40), and less correlated with the handgrip test (r<0.30), which reinforced convergent and divergent validities. A SPPB summary score cutoff of 10 provided the best accuracy for identifying mobility limitations. Conclusion This study provided evidence for the validity of the SPPB summary score and the five-repetition sit-to-stand test for assessing mobility in patients with COPD. These tests also showed potential as a screening test for identifying patients with COPD that have mobility limitations. PMID:26664110
Cross-cultural adaptation of the German version of the spinal stenosis measure.

PubMed

Wertli, Maria M; Steurer, Johann; Wildi, Lukas M; Held, Ulrike

2014-06-01

To validate the German version of the spinal stenosis measure (SSM), a disease-specific questionnaire assessing symptom severity, physical function, and satisfaction with treatment in patients with lumbar spinal stenosis. After translation, cross-cultural adaptation, and pilot testing, we assessed internal consistency, test-retest reliability, construct validity, and responsiveness of the SSM subscales. Data from a large Swiss multi-center prospective cohort study were used. Reference scales for the assessment of construct validity and responsiveness were the numeric rating scale, pain thermometer, and the Roland Morris Disability Questionnaire. One hundred and eight consecutive patients were included in this validation study, recruited from five different centers. Cronbach's alpha was above 0.8 for all three subscales of the SSM. The objectivity of the SSM was assessed using a partial credit approach. The model showed a good global fit to the data. Of the 108 patients 78 participated in the test-retest procedure. The ICC values were above 0.8 for all three subscales of the SSM. Correlations with reference scales were above 0.7 for the symptom and function subscales. For satisfaction subscale, it was 0.66 or above. Clinically meaningful changes of the reference scales over time were associated with significantly more improvement in all three SSM subscales (p < 0.001). Conclusion: The proposed version of the SSM showed very good measurement properties and can be considered validated for use in the German language.
Cross-cultural evaluation of the French version of the LEIPAD, a health-related quality of life instrument for use in the elderly living at home.

PubMed

Jalenques, I; Auclair, C; Roblin, J; Morand, D; Tourtauchaux, R; May, R; Vaille-Perret, E; Watts, J; Gerbaud, L; De Leo, D

2013-04-01

To cross-culturally adapt a French version of the LEIPAD, a self-administered questionnaire assessing the health-related quality of life (HRQoL) in adults aged 65 years and over living at home, and to evaluate its psychometric properties. After having translated LEIPAD in accordance with guidelines, we studied psychometric properties: reliability and construct validity-factor analysis, relationships between items and scales, internal consistency, concurrent validity with the Medical Outcome Study Short-Form 36 and known-groups validity. The results obtained in a sample of 195 elderly from the general population showed very good acceptability, with response rates superior to 93 %. Exploratory factor analysis extracted eight factors providing a multidimensionality structure with five misclassifications of items in the seven theoretical scales. Good internal consistency (Cronbach's alpha ranging from 0.73 and 0.86) and strong test-retest reliability (ICCs higher than 0.80 for six scales and 0.70 for one) were demonstrated. Concurrent validity with the SF-36 showed small to strong expected correlations. This first evaluation of the French version of LEIPAD's psychometric properties provides evidence in construct validity and reliability. It would allow HRQoL assessment in clinical and common practice, and investigators would be able to take part in national and international research projects.
UTM Technical Capabilities Level 2 (TLC2) Test at Reno-Stead Airport.

NASA Image and Video Library

2016-10-06

Test of Unmanned Aircraft Systems Traffic Management (UTM) technical capability Level 2 (TCL2) at Reno-Stead Airport, Nevada. During the test, five drones simultaneously crossed paths, separated by altitude. Two drones flew beyond visual line-of-sight and three flew within line-of-sight of their operators. Engineer Joey Mercer reviews flight paths using the UAS traffic management research platform UTM coordinator app to verify and validate flight paths.
Predicting protein-binding RNA nucleotides with consideration of binding partners.

PubMed

Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

2015-06-01

In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures. To the best of our knowledge, this is the first sequence-based prediction of protein-binding nucleotides in RNA which considers the binding partner of RNA. The new model will provide valuable information for designing biochemical experiments to find putative protein-binding sites in RNA with unknown structure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Using deep learning for detecting gender in adult chest radiographs

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2018-03-01

In this paper, we present a method for automatically identifying the gender of an imaged person using their frontal chest x-ray images. Our work is motivated by the need to determine missing gender information in some datasets. The proposed method employs the technique of convolutional neural network (CNN) based deep learning and transfer learning to overcome the challenge of developing handcrafted features in limited data. Specifically, the method consists of four main steps: pre-processing, CNN feature extractor, feature selection, and classifier. The method is tested on a combined dataset obtained from several sources with varying acquisition quality resulting in different pre-processing steps that are applied for each. For feature extraction, we tested and compared four CNN architectures, viz., AlexNet, VggNet, GoogLeNet, and ResNet. We applied a feature selection technique, since the feature length is larger than the number of images. Two popular classifiers: SVM and Random Forest, are used and compared. We evaluated the classification performance by cross-validation and used seven performance measures. The best performer is the VggNet-16 feature extractor with the SVM classifier, with accuracy of 86.6% and ROC Area being 0.932 for 5-fold cross validation. We also discuss several misclassified cases and describe future work for performance improvement.
RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).
Penalized spline estimation for functional coefficient regression models.

PubMed

Cao, Yanrong; Lin, Haiqun; Wu, Tracy Z; Yu, Yan

2010-04-01

The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter λ, which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter λ: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty λ accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.
Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.

PubMed

Sadrawi, Muammar; Fan, Shou-Zen; Abbod, Maysam F; Jen, Kuo-Kuang; Shieh, Jiann-Shing

2015-01-01

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.
Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

PubMed Central

Sadrawi, Muammar; Fan, Shou-Zen; Abbod, Maysam F.; Jen, Kuo-Kuang; Shieh, Jiann-Shing

2015-01-01

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly. PMID:26568957
Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.
Carotenoid biosynthesis changes in five red pepper (Capsicum annuum L.) cultivars during ripening. Cultivar selection for breeding.

PubMed

Hornero-Méndez, D; Gómez-Ladrón De Guevara, R; Mínguez-Mosquera, M I

2000-09-01

Changes in the biosynthesis of individual carotenoid pigments have been investigated during fruit ripening of five cultivars of red pepper (Capsicum annuum L.): Mana, Numex, Belrubi, Delfin, and Negral (a chlorophyll-retaining mutant when ripe). The study was carried out throughout the ripening process, and with special emphasis on the ripe stage, to discover possible differences between cultivars and to characterize these by their carotenoid pattern and content for selecting the best varieties for breeding programs. Ripening fruit of the five cultivars showed the typical and characteristic pattern of carotenoid biosynthesis for the Capsicum genus. In the five cultivars, lutein and neoxanthin, both characteristic chloroplast pigments, decreased in concentration with ripening and eventually disappeared. beta-Carotene, antheraxanthin, and violaxanthin increased in concentration, and other pigments were biosynthesized de novo: zeaxanthin, beta-cryptoxanthin, capsanthin, capsorubin, capsanthin-5,6-epoxide, and cucurbitaxanthin A. A pool of zeaxanthin stands out of the rest of pigment during ripening, which reveals the importance of this pigment as a branching point in the carotenoid biosynthesis in Capsicum. Quantitatively, Negral cultivar showed the highest increase in total carotenoid content (48. 39-fold), followed by Mana and Delfin with 38.03- and 36.8-fold, respectively, and by Belrubi and Numex with 28.03- and 23.48-fold, respectively. In all the red varieties, there was an inverse relationship between total carotenoid content and the red to yellow isochromic pigment fraction ratio (R/Y) and the capsanthin-to-zeaxanthin ratio (Caps/Zeax). This seems to be related to the carotenogenic capacity of the cultivar, and thus selection and breeding should not only seek a higher total carotenoid content but also attempt to increase these ratios. In the present study, the cultivar Mana had the highest total carotenoid content (13 208 mg/kg dwt), but the lowest R/Y (1.25) and Caps/Zeax (3.38) ratios, which are therefore the parameters to improve. The cultivar Negral had a high carotenoid content (8797 mg/kg dwt) and high R/Y and Caps/Zeax ratios and could be used for transfer of these characters in direct crosses with the cultivar Mana. The cultivar Numex had the highest Caps/Zeax ratio (7.17) and is thus an ideal progenitor for this character.
Beluga whale liver microsomal cytochrome P4501A (CYP1A) enzymes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bullock, P.L.; Addison, R.; Lockhart, L.

1995-12-31

Beluga whale (Delphinapterus leucas) liver from the Canadian arctic was analyzed for the presence of CYP1A enzymes, as part of current studies on biomarkers for environmental contamination. CYP1A1-associated 7-ethoxyresorufin O-dealkylase activity (EROD) varied 13 fold among sixteen male whale liver microsomal samples and 31 fold among five females. Similarly, the rate of 7-methoxyresorufin O-dealkylation (MROD) varied 7 fold and 3 fold in microsomal samples from males and females, respectively. Furthermore, 7-pentoxyresorufin O-dealkylase activity (PROD) varied 10 fold in both sexes. None of these enzyme activities were sexually differentiated, and EROD and MROD were inhibited by {alpha}-naphthoflavone. There was very goodmore » correlation between EROD and MROD (r{sup 2} = .894), EROD and PROD (r{sup 2} = .909), but MROD and PROD were not as well correlated (r{sup 2} = 785). On Western immunoblots, a single band was recognized in Beluga whale liver microsomes by a polygonal antibody raised against an oligopeptide related to trout CYP1A1. This antibody also recognized purified rat CYP1A1 (56 kDa) and stained only one band (56 kDa) in liver microsomes isolated from male rats treated with {beta}-naphthoflavone. The interindividual variation in EROD paralleled differences in the amount of whale liver microsomal protein that cross-reacted with the anti-peptide antibody. The results suggest that Beluga whale liver contains at least one CYP1A enzyme which catalyzes the 0-dealkylation of 7-ethoxy, 7-methoxy and 7-pentoxyresorufin and has a molecular weight less than that of rat CYP1A1, but similar to rat CYP1A2 (52 kDa).« less
γ production and neutron inelastic scattering cross sections for 76Ge

NASA Astrophysics Data System (ADS)

Rouki, C.; Domula, A. R.; Drohé, J. C.; Koning, A. J.; Plompen, A. J. M.; Zuber, K.

2013-11-01

The 2040.7-keV γ ray from the 69th excited state of 76Ge was investigated in the interest of Ge-based double-β-decay experiments like the Germanium Detector Array (GERDA) experiment. The predicted transition could interfere with valid 0νββ events at 2039.0 keV, creating false signals in large-volume 76Ge enriched detectors. The measurement was performed with the Gamma Array for Inelastic Neutron Scattering (GAINS) at the Geel Electron Linear Accelerator (GELINA) white neutron source, using the (n,n'γ) technique and focusing on the strongest γ rays originating from the level. Upper limits obtained for the production cross section of the 2040.7-keV γ ray showed no possible influence on GERDA data. Additional analysis of the data yielded high-resolution cross sections for the low-lying states of 76Ge and related γ rays, improving the accuracy and extending existing data for five transitions and five levels. The inelastic scattering cross section for 76Ge was determined for incident neutron energies up to 2.23 MeV, significantly increasing the energy range for which experimental data are available. Comparisons with model calculations using the talys code are presented indicating that accounting for the recently established asymmetric rotor structure should lead to an improved description of the data.
Twofold processing for denoising ultrasound medical images.

PubMed

Kishore, P V V; Kumar, K V V; Kumar, D Anil; Prasad, M V D; Goutham, E N D; Rahul, R; Krishna, C B S Vamsi; Sandeep, Y

2015-01-01

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.
The Structure of a BamA-BamD Fusion Illuminates the Architecture of the β-Barrel Assembly Machine Core.

PubMed

Bergal, Hans Thor; Hopkins, Alex Hunt; Metzner, Sandra Ines; Sousa, Marcelo Carlos

2016-02-02

The β-barrel assembly machine (BAM) mediates folding and insertion of integral β-barrel outer membrane proteins (OMPs) in Gram-negative bacteria. Of the five BAM subunits, only BamA and BamD are essential for cell viability. Here we present the crystal structure of a fusion between BamA POTRA4-5 and BamD from Rhodothermus marinus. The POTRA5 domain binds BamD between its tetratricopeptide repeats 3 and 4. The interface structural elements are conserved in the Escherichia coli proteins, which allowed structure validation by mutagenesis and disulfide crosslinking in E. coli. Furthermore, the interface is consistent with previously reported mutations that impair BamA-BamD binding. The structure serves as a linchpin to generate a BAM model where POTRA domains and BamD form an elongated periplasmic ring adjacent to the membrane with a central cavity approximately 30 × 60 Å wide. We propose that nascent OMPs bind this periplasmic ring prior to insertion and folding by BAM. Copyright © 2016 Elsevier Ltd. All rights reserved.
Placental genome and maternal-placental genetic interactions: a genome-wide and candidate gene association study of placental abruption.

PubMed

Denis, Marie; Enquobahrie, Daniel A; Tadesse, Mahlet G; Gelaye, Bizu; Sanchez, Sixto E; Salazar, Manuel; Ananth, Cande V; Williams, Michelle A

2014-01-01

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina's Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8-12.56) and a 4.46-fold (95% CI: 2.94-6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3:12313450 and chr3:12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.
Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

PubMed Central

Stikic, Maja; Berka, Chris; Levendowski, Daniel J.; Rubio, Roberto F.; Tan, Veasna; Korszen, Stephanie; Barba, Douglas; Wurzer, David

2014-01-01

The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN) was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make “deadly force decisions” in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects' performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback), and applied across a variety of training environments. PMID:25414629
Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation.

PubMed

Saatchi, Mahdi; McClure, Mathew C; McKay, Stephanie D; Rolf, Megan M; Kim, JaeWoo; Decker, Jared E; Taxis, Tasia M; Chapple, Richard H; Ramey, Holly R; Northcutt, Sally L; Bauck, Stewart; Woodward, Brent; Dekkers, Jack C M; Fernando, Rohan L; Schnabel, Robert D; Garrick, Dorian J; Taylor, Jeremy F

2011-11-28

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.
Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

PubMed Central

2011-01-01

Background Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy. PMID:22122853

Changes in Gene Expression Predicting Local Control in Cervical Cancer: Results from Radiation Therapy Oncology Group 0128

PubMed Central

Weidhaas, Joanne B.; Li, Shu-Xia; Winter, Kathryn; Ryu, Janice; Jhingran, Anuja; Miller, Bridgette; Dicker, Adam P.; Gaffney, David

2009-01-01

Purpose To evaluate the potential of gene expression signatures to predict response to treatment in locally advanced cervical cancer treated with definitive chemotherapy and radiation. Experimental Design Tissue biopsies were collected from patients participating in Radiation Therapy Oncology Group (RTOG) 0128, a phase II trial evaluating the benefit of celecoxib in addition to cisplatin chemotherapy and radiation for locally advanced cervical cancer. Gene expression profiling was done and signatures of pretreatment, mid-treatment (before the first implant), and “changed” gene expression patterns between pre- and mid-treatment samples were determined. The ability of the gene signatures to predict local control versus local failure was evaluated. Two-group t test was done to identify the initial gene set separating these end points. Supervised classification methods were used to enrich the gene sets. The results were further validated by leave-one-out and 2-fold cross-validation. Results Twenty-two patients had suitable material from pretreatment samples for analysis, and 13 paired pre- and mid-treatment samples were obtained. The changed gene expression signatures between the pre- and mid-treatment biopsies predicted response to treatment, separating patients with local failures from those who achieved local control with a seven-gene signature. The in-sample prediction rate, leave-one-out prediction rate, and 2-fold prediction rate are 100% for this seven-gene signature. This signature was enriched for cell cycle genes. Conclusions Changed gene expression signatures during therapy in cervical cancer can predict outcome as measured by local control. After further validation, such findings could be applied to direct additional therapy for cervical cancer patients treated with chemotherapy and radiation. PMID:19509178
Viscoelasticity of rabbit vocal folds after injection augmentation.

PubMed

Dahlqvist, Ake; Gärskog, Ola; Laurent, Claude; Hertegård, Stellan; Ambrosio, Luigi; Borzacchiello, Assunta

2004-01-01

Vocal fold function is related to the viscoelasticity of the vocal fold tissue. Augmentation substances used for injection treatment of voice insufficiency may alter the viscoelastic properties of vocal folds and their vibratory capacity. The objective was to compare the mechanical properties (viscoelasticity) of various injectable substances and the viscoelasticity of rabbit vocal folds, 6 months after injection with one of these substances. Animal model. Cross-linked collagen (Zyplast), double cross-linked hyaluronan (hylan B gel), dextranomers in hyaluronan (DHIA), and polytetrafluoroethylene (Teflon) were injected into rabbit vocal folds. Six months after the injection, the animals were killed and the right- and left-side vocal folds were removed. Dynamic viscosity of the injected substances and the vocal folds was measured with a Bohlin parallel-plate rheometer during small-amplitude oscillation. All injected vocal folds showed a decreasing dynamic viscosity with increasing frequency. Hylan B gel and DiHA showed the lowest dynamic viscosity values, and vocal folds injected with these substances also showed the lowest dynamic viscosity (similar to noninjected control samples). Teflon (and vocal folds injected with Teflon) showed the highest dynamic viscosity values, followed by the collagen samples. Substances with low viscoelasticity alter the mechanical properties of the vocal fold to a lesser degree than substances with a high viscoelasticity. The data indicated that hylan B gel and DiHA render the most natural viscoelastic properties to the vocal folds. These substances seem to be appropriate for preserving or restoring the vibratory capacity of the vocal folds when glottal insufficiency is treated with augmentative injections.
Validation of the PROMIS® measures of self-efficacy for managing chronic conditions.

PubMed

Gruber-Baldini, Ann L; Velozo, Craig; Romero, Sergio; Shulman, Lisa M

2017-07-01

The Patient-Reported Outcomes Measurement Information System ® (PROMIS ® ) was designed to develop, validate, and standardize item banks to measure key domains of physical, mental, and social health in chronic conditions. This paper reports the calibration and validation testing of the PROMIS Self-Efficacy for Managing Chronic Conditions measures. PROMIS Self-Efficacy for Managing Chronic Conditions item banks comprise five domains, Self-Efficacy for Managing: Daily Activities, Symptoms, Medications and Treatments, Emotions, and Social Interactions. Banks were calibrated in 1087 subjects from two data sources: 837 patients with chronic neurologic conditions (epilepsy, multiple sclerosis, neuropathy, Parkinson disease, and stroke) and 250 subjects from an online Internet sample of adults with general chronic conditions. Scores were compared with one legacy scale: Self-Efficacy for Managing Chronic Disease 6-Item scale (SEMCD6) and five PROMIS short forms: Global Health (Physical and Mental), Physical Function, Fatigue, Depression, and Anxiety. The sample was 57% female, mean age = 53.8 (SD = 14.7), 76% white, 21% African American, 6% Hispanic, and 76% with greater than high school education. Full-item banks were created for each domain. All measures had good internal consistency and correlated well with SEMCD6 (r = 0.56-0.75). Significant correlations were seen between the Self-Efficacy measures and other PROMIS short forms (r > 0.38). The newly developed PROMIS Self-Efficacy for Managing Chronic Conditions measures include five domains of self-efficacy that were calibrated across diverse chronic conditions and show good internal consistency and cross-sectional validity.
Partial wave analysis for folded differential cross sections

NASA Astrophysics Data System (ADS)

Machacek, J. R.; McEachran, R. P.

2018-03-01

The value of modified effective range theory (MERT) and the connection between differential cross sections and phase shifts in low-energy electron scattering has long been recognized. Recent experimental techniques involving magnetically confined beams have introduced the concept of folded differential cross sections (FDCS) where the forward (θ ≤ π/2) and backward scattered (θ ≥ π/2) projectiles are unresolved, that is the value measured at the angle θ is the sum of the signal for particles scattered into the angles θ and π - θ. We have developed an alternative approach to MERT in order to analyse low-energy folded differential cross sections for positrons and electrons. This results in a simplified expression for the FDCS when it is expressed in terms of partial waves and thereby enables one to extract the first few phase shifts from a fit to an experimental FDCS at low energies. Thus, this method predicts forward and backward angle scattering (0 to π) using only experimental FDCS data and can be used to determine the total elastic cross section solely from experimental results at low-energy, which are limited in angular range.
Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Wei, Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun; Zhang, Yiheng

2006-03-01

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.
Development of Short-Form Versions of the Screener and Opioid Assessment for Patients with Pain-Revised (SOAPP-R): A Proof-of-Principle Study.

PubMed

Finkelman, Matthew D; Smits, Niels; Kulich, Ronald J; Zacharoff, Kevin L; Magnuson, Britta E; Chang, Hong; Dong, Jinghui; Butler, Stephen F

2017-07-01

The Screener and Opioid Assessment for Patients with Pain-Revised (SOAPP-R) is a 24-item questionnaire designed to assess risk of aberrant medication-related behaviors in chronic pain patients. The introduction of short forms of the SOAPP-R may save time and increase utilization by practitioners. To develop and evaluate candidate SOAPP-R short forms. Retrospective study. Pain centers. Four hundred and twenty-eight patients with chronic noncancer pain. Subjects had previously been administered the full-length version of the SOAPP-R and been categorized as positive or negative for aberrant medication-related behaviors via the Aberrant Drug Behavior Index (ADBI). Short forms of the SOAPP-R were developed using lasso logistic regression. Sensitivity, specificity, and area under the curve (AUC) of all forms were calculated with respect to the ADBI using the complete data set, training-test analysis, and 10-fold cross-validation. The coefficient alpha of each form was also calculated. An external set of 12 pain practitioners reviewed the forms for content. In the complete data set analysis, a form of 12 items exhibited sensitivity, specificity, and AUC greater than or equal to those of the full-length SOAPP-R (which were 0.74, 0.67, and 0.76, respectively). The short form had a coefficient alpha of 0.76. In the training-test analysis and 10-fold cross-validation, it exhibited an AUC value within 0.01 of that of the full-length SOAPP-R. The majority of external practitioners reported a preference for this short form. The 12-item version of the SOAPP-R has potential as a short risk screener and should be tested prospectively. © 2016 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information.

PubMed

Chen, Gongbo; Knibbs, Luke D; Zhang, Wenyi; Li, Shanshan; Cao, Wei; Guo, Jianping; Ren, Hongyan; Wang, Boguang; Wang, Hao; Williams, Gail; Hamm, N A S; Guo, Yuming

2018-02-01

PM 1 might be more hazardous than PM 2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM 1 concentrations and its health effects are limited due to a lack of PM 1 monitoring data. To estimate spatial and temporal variations of PM 1 concentrations in China during 2005-2014 using satellite remote sensing, meteorology, and land use information. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM 1 data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability. The results of 10-fold cross-validation showed R 2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m 3 , respectively. For seasonal prediction, the R 2 and RMSE were 77% and 11.4 μg/m 3 , respectively. The predicted annual mean concentration of PM 1 across China was 26.9 μg/m 3 . The PM 1 level was highest in winter while lowest in summer. Generally, the PM 1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM 1 levels increased substantially in the South-Western Hebei and Beijing-Tianjin region. GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM 1 . Ambient PM 1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM 1 . Copyright © 2017 Elsevier Ltd. All rights reserved.
Automatic identification of inertial sensor placement on human body segments during walking

PubMed Central

2013-01-01

Background Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided. We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Methods Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). Results and conclusions After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method. PMID:23517757
Automatic identification of inertial sensor placement on human body segments during walking.

PubMed

Weenk, Dirk; van Beijnum, Bert-Jan F; Baten, Chris T M; Hermens, Hermie J; Veltink, Peter H

2013-03-21

Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.
Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

PubMed

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.
SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, W; Tu, S

Purpose: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. Methods: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy. The specimens were classified by experienced pathologists with a B (benign) or M (malignant). CT images with slice thickness ofmore » 0.625 mm were acquired from a GE BrightSpeed 16 scanner. The study was formally approved by our institutional internal review board. Nodules were delineated and 374 feature parameters were extracted from IBEX. We first used the t-test and p-value criteria to study which feature can differentiate between group B and M. Then we implemented a logistic regression algorithm to perform nodule malignancy classification. 10-fold cross-validation and the receiver operating characteristic curve (ROC) were used to evaluate the classification accuracy. Finally hierarchical clustering analysis, Spearman rank correlation coefficient, and clustering heat map were used to further study correlation characteristics among different features. Results: 238 features were found differentiable between group B and M based on whether their statistical p-values were less than 0.05. A forward search algorithm was used to select an optimal combination of features for the best classification and 9 features were identified. Our study found the best accuracy of classifying malignancy was 0.79±0.01 with the 10-fold cross-validation. The area under the ROC curve was 0.81±0.02. Conclusion: Benign nodules may be treated as a malignant tumor in low-dose CT and patients may undergo unnecessary surgeries or treatments. Our study may help radiologists to differentiate nodule malignancy for low-dose CT.« less
Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

PubMed

Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

2016-12-01

Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p < 0.01, representing 43% and 53% variations from ESMO/NCCN guidelines, respectively). Using ten-fold cross-validation, the best classifiers achieved areas under the receiver operating characteristic curve (AUC) of 0.940 for chemotherapy (95% C.I., 0.922-0.958), 0.899 for the endocrine therapy (95% C.I., 0.880-0.918), and 0.977 for trastuzumab therapy (95% C.I., 0.955-0.999) respectively. Overall, bootstrap aggregated classifiers performed better among all evaluated machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.
Bedload transport in a river confluence

NASA Astrophysics Data System (ADS)

Martín-Vide, J. P.; Plana-Casado, A.; Sambola, A.; Capapé, S.

2015-12-01

The confluence of the regulated Toltén River and its tributary the unregulated Allipén (south of Chile) has proved dynamic in the last decade. Daily bedload measurements with a Helley-Smith sampler, bed surveys, and grain-size distributions of the two rivers are obtained from a field campaign that lasts 3 months in high-flow season. The goals are to quantify total bedload and to understand the balance between tributary and main river and the bedload distribution in space and texture. The bedload transport varies 200-fold, with a maximum of 5000 t/day. The discharge varies five-fold, with a maximum of 900 m3/s. Two-thirds of the total bedload volume are transported through the deeper area of the cross section and gravel is predominant (64%). Average bedload volumes in the confluence seem unbalanced in favour of the tributary. Main river bedload transport is predominantly at below-capacity conditions, while the tributary bedload transport is at-capacity conditions. This is deemed the main reason of inaccuracy of the bedload predictors. The roles of entrainment into suspension, helical flow, partial transport, and mobile armour are discussed.
Reducing respiratory motion artifacts in positron emission tomography through retrospective stacking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thorndyke, Brian; Schreibmann, Eduard; Koong, Albert

Respiratory motion artifacts in positron emission tomography (PET) imaging can alter lesion intensity profiles, and result in substantially reduced activity and contrast-to-noise ratios (CNRs). We propose a corrective algorithm, coined 'retrospective stacking' (RS), to restore image quality without requiring additional scan time. Retrospective stacking uses b-spline deformable image registration to combine amplitude-binned PET data along the entire respiratory cycle into a single respiratory end point. We applied the method to a phantom model consisting of a small, hot vial oscillating within a warm background, as well as to {sup 18}FDG-PET images of a pancreatic and a liver patient. Comparisons weremore » made using cross-section visualizations, activity profiles, and CNRs within the region of interest. Retrospective stacking was found to properly restore the lesion location and intensity profile in all cases. In addition, RS provided CNR improvements up to three-fold over gated images, and up to five-fold over ungated data. These phantom and patient studies demonstrate that RS can correct for lesion motion and deformation, while substantially improving tumor visibility and background noise.« less
Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.
A contact photo-cross-linking investigation of the active site of the 8-17 deoxyribozyme.

PubMed

Liu, Yong; Sen, Dipankar

2008-09-12

The small RNA-cleaving 8-17 deoxyribozyme (DNAzyme) has been the subject of extensive mechanistic and structural investigation, including a number of recent single-molecule studies of its global folding. Little detailed insight exists, however, into this DNAzyme's active site; for instance, the identity of specific nucleotides that are proximal to or in contact with the scissile site in the substrate. Here, we report a systematic replacement of a number of bases within the magnesium-folded DNAzyme-substrate complex with thio- and halogen-substituted base analogues, which were then photochemically activated to generate contact cross-links within the complex. Mapping of the cross-links revealed a striking pattern of DNAzyme-substrate cross-links but an absence of significant intra-DNAzyme cross-links. Notably, the two nucleotides directly flanking the scissile phosphodiester cross-linked strongly with functionally important elements within the DNAzyme, the thymine of a G.T wobble base pair, a WCGR bulge loop, and a terminal AGC loop. Mutation of the wobble base pair to a G-C pair led to a significant folding instability of the DNAzyme-substrate complex. The cross-linking patterns obtained were used to generate a model for the DNAzyme's active site that had the substrate's scissile phosphodiester sandwiched between the DNAzyme's wobble thymine and its AGC and WCGR loops.
Development, content validity, and cross-cultural adaptation of a patient-reported outcome measure for real-time symptom assessment in irritable bowel syndrome.

PubMed

Vork, L; Keszthelyi, D; Mujagic, Z; Kruimel, J W; Leue, C; Pontén, I; Törnblom, H; Simrén, M; Albu-Soda, A; Aziz, Q; Corsetti, M; Holvoet, L; Tack, J; Rao, S S; van Os, J; Quetglas, E G; Drossman, D A; Masclee, A A M

2018-03-01

End-of-day questionnaires, which are considered the gold standard for assessing abdominal pain and other gastrointestinal (GI) symptoms in irritable bowel syndrome (IBS), are influenced by recall and ecological bias. The experience sampling method (ESM) is characterized by random and repeated assessments in the natural state and environment of a subject, and herewith overcomes these limitations. This report describes the development of a patient-reported outcome measure (PROM) based on the ESM principle, taking into account content validity and cross-cultural adaptation. Focus group interviews with IBS patients and expert meetings with international experts in the fields of neurogastroenterology & motility and pain were performed in order to select the items for the PROM. Forward-and-back translation and cognitive interviews were performed to adapt the instrument for the use in different countries and to assure on patients' understanding with the final items. Focus group interviews revealed 42 items, categorized into five domains: physical status, defecation, mood and psychological factors, context and environment, and nutrition and drug use. Experts reduced the number of items to 32 and cognitive interviewing after translation resulted in a few slight adjustments regarding linguistic issues, but not regarding content of the items. An ESM-based PROM, suitable for momentary assessment of IBS symptom patterns was developed, taking into account content validity and cross-cultural adaptation. This PROM will be implemented in a specifically designed smartphone application and further validation in a multicenter setting will follow. © 2017 John Wiley & Sons Ltd.
Prediction and Cross-Situational Consistency of Daily Behavior across Cultures: Testing Trait and Cultural Psychology Perspectives

PubMed Central

Church, A. Timothy; Katigbak, Marcia S.; Reyes, Jose Alberto S.; Salanga, Maria Guadalupe C.; Miramontes, Lilia A.; Adams, Nerissa B.

2008-01-01

Trait and cultural psychology perspectives on the cross-situational consistency of behavior, and the predictive validity of traits, were tested in a daily process study in the United States (N = 68), an individualistic culture, and the Philippines (N = 80), a collectivistic culture. Participants completed the Revised NEO Personality Inventory (Costa & McCrae, 1992) and a measure of self-monitoring, then reported their daily behaviors and associated situational contexts for approximately 30 days. Consistent with trait perspectives, the Big Five traits predicted daily behaviors in both cultures, and relative (interindividual) consistency was observed across many, although not all, situational contexts. The frequency of various Big Five behaviors varied across relevant situational contexts in both cultures and, consistent with cultural psychology perspectives, there was a tendency for Filipinos to exhibit greater situational variability than Americans. Self-monitoring showed some ability to account for individual differences in situational variability in the American sample, but not the Filipino sample. PMID:22146866
An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

PubMed Central

Brender, Jeffrey R.; Czajka, Jeff; Marsh, David; Gray, Felicia; Cierpicki, Tomasz; Zhang, Yang

2013-01-01

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality. PMID:24204234
Sprague-Dawley rats display sex-linked differences in the pharmacokinetics of 3,4-methylenedioxymethamphetamine (MDMA) and its metabolite 3,4-methylenedioxyamphetamine (MDA)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fonsart, Julien, E-mail: julien.fonsart@lrb.aphp.f; CNRS, UMR 7157, Paris F-75006; INSERM, U705, Paris F-75006

The use of 3,4-methylenedioxymethamphetamine (MDMA, ecstasy) has increased in recent years; it can lead to life-threatening hyperthermia and serotonin syndrome. Human and rodent males appear to be more sensitive to acute toxicity than are females. MDMA is metabolized to five main metabolites by the enzymes CYP1A2, CYP2D and COMT. Little is presently known about sex-dependent differences in the pharmacokinetics of MDMA and its metabolites. We therefore analyzed MDMA disposition in male and female rats by measuring the plasma and urine concentrations of MDMA and its metabolites using a validated LC-MS method. MDA AUC{sub last} and C{sub max} were 1.6- tomore » 1.7-fold higher in males than in females given MDMA (5 mg/kg sc), while HMMA C{sub max} and AUC{sub last} were 3.2- and 3.5-fold higher, respectively. MDMA renal clearance was 1.26-fold higher in males, and that of MDA was 2.2-fold higher. MDMA AUC{sub last} and t{sub 1/2} were 50% higher in females given MDMA (1 mg/kg iv). MDA C{sub max} and AUC{sub last} were 75-82% higher in males, with a 2.8-fold higher metabolic index. Finally, the AUC{sub last} of MDA was 0.73-fold lower in males given 1 mg/kg iv MDA. The volumes of distribution of MDMA and MDA at steady-state were similar in the two sexes. These data strongly suggest that differences in the N-demethylation of MDMA to MDA are major influences on the MDMA and MDA pharmacokinetics in male and female rats. Hence, males are exposed to significantly more toxic MDA, which could explain previously reported sexual dysmorphism in the acute effects and toxicity of MDMA in rats.« less

Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Rukmana, S. H.; Sugiharti, E.; Prasetiyo, B.; Alimah, S.

2018-03-01

Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.
Predictive modeling of addiction lapses in a mobile health application.

PubMed

Chih, Ming-Yuan; Patton, Timothy; McTavish, Fiona M; Isham, Andrew J; Judkins-Fisher, Chris L; Atwood, Amy K; Gustafson, David H

2014-01-01

The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-comprehensive health enhancement support system (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients' recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support. © 2013.
Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals

PubMed Central

Yin, Jinghai; Mu, Zhendong

2016-01-01

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors’ main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue. PMID:28529761
Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals.

PubMed

Yin, Jinghai; Hu, Jianfeng; Mu, Zhendong

2017-02-01

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.
Breast Cancer Detection with Reduced Feature Set.

PubMed

Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

2015-01-01

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.
Predictive Modeling of Addiction Lapses in a Mobile Health Application

PubMed Central

Chih, Ming-Yuan; Patton, Timothy; McTavish, Fiona M.; Isham, Andrew; Judkins-Fisher, Chris L.; Atwood, Amy K.; Gustafson, David H.

2013-01-01

The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-Comprehensive Health Enhancement Support System (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients’ recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support. PMID:24035143
Tribology of alternative bearings.

PubMed

Fisher, John; Jin, Zhongmin; Tipper, Joanne; Stone, Martin; Ingham, Eileen

2006-12-01

The tribological performance and biological activity of the wear debris produced has been compared for highly cross-linked polyethylene, ceramic-on-ceramic, metal-on-metal, and modified metal bearings in a series of in vitro studies from a single laboratory. The functional lifetime demand of young and active patients is 10-fold greater than the estimated functional lifetime of traditional polyethylene. There is considerable interest in using larger diameter heads in these high demand patients. Highly cross-linked polyethylene show a four-fold reduction in functional biological activity. Ceramic-on-ceramic bearings have the lowest wear rates and least reactive wear debris. The functional biological activity is 20-fold lower than with highly cross-linked polyethylene. Hence, ceramic-on-ceramic bearings address the tribological lifetime demand of highly active patients. Metal-on-metal bearings have substantially lower wear rates than highly cross-linked polyethylene and wear decreases with head diameter. Bedding in wear is also lower with reduced radial clearance. Differential hardness ceramic-on-metal bearings and the application of ceramic-like coatings reduce metal wear and ion levels.
Cross-Cultural Adaptation, Reliability and Validity of the Danish Version of the Readiness for Return to Work Instrument.

PubMed

Stapelfeldt, Christina Malmose; Momsen, Anne-Mette Hedeager; Lund, Thomas; Grønborg, Therese Koops; Hogg-Johnson, Sheilah; Jensen, Chris; Skakon, Janne; Labriola, Merete

2018-06-06

The objective of the present study was to translate and validate the Canadian Readiness for Return To Work instrument (RRTW-CA) into a Danish version (RRTWDK) by testing its test-retest and internal consistency reliability and its structural and construct validity. Cross-cultural adaptation of the six-staged RRTW-CA instrument was performed in a standardised, systematic five-step-procedure; forward translation, panel synthesis of the translation, back translation, consolidation and revision by researchers, and finally pre-testing. This RRTW-DK beta-version was tested for its psychometric properties by intra-class correlation coefficient and standard error of measurement (n = 114), Cronbach's alpha (n = 471), confirmatory factor analyses (n = 373), and Spearman's rank correlation coefficient (n = 436) in sickness beneficiaries from a municipal employment agency and hospital wards. The original RRTW-CA stage structure could not be confirmed in the RRTWDK. The psychometric properties were thus inconclusive. The RRTW-DK cannot be recommended for use in the current version as the RRTW construct is questionable. The RRTW construct needs further exploration, preferably in a population that is homogeneous with regard to cause of sickness, disability duration and age.
Improvement on a simplified model for protein folding simulation.

PubMed

Zhang, Ming; Chen, Changjun; He, Yi; Xiao, Yi

2005-11-01

Improvements were made on a simplified protein model--the Ramachandran model-to achieve better computer simulation of protein folding. To check the validity of such improvements, we chose the ultrafast folding protein Engrailed Homeodomain as an example and explored several aspects of its folding. The engrailed homeodomain is a mainly alpha-helical protein of 61 residues from Drosophila melanogaster. We found that the simplified model of Engrailed Homeodomain can fold into a global minimum state with a tertiary structure in good agreement with its native structure.
Development and validation of a knowledge test for health professionals regarding lifestyle modification.

PubMed

Talip, Whadi-ah; Steyn, Nelia P; Visser, Marianne; Charlton, Karen E; Temple, Norman

2003-09-01

We wanted to develop and validate a test that assesses the knowledge and practices of health professionals (HPs) with regard to the role of nutrition, physical activity, and smoking cessation (lifestyle modification) in chronic diseases of lifestyle. A descriptive cross-sectional validation study was carried out. The validation design consisted of two phases, namely 1) test planning and development and 2) test evaluation. The study sample consisted of five groups of HPs: dietitians, dietetic interns, general practitioners, medical students, and nurses. The overall response rate was 58%, resulting in a sample size of 186 participants. A test was designed to evaluate the knowledge and practices of HPs. The test was first evaluated by an expert group to ensure content, construct, and face validity. Thereafter, the questionnaire was tested on five groups of HPs to test for criterion validity. Internal consistency was evaluated by Cronbach's alpha. An expert panel ensured content, construct, and face validity of the test. Groups with the most training and exposure to nutrition (dietitians and dietetic interns) had the highest group mean score, ranging from 61% to 88%, whereas those with limited nutrition training (general practitioners, medical students, and nurses) had significantly lower scores, ranging from 26% to 80%. This result demonstrated criterion validity. Internal consistency of the overall test demonstrated a Cronbach's alpha of 0.99. Most HPs identified the mass media as their main source of information on lifestyle modification. These HPs also identified lack of time, lack of patient compliance, and lack of knowledge as barriers that prevent them from providing counseling on lifestyle modification. The results of this study showed that this test instrument identifies groups of health professionals with adequate training (knowledge) in lifestyle modification and those who require further training (knowledge).
Development and validation of a high-fidelity phonomicrosurgical trainer.

PubMed

Klein, Adam M; Gross, Jennifer

2017-04-01

To validate the use of a high-fidelity phonomicrosurgical trainer. A high-fidelity phonomicrosurgical trainer, based on a previously validated model by Contag et al., 1 was designed with multilayered vocal folds that more closely mimic the consistency of true vocal folds, containing intracordal lesions to practice phonomicrosurgical removal. A training module was developed to simulate the true phonomicrosurgical experience. A validation study with novice and expert surgeons was conducted. Novices and experts were instructed to remove the lesion from the synthetic vocal folds, and novices were given four training trials. Performances were measured by the amount of time spent and tissue injury (microflap, superficial, deep) to the vocal fold. An independent Student t test and Fisher exact tests were used to compare subjects. A matched-paired t test and Wilcoxon signed rank tests were used to compare novice performance on the first and fourth trials and assess for improvement. Experts completed the excision with less total errors than novices (P = .004) and made less injury to the microflap (P = .05) and superficial tissue (P = .003). Novices improved their performance with training, making less total errors (P = .002) and superficial tissue injuries (P = .02) and spending less time for removal (P = .002) after several practice trials. This high-fidelity phonomicrosurgical trainer has been validated for novice surgeons. It can distinguish between experts and novices; and after training, it helped to improve novice performance. N/A. Laryngoscope, 127:888-893, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.
Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.
DNA motifs associated with aberrant CpG island methylation.

PubMed

Feltus, F Alex; Lee, Eva K; Costello, Joseph F; Plass, Christoph; Vertino, Paula M

2006-05-01

Epigenetic silencing involving the aberrant methylation of promoter region CpG islands is widely recognized as a tumor suppressor silencing mechanism in cancer. However, the molecular pathways underlying aberrant DNA methylation remain elusive. Recently we showed that, on a genome-wide level, CpG island loci differ in their intrinsic susceptibility to aberrant methylation and that this susceptibility can be predicted based on underlying sequence context. These data suggest that there are sequence/structural features that contribute to the protection from or susceptibility to aberrant methylation. Here we use motif elicitation coupled with classification techniques to identify DNA sequence motifs that selectively define methylation-prone or methylation-resistant CpG islands. Motifs common to 28 methylation-prone or 47 methylation-resistant CpG island-containing genomic fragments were determined using the MEME and MAST algorithms (). The five most discriminatory motifs derived from methylation-prone sequences were found to be associated with CpG islands in general and were nonrandomly distributed throughout the genome. In contrast, the eight most discriminatory motifs derived from the methylation-resistant CpG islands were randomly distributed throughout the genome. Interestingly, this latter group tended to associate with Alu and other repetitive sequences. Used together, the frequency of occurrence of these motifs successfully discriminated methylation-prone and methylation-resistant CpG island groups with an accuracy of 87% after 10-fold cross-validation. The motifs identified here are candidate methylation-targeting or methylation-protection DNA sequences.
Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Langdon-Embry, Liana; Escalon, Joanna G.; Allen, Peter J.; Lowery, Maeve A.; O'Reilly, Eileen M.; Do, Richard K. G.; Simpson, Amber L.

2016-03-01

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the United States. The five-year survival rate for all stages is approximately 6%, and approximately 2% when presenting with distant disease.1 Only 10-20% of all patients present with resectable disease, but recurrence rates are high with only 5 to 15% remaining free of disease at 5 years. At this time, we are unable to distinguish between resectable PDAC patients with occult metastatic disease from those with potentially curable disease. Early classification of these tumor types may eventually lead to changes in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant treatments. Texture analysis is an emerging methodology in oncologic imaging for quantitatively assessing tumor heterogeneity that could potentially aid in the stratification of these patients. The present study derives several texture-based features from CT images of PDAC patients, acquired prior to neoadjuvant chemotherapy, and analyzes their performance, individually as well as in combination, as prognostic markers. A fuzzy minimum redundancy maximum relevance method with leave-one-image-out technique is included to select discriminating features from the set of extracted features. With a naive Bayes classifier, the proposed method predicts the 5-year overall survival of PDAC patients prior to neoadjuvant therapy and achieves the best results in terms of the area under the receiver operating characteristic curve of 0:858 and accuracy of 83:0% with four-fold cross-validation techniques.
GSHSite: Exploiting an Iteratively Statistical Method to Identify S-Glutathionylation Sites with Substrate Specificity

PubMed Central

Chen, Yi-Ju; Lu, Cheng-Tsung; Huang, Kai-Yao; Wu, Hsin-Yi; Chen, Yu-Ju; Lee, Tzong-Yi

2015-01-01

S-glutathionylation, the covalent attachment of a glutathione (GSH) to the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-glutathionylation remains unknown. Based on a total of 1783 experimentally identified S-glutathionylation sites from mouse macrophages, this work presents an informatics investigation on S-glutathionylation sites including structural factors such as the flanking amino acids composition and the accessible surface area (ASA). TwoSampleLogo presents that positively charged amino acids flanking the S-glutathionylated cysteine may influence the formation of S-glutathionylation in closed three-dimensional environment. A statistical method is further applied to iteratively detect the conserved substrate motifs with statistical significance. Support vector machine (SVM) is then applied to generate predictive model considering the substrate motifs. According to five-fold cross-validation, the SVMs trained with substrate motifs could achieve an enhanced sensitivity, specificity, and accuracy, and provides a promising performance in an independent test set. The effectiveness of the proposed method is demonstrated by the correct identification of previously reported S-glutathionylation sites of mouse thioredoxin (TXN) and human protein tyrosine phosphatase 1b (PTP1B). Finally, the constructed models are adopted to implement an effective web-based tool, named GSHSite (http://csb.cse.yzu.edu.tw/GSHSite/), for identifying uncharacterized GSH substrate sites on the protein sequences. PMID:25849935
DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

PubMed

Zhang, Ling; Le Lu; Nogues, Isabella; Summers, Ronald M; Liu, Shaoxiong; Yao, Jianhua

2017-11-01

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.
EnzDP: improved enzyme annotation for metabolic network reconstruction based on domain composition profiles.

PubMed

Nguyen, Nam-Ninh; Srihari, Sriganesh; Leong, Hon Wai; Chong, Ket-Fah

2015-10-01

Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .
Photoanthropometric face iridial proportions for age estimation: An investigation using features selected via a joint mutual information criterion.

PubMed

Borges, Díbio L; Vidal, Flávio B; Flores, Marta R P; Melani, Rodolfo F H; Guimarães, Marco A; Machado, Carlos E P

2018-03-01

Age assessment from images is of high interest in the forensic community because of the necessity to establish formal protocols to identify child pornography, child missing and abuses where visual evidences are the mostly admissible. Recently, photoanthropometric methods have been found useful for age estimation correlating facial proportions in image databases with samples of some age groups. Notwithstanding the advances, newer facial features and further analysis are needed to improve accuracy and establish larger applicability. In this investigation, frontal images of 1000 individuals (500 females, 500 males), equally distributed in five age groups (6, 10, 14, 18, 22 years old) were used in a 10 fold cross-validated experiment for three age thresholds classifications (<10, <14, <18 years old). A set of novel 40 features, based on a relation between landmark distances and the iris diameter, is proposed and joint mutual information is used to select the most relevant and complementary features for the classification task. In a civil image identification database with diverse ancestry, receiver operating characteristic (ROC) curves were plotted to verify accuracy, and the resultant AUCs achieved 0.971, 0.969, and 0.903 for the age classifications (<10, <14, <18 years old), respectively. These results add support to continuing research in age assessment from images using the metric approach. Still, larger samples are necessary to evaluate reliability in extensive conditions. Copyright © 2017 Elsevier B.V. All rights reserved.
Detection of cervical intraepithelial neoplasias and cancers in cervical tissue by in vivo light scattering

PubMed Central

Mourant, Judith R.; Bocklage, Thérese J.; Powers, Tamara M.; Greene, Heather M.; Dorin, Maxine H.; Waxman, Alan G.; Zsemlye, Meggan M.; Smith, Harriet O.

2009-01-01

Objective To examine the utility of in vivo elastic light scattering measurements to identify cervical intraepithelial neoplasias (CIN) 2/3 and cancers in women undergoing colposcopy and to determine the effects of patient characteristics such as menstrual status on the elastic light scattering spectroscopic measurements. Materials and Methods A fiber optic probe was used to measure light transport in the cervical epithelium of patients undergoing colposcopy. Spectroscopic results from 151 patients were compared with histopathology of the measured and biopsied sites. A method of classifying the measured sites into two clinically relevant categories was developed and tested using five-fold cross-validation. Results Statistically significant effects by age at diagnosis, menopausal status, timing of the menstrual cycle, and oral contraceptive use were identified, and adjustments based upon these measurements were incorporated in the classification algorithm. A sensitivity of 77±5% and a specificity of 62±2% were obtained for separating CIN 2/3 and cancer from other pathologies and normal tissue. Conclusions The effects of both menstrual status and age should be taken into account in the algorithm for classifying tissue sites based on elastic light scattering spectroscopy. When this is done, elastic light scattering spectroscopy shows good potential for real-time diagnosis of cervical tissue at colposcopy. Guiding biopsy location is one potential near-term clinical application area, while facilitating ”see and treat” protocols is a longer term goal. Improvements in accuracy are essential. PMID:20694193
Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

PubMed

Al-Masni, Mohammed A; Al-Antari, Mugahed A; Park, Jeong-Min; Gi, Geon; Kim, Tae-Yeon; Rivera, Patricio; Valarezo, Edwin; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong

2018-04-01

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. Copyright © 2018 Elsevier B.V. All rights reserved.

Molecular phenotyping of severe asthma using pattern recognition of bronchoalveolar lavage-derived cytokines.

PubMed

Brasier, Allan R; Victor, Sundar; Boetticher, Gary; Ju, Hyunsu; Lee, Chang; Bleecker, Eugene R; Castro, Mario; Busse, William W; Calhoun, William J

2008-01-01

Asthma is a heterogeneous clinical disorder. Methods for objective identification of disease subtypes will focus on clinical interventions and help identify causative pathways. Few studies have explored phenotypes at a molecular level. We sought to discriminate asthma phenotypes on the basis of cytokine profiles in bronchoalveolar lavage (BAL) samples from patients with mild-moderate and severe asthma. Twenty-five cytokines were measured in BAL samples of 84 patients (41 severe, 43 mild-moderate) using bead-based multiplex immunoassays. The normalized data were subjected to statistical and informatics analysis. Four groups of asthmatic profiles could be identified on the basis of unsupervised analysis (hierarchical clustering) that were independent of treatment. One group, enriched in patients with severe asthma, showed differences in BAL cellular content, reductions in baseline pulmonary function, and enhanced response to methacholine provocation. Ten cytokines were identified that accurately predicted this group. Classification methods for predicting methacholine sensitivity were developed. The best model analysis predicted hyperresponders with 88% accuracy in 10 trials by using a 10-fold cross-validation. The cytokines that contributed to this model were IL-2, IL-4, and IL-5. On the basis of this classifier, 3 distinct hyperresponder classes were identified that varied in BAL eosinophil count and PC20 methacholine. Cytokine expression patterns in BAL can be used to identify distinct types of asthma and identify distinct subsets of methacholine hyperresponders. Further biomarker discovery in BAL may be informative.
Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

PubMed

Banno, Masaki; Komiyama, Yusuke; Cao, Wei; Oku, Yuya; Ueki, Kokoro; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

2017-02-01

Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Identification of DNA-binding proteins using structural, electrostatic and evolutionary features.

PubMed

Nimrod, Guy; Szilágyi, András; Leslie, Christina; Ben-Tal, Nir

2009-04-10

DNA-binding proteins (DBPs) participate in various crucial processes in the life-cycle of the cells, and the identification and characterization of these proteins is of great importance. We present here a random forests classifier for identifying DBPs among proteins with known 3D structures. First, clusters of evolutionarily conserved regions (patches) on the surface of proteins were detected using the PatchFinder algorithm; earlier studies showed that these regions are typically the functionally important regions of proteins. Next, we trained a classifier using features like the electrostatic potential, cluster-based amino acid conservation patterns and the secondary structure content of the patches, as well as features of the whole protein, including its dipole moment. Using 10-fold cross-validation on a dataset of 138 DBPs and 110 proteins that do not bind DNA, the classifier achieved a sensitivity and a specificity of 0.90, which is overall better than the performance of published methods. Furthermore, when we tested five different methods on 11 new DBPs that did not appear in the original dataset, only our method annotated all correctly. The resulting classifier was applied to a collection of 757 proteins of known structure and unknown function. Of these proteins, 218 were predicted to bind DNA, and we anticipate that some of them interact with DNA using new structural motifs. The use of complementary computational tools supports the notion that at least some of them do bind DNA.
Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.

PubMed

Chakraborty, Jayasree; Langdon-Embry, Liana; Cunanan, Kristen M; Escalon, Joanna G; Allen, Peter J; Lowery, Maeve A; O'Reilly, Eileen M; Gönen, Mithat; Do, Richard G; Simpson, Amber L

2017-01-01

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.
Measurement of semi-inclusive π+ electroproduction off the proton

NASA Astrophysics Data System (ADS)

Osipenko, M.; Ripani, M.; Ricco, G.; Avakian, H.; de Vita, R.; Adams, G.; Amaryan, M. J.; Ambrozewicz, P.; Anghinolfi, M.; Asryan, G.; Bagdasaryan, H.; Baillie, N.; Ball, J. P.; Baltzell, N. A.; Barrow, S.; Battaglieri, M.; Bedlinskiy, I.; Bektasoglu, M.; Bellis, M.; Benmouna, N.; Berman, B. L.; Biselli, A. S.; Blaszczyk, L.; Bonner, B. E.; Bouchigny, S.; Boiarinov, S.; Bradford, R.; Branford, D.; Briscoe, W. J.; Brooks, W. K.; Bültmann, S.; Burkert, V. D.; Butuceanu, C.; Calarco, J. R.; Careccia, S. L.; Carman, D. S.; Cazes, A.; Ceccopieri, F.; Chen, S.; Cole, P. L.; Collins, P.; Coltharp, P.; Corvisiero, P.; Crabb, D.; Crede, V.; Cummings, J. P.; Dashyan, N.; de Masi, R.; de Sanctis, E.; Degtyarenko, P. V.; Denizli, H.; Dennis, L.; Deur, A.; Dharmawardane, K. V.; Dhuga, K. S.; Dickson, R.; Djalali, C.; Dodge, G. E.; Donnelly, J.; Doughty, D.; Drozdov, V.; Dugger, M.; Dytman, S.; Dzyubak, O. P.; Egiyan, H.; Egiyan, K. S.; El Fassi, L.; Elouadrhiri, L.; Eugenio, P.; Fatemi, R.; Fedotov, G.; Feldman, G.; Feuerbach, R. J.; Funsten, H.; Garçon, M.; Gavalian, G.; Gilfoyle, G. P.; Giovanetti, K. L.; Girod, F. X.; Goetz, J. T.; Golovach, E.; Gonenc, A.; Gordon, C. I. O.; Gothe, R. W.; Griffioen, K. A.; Guidal, M.; Guillo, M.; Guler, N.; Guo, L.; Gyurjyan, V.; Hadjidakis, C.; Hafidi, K.; Hakobyan, H.; Hakobyan, R. S.; Hanretty, C.; Hardie, J.; Hassall, N.; Heddle, D.; Hersman, F. W.; Hicks, K.; Hleiqawi, I.; Holtrop, M.; Hyde-Wright, C. E.; Ilieva, Y.; Ilyichev, A.; Ireland, D. G.; Ishkhanov, B. S.; Isupov, E. L.; Ito, M. M.; Jenkins, D.; Jo, H. S.; Joo, K.; Juengst, H. G.; Kalantarians, N.; Kellie, J. D.; Khandaker, M.; Kim, W.; Klein, A.; Klein, F. J.; Klimenko, A. V.; Kossov, M.; Krahn, Z.; Kramer, L. H.; Kubarovsky, V.; Kuhn, J.; Kuhn, S. E.; Kuleshov, S. V.; Lachniet, J.; Laget, J. M.; Langheinrich, J.; Lawrence, D.; Li, Ji; Livingston, K.; Lu, H. Y.; MacCormick, M.; Markov, N.; Mattione, P.; McAleer, S.; McCracken, M.; McKinnon, B.; McNabb, J. W. C.; Mecking, B. A.; Mehrabyan, S.; Melone, J. J.; Mestayer, M. D.; Meyer, C. A.; Mibe, T.; Mikhailov, K.; Minehart, R.; Mirazita, M.; Miskimen, R.; Mokeev, V.; Moriya, K.; Morrow, S. A.; Moteabbed, M.; Mueller, J.; Munevar, E.; Mutchler, G. S.; Nadel-Turonski, P.; Napolitano, J.; Nasseripour, R.; Niccolai, S.; Niculescu, G.; Niculescu, I.; Niczyporuk, B. B.; Niroula, M. R.; Niyazov, R. A.; Nozar, M.; O'Rielly, G. V.; Ostrovidov, A. I.; Park, K.; Pasyuk, E.; Paterson, C.; Pereira, S. Anefalos; Philips, S. A.; Pierce, J.; Pivnyuk, N.; Pocanic, D.; Pogorelko, O.; Polli, E.; Popa, I.; Pozdniakov, S.; Preedom, B. M.; Price, J. W.; Prok, Y.; Protopopescu, D.; Qin, L. M.; Raue, B. A.; Riccardi, G.; Ritchie, B. G.; Rosner, G.; Rossi, P.; Rubin, P. D.; Sabatié, F.; Salamanca, J.; Salgado, C.; Santoro, J. P.; Sapunenko, V.; Schumacher, R. A.; Serov, V. S.; Sharabian, Y. G.; Shvedunov, N. V.; Skabelin, A. V.; Smith, E. S.; Smith, L. C.; Sober, D. I.; Sokhan, D.; Stavinsky, A.; Stepanyan, S. S.; Stepanyan, S.; Stokes, B. E.; Stoler, P.; Strakovsky, I. I.; Strauch, S.; Taiuti, M.; Tedeschi, D. J.; Thoma, U.; Tkabladze, A.; Tkachenko, S.; Todor, L.; Trentadue, L.; Tur, C.; Ungaro, M.; Vineyard, M. F.; Vlassov, A. V.; Watts, D. P.; Weinstein, L. B.; Weygand, D. P.; Williams, M.; Wolin, E.; Wood, M. H.; Yegneswaran, A.; Zana, L.; Zhang, J.; Zhao, B.; Zhao, Z. W.

2009-08-01

Semi-inclusive π+ electroproduction on protons has been measured with the CLAS detector at Jefferson Lab. The measurement was performed on a liquid-hydrogen target using a 5.75 GeV electron beam. The complete five-fold differential cross sections were measured over a wide kinematic range including the complete range of azimuthal angles between hadronic and leptonic planes, ϕ, enabling us to separate the ϕ-dependent terms. Our measurements of the ϕ-independent term of the cross section at low Bjorken x were found to be in fairly good agreement with pQCD calculations. Indeed, the conventional current fragmentation calculation can account for almost all of the observed cross section, even at small π+ momentum. The measured center-of-momentum spectra are in qualitative agreement with high-energy data, which suggests a surprising numerical similarity between the spectator diquark fragmentation in the present reaction and the antiquark fragmentation measured in e+e- collisions. We have observed that the two ϕ-dependent terms of the cross section are small. Within our precision the cos⁡2ϕ term is compatible with zero, except for the low-z region, and the measured cos⁡ϕ term is much smaller in magnitude than the sum of the Cahn and Berger effects.
Transpressional folding and associated cross-fold jointing controlling the geometry of post-orogenic vein-type W-Sn mineralization: examples from Minas da Panasqueira, Portugal

NASA Astrophysics Data System (ADS)

Jacques, Dominique; Vieira, Romeu; Muchez, Philippe; Sintubin, Manuel

2018-02-01

The world-class W-Sn Panasqueira deposit consists of an extensive, subhorizontal vein swarm, peripheral to a late-orogenic greisen cupola. The vein swarm consists of hundreds of co-planar quartz veins that are overlapping and connected laterally over large distances. Various segmentation structures, a local zigzag geometry, and the occurrence of straight propagation paths indicate that they exploited a regional joint system. A detailed orientation analysis of the systematic joints reveals a geometrical relationship with the subvertical F2 fold generation, reflecting late-Variscan transpression. The joints are consistently orthogonal to the steeply plunging S0-S2 intersection lineation, both on the regional and the outcrop scale, and are thus defined as cross-fold or ac-joints. The joint system developed during the waning stages of the Variscan orogeny, when already uplifted to an upper-crustal level. Veining reactivated these cross-fold joints under the conditions of hydraulic overpressures and low differential stress. The consistent subperpendicular orientation of the veins relative to the non-cylindrical F2 hinge lines, also when having an inclined attitude, demonstrates that veining did not occur during far-field horizontal compression. Vein orientation is determined by local stress states variable on a meter-scale but with the minimum principal stress consistently subparallel to fold hinge lines. The conspicuous subhorizontal attitude of the Panasqueira vein swarm is thus dictated by the geometry of late-orogenic folds, which developed synchronous with oroclinal buckling of the Ibero-Armorican arc.
The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle.

PubMed

Lee, J; Kachman, S D; Spangler, M L

2017-08-01

Genomic selection (GS) has become an integral part of genetic evaluation methodology and has been applied to all major livestock species, including beef and dairy cattle, pigs, and chickens. Significant contributions in increased accuracy of selection decisions have been clearly illustrated in dairy cattle after practical application of GS. In the majority of U.S. beef cattle breeds, similar efforts have also been made to increase the accuracy of genetic merit estimates through the inclusion of genomic information into routine genetic evaluations using a variety of methods. However, prediction accuracies can vary relative to panel density, the number of folds used for folds cross-validation, and the choice of dependent variables (e.g., EBV, deregressed EBV, adjusted phenotypes). The aim of this study was to evaluate the accuracy of genomic predictors for Red Angus beef cattle with different strategies used in training and evaluation. The reference population consisted of 9,776 Red Angus animals whose genotypes were imputed to 2 medium-density panels consisting of over 50,000 (50K) and approximately 80,000 (80K) SNP. Using the imputed panels, we determined the influence of marker density, exclusion (deregressed EPD adjusting for parental information [DEPD-PA]) or inclusion (deregressed EPD without adjusting for parental information [DEPD]) of parental information in the deregressed EPD used as the dependent variable, and the number of clusters used to partition training animals (3, 5, or 10). A BayesC model with π set to 0.99 was used to predict molecular breeding values (MBV) for 13 traits for which EPD existed. The prediction accuracies were measured as genetic correlations between MBV and weighted deregressed EPD. The average accuracies across all traits were 0.540 and 0.552 when using the 50K and 80K SNP panels, respectively, and 0.538, 0.541, and 0.561 when using 3, 5, and 10 folds, respectively, for cross-validation. Using DEPD-PA as the response variable resulted in higher accuracies of MBV than those obtained by DEPD for growth and carcass traits. When DEPD were used as the response variable, accuracies were greater for threshold traits and those that are sex limited, likely due to the fact that these traits suffer from a lack of information content and excluding animals in training with only parental information substantially decreases the training population size. It is recommended that the contribution of parental average to deregressed EPD should be removed in the construction of genomic prediction equations. The difference in terms of prediction accuracies between the 2 SNP panels or the number of folds compared herein was negligible.
Paternal identity impacts embryonic development for two species of freshwater fish.

PubMed

Siddique, Mohammad Abdul Momin; Linhart, Otomar; Krejszeff, Sławomir; Żarski, Daniel; Pitcher, Trevor E; Politis, Sebastian Nikitas; Butts, Ian Anthony Ernest

2017-05-01

Paternal, compared to maternal, contributions were believed to have only a limited influence on embryonic development and larval fitness traits in fishes. Therefore, the perspective of male influence on early life history traits has come under scrutiny. This study was conducted to determine parental effects on the rate of eyed embryos of Ide Leuciscus idus and Northern pike Esox lucius. Five sires and five dams from each species were crossed using a quantitative genetic breeding design and the resulting 25 sib groups of each species were reared to the embryonic eyed stage. We then partition variation in embryonic phenotypic performance to maternal, paternal, and parental interactions using the Restricted Maximum Likelihood (REML) model. Results showed that paternal, maternal, and the paternal×maternal interaction terms were highly significant for both species; clearly demonstrating that certain family combinations were more compatible than others. Paternal effects explained 20.24% of the total variance, which was 2-fold higher than the maternal effects (10.73%) in Ide, while paternal effects explained 18.9% of the total variance, which was 15-fold higher than the maternal effects (1.3%) in Northern pike. Together, these results indicate that male effects are of major importance during embryonic development for these species. Furthermore, this study demonstrates that genetic compatibility between sires and dams plays an important role and needs to be taken into consideration for reproduction of these and likely other economically important fish species. Copyright © 2016 Elsevier Inc. All rights reserved.
Cross-cultural adaptation and validation of the Korean version of the Oxford shoulder score.

PubMed

Roh, Young Hak; Noh, Jung Ho; Kim, Woo; Oh, Joo Han; Gong, Hyun Sik; Baek, Goo Hyun

2012-01-01

The Oxford shoulder score (OSS) is being used increasingly and has been adapted cross-culturally in some Western countries. On the other hand, there are few validated translations of the OSS in Asian countries. This study translated and adapted cross-culturally the original OSS to produce a Korean version, and assessed the validity and reliability of the Korean version of the OSS (Korean OSS). One hundred and five patients with shoulder pain caused by degenerative or inflammatory disorders completed the Korean OSS and Korean disability of arm, shoulder and hand (DASH). In addition, the pain score by a visual analog scale (VAS) during activity and at rest, subjective assessment of activities of daily living (ADL), the active range of motion (ROM), and measurements of the abduction strength (strength) were included in the validation process. There were no major linguistic or cultural problems during the forward and backward translations of the MHQ, except for a minor change due to cultural discrepancies in eating such as using a spoon and chopsticks by one dominant hand instead of a knife and fork by two hands. The internal consistency was high (Cronbach's alpha 0.91). The reproducibility test showed no significant difference (Intra-class coefficient 0.95). The construct validity, which was tested by the Pearson correlation coefficient revealed a strong correlation (r > 0.6) between the Korean OSS against subscale of DASH disability/symptom, DASH work and ADL, as well as a moderate correlation (0.3 < r < 0.6) with the DASH sports/music, strength, ROM, pain during activity and pain at rest. The Korean OSS proved to be valid by demonstrating a significant correlation with the patient-based upper extremity questionnaire and clinical assessment. The application and evaluation of the instrument is feasible and understandable among patients in Korea.
The Vocal Tract Discomfort Scale: Validity and Reliability of the Persian Version in the Assessment of Patients With Muscle Tension Dysphonia.

PubMed

Torabi, Hadi; Khoddami, Seyyedeh Maryam; Ansari, Noureddin Nakhostin; Dabirmoghaddam, Payman

2016-11-01

To cross-culturally adapt of Persian Vocal Tract Discomfort (VTDp) scale and evaluate its validity and reliability in the assessment of patients with muscle tension dysphonia (MTD). A cross-sectional and prospective cohort design was used to psychometrically test the VTDp. The VTD scale was cross-culturally adapted into Persian language following standard forward-backward translations. The VTDp scale was administrated to 100 patients with MTD (54 men and 46 women; mean age: 38.05 ± 10.02 years) and 50 healthy volunteers (26 men and 24 women; mean age: 36.50 ± 12.27 years). Forty-five patients with MTD completed the VTDp 7 days later for test-retest reliability. Patients also completed the Persian Voice Handicap Index (VHIp) to assess construct validity. The results of discriminative validity demonstrated that the VTDp was able to discriminate between patients with MTD and healthy participants. The internal consistency was confirmed with Cronbach α .77 and 0.73 for VTDp frequency and severity subscales, respectively. The test-retest reliability was excellent with an intraclass correlation coefficient (ICC agreement ) of 0.93 for the frequency subscale and 0.91 for the severity subscale. Construct validity of the VTDp was shown with significant correlations between the VTDp frequency and severity subscales and the VHIp total scores (0.36 and 0.37, respectively). The standard error of measurement and smallest detectable change values for VTDp frequency (2.11 and 5.85, respectively) and severity (2.25 and 6.23, respectively) were acceptable. The Bland-Altman analysis for assessing the agreement between test and retest measurements showed no systematic bias. The VTDp is a valid and reliable self-administered scale to measure patient's vocal tract sensations in Persian-speaking population. Copyright Â© 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer

PubMed Central

Paik, E Sun; Sohn, Insuk; Baek, Sun-Young; Shim, Minhee; Choi, Hyun Jin; Kim, Tae-Joong; Choi, Chel Hun; Lee, Jeong-Won; Kim, Byoung-Gie; Lee, Yoo-Young; Bae, Duk-Soo

2017-01-01

Purpose This study was conducted to evaluate the prognostic significance of pre-treatment complete blood cell count (CBC), including white blood cell (WBC) differential, in epithelial ovarian cancer (EOC) patients with primary debulking surgery (PDS) and to develop nomograms for platinum sensitivity, progression-free survival (PFS), and overall survival (OS). Materials and Methods We retrospectively reviewed the records of 757 patients with EOC whose primary treatment consisted of surgical debulking and chemotherapy at Samsung Medical Center from 2002 to 2012. We subsequently created nomograms for platinum sensitivity, 3-year PFS, and 5-year OS as prediction models for prognostic variables including age, stage, grade, cancer antigen 125 level, residual disease after PDS, and pre-treatment WBC differential counts. The models were then validated by 10-fold cross-validation (CV). Results In addition to stage and residual disease after PDS, which are known predictors, lymphocyte and monocyte count were found to be significant prognostic factors for platinum-sensitivity, platelet count for PFS, and neutrophil count for OS on multivariate analysis. The area under the curves of platinum sensitivity, 3-year PFS, and 5-year OS calculated by the 10-fold CV procedure were 0.7405, 0.8159, and 0.815, respectively. Conclusion Prognostic factors including pre-treatment CBC were used to develop nomograms for platinum sensitivity, 3-year PFS, and 5-year OS of patients with EOC. These nomograms can be used to better estimate individual outcomes. PMID:27669704
Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Sohn, Insuk; Baek, Sun-Young; Shim, Minhee; Choi, Hyun Jin; Kim, Tae-Joong; Choi, Chel Hun; Lee, Jeong-Won; Kim, Byoung-Gie; Lee, Yoo-Young; Bae, Duk-Soo

2017-07-01

This study was conducted to evaluate the prognostic significance of pre-treatment complete blood cell count (CBC), including white blood cell (WBC) differential, in epithelial ovarian cancer (EOC) patients with primary debulking surgery (PDS) and to develop nomograms for platinum sensitivity, progression-free survival (PFS), and overall survival (OS). We retrospectively reviewed the records of 757 patients with EOC whose primary treatment consisted of surgical debulking and chemotherapy at Samsung Medical Center from 2002 to 2012. We subsequently created nomograms for platinum sensitivity, 3-year PFS, and 5-year OS as prediction models for prognostic variables including age, stage, grade, cancer antigen 125 level, residual disease after PDS, and pre-treatment WBC differential counts. The models were then validated by 10-fold cross-validation (CV). In addition to stage and residual disease after PDS, which are known predictors, lymphocyte and monocyte count were found to be significant prognostic factors for platinum-sensitivity, platelet count for PFS, and neutrophil count for OS on multivariate analysis. The area under the curves of platinum sensitivity, 3-year PFS, and 5-year OS calculated by the 10-fold CV procedure were 0.7405, 0.8159, and 0.815, respectively. Prognostic factors including pre-treatment CBC were used to develop nomograms for platinum sensitivity, 3-year PFS, and 5-year OS of patients with EOC. These nomograms can be used to better estimate individual outcomes.
Revising the predictions of inflation for the cosmic microwave background anisotropies.

PubMed

Agulló, Iván; Navarro-Salas, José; Olmo, Gonzalo J; Parker, Leonard

2009-08-07

We point out that, if quantum field renormalization is taken into account and the counterterms are evaluated at the Hubble-radius crossing time or few e-foldings after it, the predictions of slow-roll inflation for both the scalar and the tensorial power spectrum change significantly. This leads to a change in the consistency condition that relates the tensor-to-scalar amplitude ratio with spectral indices. A reexamination of the potentials varphi;{2} and varphi;{4} shows that both are compatible with five-year WMAP data. Only when the counterterms are evaluated at much larger times beyond the end of inflation does one recover the standard predictions. The alternative predictions presented here may soon come within the range of measurement of near-future experiments.
Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

PubMed Central

Lou, Wangchao; Wang, Xiaoqing; Chen, Fan; Chen, Yixiao; Jiang, Bo; Zhang, Hua

2014-01-01

Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins. PMID:24475169
Emotional Sentence Annotation Helps Predict Fiction Genre.

PubMed

Samothrakis, Spyridon; Fasli, Maria

2015-01-01

Fiction, a prime form of entertainment, has evolved into multiple genres which one can broadly attribute to different forms of stories. In this paper, we examine the hypothesis that works of fiction can be characterised by the emotions they portray. To investigate this hypothesis, we use the work of fictions in the Project Gutenberg and we attribute basic emotional content to each individual sentence using Ekman's model. A time-smoothed version of the emotional content for each basic emotion is used to train extremely randomized trees. We show through 10-fold Cross-Validation that the emotional content of each work of fiction can help identify each genre with significantly higher probability than random. We also show that the most important differentiator between genre novels is fear.
Imputing data that are missing at high rates using a boosting algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cauthen, Katherine Regina; Lambert, Gregory; Ray, Jaideep

Traditional multiple imputation approaches may perform poorly for datasets with high rates of missingness unless many m imputations are used. This paper implements an alternative machine learning-based approach to imputing data that are missing at high rates. Here, we use boosting to create a strong learner from a weak learner fitted to a dataset missing many observations. This approach may be applied to a variety of types of learners (models). The approach is demonstrated by application to a spatiotemporal dataset for predicting dengue outbreaks in India from meteorological covariates. A Bayesian spatiotemporal CAR model is boosted to produce imputations, andmore » the overall RMSE from a k-fold cross-validation is used to assess imputation accuracy.« less
Thermoreversible Folding as a Route to the Unique Shape-Memory Character in Ductile Polymer Networks.

PubMed

McBride, Matthew K; Podgorski, Maciej; Chatani, Shunsuke; Worrell, Brady T; Bowman, Christopher N

2018-06-21

Ductile, cross-linked films were folded as a means to program temporary shapes without the need for complex heating cycles or specialized equipment. Certain cross-linked polymer networks, formed here with the thiol-isocyanate reaction, possessed the ability to be pseudoplastically deformed below the glass transition, and the original shape was recovered during heating through the glass transition. To circumvent the large forces required to plastically deform a glassy polymer network, we have utilized folding, which localizes the deformation in small creases, and achieved large dimensional changes with simple programming procedures. In addition to dimension changes, three-dimensional objects such as swans and airplanes were developed to demonstrate applying origami principles to shape memory. We explored the fundamental mechanical properties that are required to fold polymer sheets and observed that a yield point that does not correspond to catastrophic failure is required. Unfolding occurred during heating through the glass transition, indicating the vitrification of the network that maintained the temporary, folded shape. Folding was demonstrated as a powerful tool to simply and effectively program ductile shape-memory polymers without the need for thermal cycling.
Validated near-atomic resolution structure of bacteriophage epsilon15 derived from cryo-EM and modeling

PubMed Central

Baker, Matthew L.; Hryc, Corey F.; Zhang, Qinfen; Wu, Weimin; Jakana, Joanita; Haase-Pettingell, Cameron; Afonine, Pavel V.; Adams, Paul D.; King, Jonathan A.; Jiang, Wen; Chiu, Wah

2013-01-01

High-resolution structures of viruses have made important contributions to modern structural biology. Bacteriophages, the most diverse and abundant organisms on earth, replicate and infect all bacteria and archaea, making them excellent potential alternatives to antibiotics and therapies for multidrug-resistant bacteria. Here, we improved upon our previous electron cryomicroscopy structure of Salmonella bacteriophage epsilon15, achieving a resolution sufficient to determine the tertiary structures of both gp7 and gp10 protein subunits that form the T = 7 icosahedral lattice. This study utilizes recently established best practice for near-atomic to high-resolution (3–5 Å) electron cryomicroscopy data evaluation. The resolution and reliability of the density map were cross-validated by multiple reconstructions from truly independent data sets, whereas the models of the individual protein subunits were validated adopting the best practices from X-ray crystallography. Some sidechain densities are clearly resolved and show the subunit–subunit interactions within and across the capsomeres that are required to stabilize the virus. The presence of the canonical phage and jellyroll viral protein folds, gp7 and gp10, respectively, in the same virus suggests that epsilon15 may have emerged more recently relative to other bacteriophages. PMID:23840063
High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications.

PubMed

Fernandez-Miranda, Juan C; Pathak, Sudhir; Engh, Johnathan; Jarbo, Kevin; Verstynen, Timothy; Yeh, Fang-Cheng; Wang, Yibao; Mintz, Arlan; Boada, Fernando; Schneider, Walter; Friedlander, Robert

2012-08-01

High-definition fiber tracking (HDFT) is a novel combination of processing, reconstruction, and tractography methods that can track white matter fibers from cortex, through complex fiber crossings, to cortical and subcortical targets with subvoxel resolution. To perform neuroanatomical validation of HDFT and to investigate its neurosurgical applications. Six neurologically healthy adults and 36 patients with brain lesions were studied. Diffusion spectrum imaging data were reconstructed with a Generalized Q-Ball Imaging approach. Fiber dissection studies were performed in 20 human brains, and selected dissection results were compared with tractography. HDFT provides accurate replication of known neuroanatomical features such as the gyral and sulcal folding patterns, the characteristic shape of the claustrum, the segmentation of the thalamic nuclei, the decussation of the superior cerebellar peduncle, the multiple fiber crossing at the centrum semiovale, the complex angulation of the optic radiations, the terminal arborization of the arcuate tract, and the cortical segmentation of the dorsal Broca area. From a clinical perspective, we show that HDFT provides accurate structural connectivity studies in patients with intracerebral lesions, allowing qualitative and quantitative white matter damage assessment, aiding in understanding lesional patterns of white matter structural injury, and facilitating innovative neurosurgical applications. High-grade gliomas produce significant disruption of fibers, and low-grade gliomas cause fiber displacement. Cavernomas cause both displacement and disruption of fibers. Our HDFT approach provides an accurate reconstruction of white matter fiber tracts with unprecedented detail in both the normal and pathological human brain. Further studies to validate the clinical findings are needed.
Bridging a translational gap: using machine learning to improve the prediction of PTSD.

PubMed

Karstoft, Karen-Inge; Galatzer-Levy, Isaac R; Statnikov, Alexander; Li, Zhiguo; Shalev, Arieh Y

2015-03-16

Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators. Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

Identification of a MicroRNA Signature for the Diagnosis of Fibromyalgia

PubMed Central

Monsalve, Vicente; Oltra, Elisa

2015-01-01

Background Diagnosis of fibromyalgia (FM), a chronic musculoskeletal pain syndrome characterized by generalized body pain, hyperalgesia and other functional and emotional comorbidities, is a challenging process hindered by symptom heterogeneity and clinical overlap with other disorders. No objective diagnostic method exists at present. The aim of this study was to identify changes in miRNA expression profiles (miRNome) of these patients for the development of a quantitative diagnostic method of FM. In addition, knowledge of FM patient miRNomes should lead to a deeper understanding of the etiology and/or symptom severity of this complex disease. Methods Genome-wide expression profiling of miRNAs was assessed in Peripheral Blood Mononuclear Cells (PBMCs) of FM patients (N=11) and population-age-matched controls (N=10) using human v16-miRbase 3D-Gene microarrays (Toray Industries, Japan). Selected miRNAs from the screen were further validated by RT-qPCR. Participating patients were long term sufferers (over 10 years) diagnosed by more than one specialist under 1990 American College of Rheumatology criteria. Results Microarray analysis of FM patient PBMCs evidenced a marked downregulation of hsa-miR223-3p, hsa-miR451a, hsa-miR338-3p, hsa-miR143-3p, hsa-miR145-5p and hsa-miR-21-5p (4-fold or more). All but the mildest inhibited miRNA, hsa-miR-21-5p, were validated by RT-qPCR. Globally, 20% of the miRNAs analyzed (233/1212) showed downregulation of at least 2-fold in patients. This might indicate a general de-regulation of the miRNA synthetic pathway in FM. No significant correlations between miRNA inhibition and FM cardinal symptoms could be identified. However, the patient with the lowest score for mental fatigue coincided with the mildest inhibition in four of the five miRNAs associated with the FM-group. Conclusions We propose a signature of five strikingly downregulated miRNAs (hsa-miR223-3p, hsa-miR451a, hsa-miR338-3p, hsa-miR143-3p and hsa-miR145-5p) to be used as biomarkers of FM. Validation in larger study groups is required before the results can be transferred to the clinic. PMID:25803872
Identification of a microRNA signature for the diagnosis of fibromyalgia.

PubMed

Cerdá-Olmedo, Germán; Mena-Durán, Armando Vicente; Monsalve, Vicente; Oltra, Elisa

2015-01-01

Diagnosis of fibromyalgia (FM), a chronic musculoskeletal pain syndrome characterized by generalized body pain, hyperalgesia and other functional and emotional comorbidities, is a challenging process hindered by symptom heterogeneity and clinical overlap with other disorders. No objective diagnostic method exists at present. The aim of this study was to identify changes in miRNA expression profiles (miRNome) of these patients for the development of a quantitative diagnostic method of FM. In addition, knowledge of FM patient miRNomes should lead to a deeper understanding of the etiology and/or symptom severity of this complex disease. Genome-wide expression profiling of miRNAs was assessed in Peripheral Blood Mononuclear Cells (PBMCs) of FM patients (N=11) and population-age-matched controls (N=10) using human v16-miRbase 3D-Gene microarrays (Toray Industries, Japan). Selected miRNAs from the screen were further validated by RT-qPCR. Participating patients were long term sufferers (over 10 years) diagnosed by more than one specialist under 1990 American College of Rheumatology criteria. Microarray analysis of FM patient PBMCs evidenced a marked downregulation of hsa-miR223-3p, hsa-miR451a, hsa-miR338-3p, hsa-miR143-3p, hsa-miR145-5p and hsa-miR-21-5p (4-fold or more). All but the mildest inhibited miRNA, hsa-miR-21-5p, were validated by RT-qPCR. Globally, 20% of the miRNAs analyzed (233/1212) showed downregulation of at least 2-fold in patients. This might indicate a general de-regulation of the miRNA synthetic pathway in FM. No significant correlations between miRNA inhibition and FM cardinal symptoms could be identified. However, the patient with the lowest score for mental fatigue coincided with the mildest inhibition in four of the five miRNAs associated with the FM-group. We propose a signature of five strikingly downregulated miRNAs (hsa-miR223-3p, hsa-miR451a, hsa-miR338-3p, hsa-miR143-3p and hsa-miR145-5p) to be used as biomarkers of FM. Validation in larger study groups is required before the results can be transferred to the clinic.
Mesozoic intracontinental underthrust in the SE margin of the North China Block: Insights from the Xu-Huai thrust-and-fold belt

NASA Astrophysics Data System (ADS)

Shu, Liangshu; Yin, Hongwei; Faure, Michel; Chen, Yan

2017-06-01

The Xu-Huai thrust-and-fold belt, located in the southeastern margin of the North China Block, consists mainly of thrust and folded pre-Mesozoic strata. Its geodynamic evolution and tectonic setting are topics of long debate. This paper provides new evidence from geological mapping, structural analysis, and making balance cross-sections, with restoration of cross-sections. Results suggest that this belt was subjected to two-phase deformation, including an early-phase regional-scale NW-ward thrust and fold, and a late-phase extension followed by the emplacement of dioritic, monzodioritic porphyrites dated at 131-135 Ma and locally strike-slip shearing. According to the mapping, field observations and drill-hole data, three structural units were distinguished, namely, (1) the pre-Neoproterozoic crystalline basement in the eastern segment, (2) the nappe unit or the thrust-and-fold zone in the central segment, which is composed of Neoproterozoic to Ordovician carbonate rocks and Carboniferous-Permian coal-bearing rocks, about 2600 m thick, and (3) the western frontal zone. A major decollement fault has also been identified in the base of the nappe unit, on which dozen-meter to km-scale thrust-and-fold bodies were commonly developed. All pre-Mesozoic depositional sequences were involved into a widespread thrust and fold event. Six uncompetent-rock layers with biostratigraphic ages (Nanjing University, 1996) have been recognized, and each uncompetent-rock layer occurred mainly in the top of the footwall, playing an important role in the development of the Xu-Huai thrust-and-fold belt. Geometry of the major decollement fault suggests that the nappe unit of this belt was rooted in its eastern side, near the Tan-Lu Fault Zone. Two geological cross-sections were chosen for structural balancing and restoration. From the balanced cross-sections, ramp-flat and imbricated faults as well as fault-related folds were identified. A shortening of 20.6-29.6 km was obtained from restoration of balanced sections, corresponding to a shortening rate of 43.6-46.4%. This shortening deformation was likely related to the SE-ward intracontinental underthrust of the North China Block beneath the South China Block during the Mesozoic.
In vivo cross-sectional imaging of the phonating larynx using long-range Doppler optical coherence tomography

NASA Astrophysics Data System (ADS)

Coughlan, Carolyn A.; Chou, Li-Dek; Jing, Joseph C.; Chen, Jason J.; Rangarajan, Swathi; Chang, Theodore H.; Sharma, Giriraj K.; Cho, Kyoungrai; Lee, Donghoon; Goddard, Julie A.; Chen, Zhongping; Wong, Brian J. F.

2016-03-01

Diagnosis and treatment of vocal fold lesions has been a long-evolving science for the otolaryngologist. Contemporary practice requires biopsy of a glottal lesion in the operating room under general anesthesia for diagnosis. Current in-office technology is limited to visualizing the surface of the vocal folds with fiber-optic or rigid endoscopy and using stroboscopic or high-speed video to infer information about submucosal processes. Previous efforts using optical coherence tomography (OCT) have been limited by small working distances and imaging ranges. Here we report the first full field, high-speed, and long-range OCT images of awake patients’ vocal folds as well as cross-sectional video and Doppler analysis of their vocal fold motions during phonation. These vertical-cavity surface-emitting laser source (VCSEL) OCT images offer depth resolved, high-resolution, high-speed, and panoramic images of both the true and false vocal folds. This technology has the potential to revolutionize in-office imaging of the larynx.
Intelligent quotient estimation of mental retarded people from different psychometric instruments using artificial neural networks.

PubMed

Di Nuovo, Alessandro G; Di Nuovo, Santo; Buono, Serafino

2012-02-01

The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in the application of psychological assessment to several fields. When complex tests as the Wechsler scales, which are the most commonly used and universally recognized parameter for the diagnosis of degrees of retardation, are not applicable, it is necessary to use other psycho-diagnostic tools more suited for the subject's specific condition. But to ensure a homogeneous diagnosis it is necessary to reach a common metric, thus, the aim of our work is to build models able to estimate accurately and reliably the Wechsler IQ, starting from different psycho-diagnostic tools. Four different psychometric tests (Leiter international performance scale; coloured progressive matrices test; the mental development scale; psycho educational profile), along with the Wechsler scale, were administered to a group of 40 mentally retarded subjects, with various pathologies, and control persons. The obtained database is used to evaluate Wechsler IQ estimation models starting from the scores obtained in the other tests. Five modelling methods, two statistical and three from machine learning, that belong to the family of artificial neural networks (ANNs) are employed to build the estimator. Several error metrics for estimated IQ and for retardation level classification are defined to compare the performance of the various models with univariate and multivariate analyses. Eight empirical studies show that, after ten-fold cross-validation, best average estimation error is of 3.37 IQ points and mental retardation level classification error of 7.5%. Furthermore our experiments prove the superior performance of ANN methods over statistical regression ones, because in all cases considered ANN models show the lowest estimation error (from 0.12 to 0.9 IQ points) and the lowest classification error (from 2.5% to 10%). Since the estimation performance is better than the confidence interval of Wechsler scales (five IQ points), we consider models built very accurate and reliable and they can be used into help clinical diagnosis. Therefore a computer software based on the results of our work is currently used in a clinical center and empirical trails confirm its validity. Furthermore positive results in our multivariate studies suggest new approaches for clinicians. Copyright © 2011 Elsevier B.V. All rights reserved.
Confirmatory factorial analysis of the children´s attraction to physical activity scale (capa).

PubMed

Seabra, A C; Maia, J A; Parker, M; Seabra, A; Brustad, R; Fonseca, A M

2015-03-27

Attraction to physical activity (PA) is an important contributor to children´s intrinsic motivation to engage in games, and sports. Previous studies have supported the utility of the children´s attraction to PA scale (CAPA) (Brustad, 1996) but the validity of this measure for use in Portugal has not been established. The purpose of this study was to cross-validate the shorter version of the CAPA scale in the Portuguese cultural context. A sample of 342 children (8--10 years of age) was used. Confirmatory factor analyses using EQS software ( version 6.1) tested t hree competing measurement models: a single--factor model, a five factor model, and a second order factor model. The single--factor model and the second order model showed a poor fit to the data. It was found that a five-factor model similar to the original one revealed good fit to the data (S--B χ 2 (67) =94.27,p=0.02; NNFI=0.93; CFI=0.95; RMSEA=0.04; 90%CI=0.02;0.05). The results indicated that the CAPA scale is valid and appropriate for use in the Portuguese cultural context. The availability of a valid scale to evaluate attraction to PA at schools should provide improved opportunities for better assessment and understanding of children´s involvement in PA.
A new typology of work schedules: Evidence from a cross-sectional study among nurses working in residential elder care.

PubMed

Peters, V; de Rijk, A; Engels, J; Heerkens, Y; Nijhuis, F

2016-04-07

Work schedules contribute substantially to the health and well-being of nurses. Too broad typologies are used in research that do not meet the current variety in work schedules. To develop a new typology for nurses' work schedules based on five requirements and to validate the typology. This study is based on a questionnaire returned by 498 nurses (response 51%) including questions regarding nurses' work schedule, socio-demographic, and family characteristics and their appraisal of the work schedule. Frequencies of the different schedules were computed to determine the typology. To validate the typology, differences between the types were tested with ANOVAs, Chi2 and Kruskal-Wallis tests. Five main types can be distinguished based on predetermined requirements and frequencies, namely: (1) fixed early shift, (2) rotating two shift pattern without night shift, (3) rotating three shift pattern, (4) fixed and rotating two shift pattern including night shift, and (5) fixed normal day or afternoon shifts. Nurses in these types of work schedule differed significantly with respect to hours worked, days off between shifts, age, education, years in the job, commuting time, contribution to household income, satisfaction with work schedule and work schedule control. Especially nurses with type 3 schedules differed from other types. A typology of five main types of work schedules is proposed. Content validity of the typology is sufficient and the new typology seems useful for research on work-related aspects of nursing.
Cross-cultural validity of standardized motor development screening and assessment tools: a systematic review.

PubMed

Mendonça, Bianca; Sargent, Barbara; Fetters, Linda

2016-12-01

To investigate whether standardized motor development screening and assessment tools that are used to evaluate motor abilities of children aged 0 to 2 years are valid in cultures other than those in which the normative sample was established. This was a systematic review in which six databases were searched. Studies were selected based on inclusion/exclusion criteria and appraised for evidence level and quality. Study variables were extracted. Twenty-three studies representing six motor development screening and assessment tools in 16 cultural contexts met the inclusion criteria: Alberta Infant Motor Scale (n=7), Ages and Stages Questionnaire, 3rd edition (n=2), Bayley Scales of Infant and Toddler Development, 3rd edition (n=8), Denver Developmental Screening Test, 2nd edition (n=4), Harris Infant Neuromotor Test (n=1), and Peabody Developmental Motor Scales, 2nd edition (n=1). Thirteen studies found significant differences between the cultural context and normative sample. Two studies established reliability and/or validity of standardized motor development assessments in high-risk infants from different cultural contexts. Five studies established new population norms. Eight studies described the cross-cultural adaptation of a standardized motor development assessment. Standardized motor development assessments have limited validity in cultures other than that in which the normative sample was established. Their use can result in under- or over-referral for services. © 2016 Mac Keith Press.
Translation, cross-cultural adaptation, and validation of the french version of the 15-item Myasthenia Gravis Quality Of life scale.

PubMed

Birnbaum, Simone; Ghout, Idir; Demeret, Sophie; Bolgert, Francis; Eymard, Bruno; Sharshar, Tarek; Portero, Pierre; Hogrel, Jean-Yves

2017-05-01

Evaluation of quality of life (QOL) has become essential in healthcare. Currently no MG-specific QOL measure exists in French. The aim of this study was to translate, culturally adapt, and evaluate the psychometric properties of the French version of the 15-Item Myasthenia Gravis Quality of Life Scale (MG-QOL15) scale for French myasthenia patients. Translation and cross-cultural adaption of the MG-QOL15 was performed, followed by reliability and validity evaluations. One hundred and twenty-five patients were included. Internal consistency was excellent (Cronbach α = 0.92) as was test-retest reliability (ICC = 0.92, 95% CI 0.86-0.96). Concurrent validity was good for both clinical scores (myasthenic muscle score: ρ = -0.52, P < 0.001; Myasthenia Gravis-Activities of Daily Living scale score: ρ = 0.62, P < 0.001). Correlations were strongest for overall QOL (ρ = 0.62, P < 0.001) and physical health (ρ = 0.67, P < 0.001) on the World Health Organization Quality of Life short score (WHO-QOL BREF). The French version of the MG-QOL15 is valid and reliable and is now available for use with French-speaking patients. Muscle Nerve, 2016 Muscle Nerve 55: 639-645, 2017. © 2016 Wiley Periodicals, Inc.
Cross-cultural adaptation and validation of the Condom Self-Efficacy Scale: application to Brazilian adolescents and young adults 1

PubMed Central

de Sousa, Carla Suellen Pires; Castro, Régia Christina Moura Barbosa; Pinheiro, Ana Karina Bezerra; Moura, Escolástica Rejane Ferreira; Almeida, Paulo César; Aquino, Priscila de Souza

2018-01-01

ABSTRACT Objective: translate and adapt the Condom Self-Efficacy Scale to Portuguese in the Brazilian context. The scale originated in the United States and measures self-efficacy in condom use. Method: methodological study in two phases: translation, cross-cultural adaptation and verification of psychometric properties. The translation and adaptation process involved four translators, one mediator of the synthesis and five health professionals. The content validity was verified using the Content Validation Index, based on 22 experts’ judgments. Forty subjects participated in the pretest, who contributed to the understanding of the scale items. The scale was applied to 209 students between 13 and 26 years of age from a school affiliated with the state-owned educational network. The reliability was analyzed by means of Cronbach’s alpha. Results: the Portuguese version of the scale obtained a Cronbach’s alpha coefficient of 0.85 and the total mean score was 68.1 points. A statistically significant relation was found between the total scale and the variables not having children (p= 0.038), condom use (p= 0.008) and condom use with fixed partner (p=0.036). Conclusion: the Brazilian version of the Condom Self-Efficacy Scale is a valid and reliable tool to verify the self-efficacy in condom use among adolescents and young adults. PMID:29319748
Circulating, cell-free DNA as a marker for exercise load in intermittent sports.

PubMed

Haller, Nils; Helmig, Susanne; Taenny, Pascal; Petry, Julian; Schmidt, Sebastian; Simon, Perikles

2018-01-01

Attempts to establish a biomarker reflecting individual player load in intermittent sports such as football have failed so far. Increases in circulating DNA (cfDNA) have been demonstrated in various endurance sports settings. While it has been proposed that cfDNA could be a suitable marker for player load in intermittent sports, the effects on cfDNA of repeated sprinting as an essential feature in intermittent sports are unknown. For the first time, we assessed both alterations of cfDNA due to repeated maximal sprints and due to a professional football game. Nine participants were subjected to a standardised sprint training session with cross-over design of five maximal sprints of 40 meters with either "short" (1 minute) or "long" pauses (5 minutes). Capillary cfDNA and lactate were measured after every sprint and venous cfDNA before and after each series of sprints. Moreover, capillary cfDNA and lactate values were taken in 23 professional football players before and after incremental exercise testing, during the course of a training week at rest (baseline) and in all 17 enrolled players following a season game. Lactate and venous cfDNA increased more pronounced during "short" compared to "long" (1.4-fold, p = 0.032 and 1.7-fold, p = 0.016) and cfDNA correlated significantly with lactate (r = 0.69; p<0.001). Incremental exercise testing increased cfDNA 7.0-fold (p<0.001). The season game increased cfDNA 22.7-fold (p<0.0001), while lactate showed a 2.0-fold (p = 0.09) increase compared to baseline. Fold-changes in cfDNA correlated with distance covered during game (spearman's r = 0.87, p = 0.0012), while no correlation between lactate and the tracking data could be found. We show for the first time that cfDNA could be an objective marker for distance covered in elite intermittent sports. In contrast to the potential of more established blood-based markers like IL-6, CK, or CRP, cfDNA shows by far the strongest fold-change and a high correlation with a particular load related aspect in professional football.
Circulating, cell-free DNA as a marker for exercise load in intermittent sports

PubMed Central

Haller, Nils; Helmig, Susanne; Taenny, Pascal; Petry, Julian; Schmidt, Sebastian

2018-01-01

Background Attempts to establish a biomarker reflecting individual player load in intermittent sports such as football have failed so far. Increases in circulating DNA (cfDNA) have been demonstrated in various endurance sports settings. While it has been proposed that cfDNA could be a suitable marker for player load in intermittent sports, the effects on cfDNA of repeated sprinting as an essential feature in intermittent sports are unknown. For the first time, we assessed both alterations of cfDNA due to repeated maximal sprints and due to a professional football game. Methods Nine participants were subjected to a standardised sprint training session with cross-over design of five maximal sprints of 40 meters with either “short” (1 minute) or “long” pauses (5 minutes). Capillary cfDNA and lactate were measured after every sprint and venous cfDNA before and after each series of sprints. Moreover, capillary cfDNA and lactate values were taken in 23 professional football players before and after incremental exercise testing, during the course of a training week at rest (baseline) and in all 17 enrolled players following a season game. Results Lactate and venous cfDNA increased more pronounced during “short” compared to “long” (1.4-fold, p = 0.032 and 1.7-fold, p = 0.016) and cfDNA correlated significantly with lactate (r = 0.69; p<0.001). Incremental exercise testing increased cfDNA 7.0-fold (p<0.001). The season game increased cfDNA 22.7-fold (p<0.0001), while lactate showed a 2.0-fold (p = 0.09) increase compared to baseline. Fold-changes in cfDNA correlated with distance covered during game (spearman’s r = 0.87, p = 0.0012), while no correlation between lactate and the tracking data could be found. Discussion We show for the first time that cfDNA could be an objective marker for distance covered in elite intermittent sports. In contrast to the potential of more established blood-based markers like IL-6, CK, or CRP, cfDNA shows by far the strongest fold-change and a high correlation with a particular load related aspect in professional football. PMID:29370268
Comparative Analysis of Pharmacophore Features and Quantitative Structure-Activity Relationships for CD38 Covalent and Non-covalent Inhibitors.

PubMed

Zhang, Shuang; Xue, Xiwen; Zhang, Liangren; Zhang, Lihe; Liu, Zhenming

2015-12-01

In the past decade, the discovery, synthesis, and evaluation for hundreds of CD38 covalent and non-covalent inhibitors has been reported sequentially by our group and partners; however, a systematic structure-based guidance is still lacking for rational design of CD38 inhibitor. Here, we carried out a comparative analysis of pharmacophore features and quantitative structure-activity relationships for CD38 inhibitors. The results uncover that the essential interactions between key residues and covalent/non-covalent CD38 inhibitors include (i) hydrogen bond and hydrophobic interactions with residues Glu226 and Trp125, (ii) electrostatic or hydrogen bond interaction with the positively charged residue Arg127 region, and (iii) the hydrophobic interaction with residue Trp189. For covalent inhibitors, besides the covalent effect with residue Glu226, the electrostatic interaction with residue Arg127 is also necessary, while another hydrogen/non-bonded interaction with residues Trp125 and Trp189 can also be detected. By means of the SYBYL multifit alignment function, the best CoMFA and CoMSIA with CD38 covalent inhibitors presented cross-validated correlation coefficient values (q(2)) of 0.564 and 0.571, and non-cross-validated values (r(2)) of 0.967 and 0.971, respectively. The CD38 non-covalent inhibitors can be classified into five groups according to their chemical scaffolds, and the residues Glu226, Trp189, and Trp125 are indispensable for those non-covalent inhibitors binding to CD38, while the residues Ser126, Arg127, Asp155, Thr221, and Phe222 are also important. The best CoMFA and CoMSIA with the F12 analogues presented cross-validated correlation coefficient values (q(2)) of 0.469 and 0.454, and non-cross-validated values (r(2)) of 0.814 and 0.819, respectively. © 2015 John Wiley & Sons A/S.
The PDB_REDO server for macromolecular structure model optimization.

PubMed

Joosten, Robbie P; Long, Fei; Murshudov, Garib N; Perrakis, Anastassis

2014-07-01

The refinement and validation of a crystallographic structure model is the last step before the coordinates and the associated data are submitted to the Protein Data Bank (PDB). The success of the refinement procedure is typically assessed by validating the models against geometrical criteria and the diffraction data, and is an important step in ensuring the quality of the PDB public archive [Read et al. (2011 ▶), Structure, 19, 1395-1412]. The PDB_REDO procedure aims for 'constructive validation', aspiring to consistent and optimal refinement parameterization and pro-active model rebuilding, not only correcting errors but striving for optimal interpretation of the electron density. A web server for PDB_REDO has been implemented, allowing thorough, consistent and fully automated optimization of the refinement procedure in REFMAC and partial model rebuilding. The goal of the web server is to help practicing crystallo-graphers to improve their model prior to submission to the PDB. For this, additional steps were implemented in the PDB_REDO pipeline, both in the refinement procedure, e.g. testing of resolution limits and k-fold cross-validation for small test sets, and as new validation criteria, e.g. the density-fit metrics implemented in EDSTATS and ligand validation as implemented in YASARA. Innovative ways to present the refinement and validation results to the user are also described, which together with auto-generated Coot scripts can guide users to subsequent model inspection and improvement. It is demonstrated that using the server can lead to substantial improvement of structure models before they are submitted to the PDB.
Assessing a Top-Down Modeling Approach for Seasonal Scale Snow Sensitivity

NASA Astrophysics Data System (ADS)

Luce, C. H.; Lute, A.

2017-12-01

Mechanistic snow models are commonly applied to assess changes to snowpacks in a warming climate. Such assessments involve a number of assumptions about details of weather at daily to sub-seasonal time scales. Models of season-scale behavior can provide contrast for evaluating behavior at time scales more in concordance with climate warming projections. Such top-down models, however, involve a degree of empiricism, with attendant caveats about the potential of a changing climate to affect calibrated relationships. We estimated the sensitivity of snowpacks from 497 Snowpack Telemetry (SNOTEL) stations in the western U.S. based on differences in climate between stations (spatial analog). We examined the sensitivity of April 1 snow water equivalent (SWE) and mean snow residence time (SRT) to variations in Nov-Mar precipitation and average Nov-Mar temperature using multivariate local-fit regressions. We tested the modeling approach using a leave-one-out cross-validation as well as targeted two-fold non-random cross-validations contrasting, for example, warm vs. cold years, dry vs. wet years, and north vs. south stations. Nash-Sutcliffe Efficiency (NSE) values for the validations were strong for April 1 SWE, ranging from 0.71 to 0.90, and still reasonable, but weaker, for SRT, in the range of 0.64 to 0.81. From these ranges, we exclude validations where the training data do not represent the range of target data. A likely reason for differences in validation between the two metrics is that the SWE model reflects the influence of conservation of mass while using temperature as an indicator of the season-scale energy balance; in contrast, SRT depends more strongly on the energy balance aspects of the problem. Model forms with lower numbers of parameters generally validated better than more complex model forms, with the caveat that pseudoreplication could encourage selection of more complex models when validation contrasts were weak. Overall, the split sample validations confirm transferability of the relationships in space and time contingent upon full representation of validation conditions in the calibration data set. The ability of the top-down space-for-time models to predict in new time periods and locations lends confidence to their application for assessments and for improving finer time scale models.
Cross-resistance and Inheritance of Resistance to Emamectin Benzoate in Spodoptera exigua (Lepidoptera: Noctuidae).

PubMed

Che, Wunan; Huang, Jianlei; Guan, Fang; Wu, Yidong; Yang, Yihua

2015-08-01

Beet armyworm, Spodoptera exigua (Hübner), is a worldwide pest of many crops. Chemical insecticides are heavily used for its control in China, and serious resistance has been evolved in the field to a variety of insecticides including emamectin benzoate. Through repeated backcrossing to a susceptible strain (WH-S) and selection with emamectin benzoate, the trait conferring resistance to emamectin benzoate in a field-collected population of S. exigua (moderately resistant to emamectin benzoate and strongly resistant to pyrethroids and indoxacarb) was introgressed into WH-S to generate a near-isogenic resistant strain (WH-EB). Compared with WH-S, the WH-EB strain developed a 1,110-fold resistance to emamectin benzoate and a high level of cross-resistance to abamectin (202-fold), with low levels of cross-resistance to cypermethrin (10-fold) and chlorfluazuron (7-fold), but no cross-resistance to representatives of another six different classes of insecticides (chlorantraniliprole, chlorfenapyr, indoxacarb, spinosad, tebufenozide, and chlorpyrifos). Resistance to emamectin benzoate in WH-EB was autosomal, incompletely dominant, and polygenic. Limited cross-resistance in WH-EB indicates that emamectin benzoate can be rotated with other classes of insecticides to which it does not show cross-resistance to delay the evolution of resistance in S. exigua. The incompletely dominant nature of resistance in S. exigua may explain the rapid evolution of resistance to emamectin benzoate in the field, and careful deployment of this chemical within a resistance management program should be considered. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Measuring the learning capacity of organisations: development and factor analysis of the Questionnaire for Learning Organizations.

PubMed

Oudejans, S C C; Schippers, G M; Schramade, M H; Koeter, M W J; van den Brink, W

2011-04-01

To investigate internal consistency and factor structure of a questionnaire measuring learning capacity based on Senge's theory of the five disciplines of a learning organisation: Personal Mastery, Mental Models, Shared Vision, Team Learning, and Systems Thinking. Cross-sectional study. Substance-abuse treatment centres (SATCs) in The Netherlands. A total of 293 SATC employees from outpatient and inpatient treatment departments, financial and human resources departments. Psychometric properties of the Questionnaire for Learning Organizations (QLO), including factor structure, internal consistency, and interscale correlations. A five-factor model representing the five disciplines of Senge showed good fit. The scales for Personal Mastery, Shared Vision and Team Learning had good internal consistency, but the scales for Systems Thinking and Mental Models had low internal consistency. The proposed five-factor structure was confirmed in the QLO, which makes it a promising instrument to assess learning capacity in teams. The Systems Thinking and the Mental Models scales have to be revised. Future research should be aimed at testing criterion and discriminatory validity.
An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank.

PubMed

Sharma, Manish; Goyal, Deepanshu; Achuth, P V; Acharya, U Rajendra

2018-07-01

Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique. Copyright © 2018 Elsevier Ltd. All rights reserved.
Assessing the performance of aerial image point cloud and spectral metrics in predicting boreal forest canopy cover

NASA Astrophysics Data System (ADS)

Melin, M.; Korhonen, L.; Kukkonen, M.; Packalen, P.

2017-07-01

Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps. The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n = 1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral + pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation. The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.
Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model

PubMed Central

Wan, Tao; Madabhushi, Anant; Phinikaridou, Alkystis; Hamilton, James A.; Hua, Ning; Pham, Tuan; Danagoulian, Jovanna; Kleiman, Ross; Buckler, Andrew J.

2014-01-01

Purpose: To develop a new spatio-temporal texture (SpTeT) based method for distinguishing vulnerable versus stable atherosclerotic plaques on DCE-MRI using a rabbit model of atherothrombosis. Methods: Aortic atherosclerosis was induced in 20 New Zealand White rabbits by cholesterol diet and endothelial denudation. MRI was performed before (pretrigger) and after (posttrigger) inducing plaque disruption with Russell's-viper-venom and histamine. Of the 30 vascular targets (segments) under histology analysis, 16 contained thrombus (vulnerable) and 14 did not (stable). A total of 352 voxel-wise computerized SpTeT features, including 192 Gabor, 36 Kirsch, 12 Sobel, 52 Haralick, and 60 first-order textural features, were extracted on DCE-MRI to capture subtle texture changes in the plaques over the course of contrast uptake. Different combinations of SpTeT feature sets, in which the features were ranked by a minimum-redundancy-maximum-relevance feature selection technique, were evaluated via a random forest classifier. A 500 iterative 2-fold cross validation was performed for discriminating the vulnerable atherosclerotic plaque and stable atherosclerotic plaque on per voxel basis. Four quantitative metrics were utilized to measure the classification results in separating between vulnerable and stable plaques. Results: The quantitative results show that the combination of five classes of SpTeT features can distinguish between vulnerable (disrupted plaques with an overlying thrombus) and stable plaques with the best AUC values of 0.9631 ± 0.0088, accuracy of 89.98% ± 0.57%, sensitivity of 83.71% ± 1.71%, and specificity of 94.55% ± 0.48%. Conclusions: Vulnerable and stable plaque can be distinguished by SpTeT based features. The SpTeT features, following validation on larger datasets, could be established as effective and reliable imaging biomarkers for noninvasively assessing atherosclerotic risk. PMID:24694153

A proposal for a comprehensive risk scoring system for predicting postoperative complications in octogenarian patients with medically operable lung cancer: JACS1303.

PubMed

Saji, Hisashi; Ueno, Takahiko; Nakamura, Hiroshige; Okumura, Norihito; Tsuchida, Masanori; Sonobe, Makoto; Miyazaki, Takuro; Aokage, Keiju; Nakao, Masayuki; Haruki, Tomohiro; Ito, Hiroyuki; Kataoka, Kazuhiko; Okabe, Kazunori; Tomizawa, Kenji; Yoshimoto, Kentaro; Horio, Hirotoshi; Sugio, Kenji; Ode, Yasuhisa; Takao, Motoshi; Okada, Morihito; Chida, Masayuki

2018-04-01

Although some retrospective studies have reported clinicopathological scoring systems for predicting postoperative complications and survival outcomes for elderly lung cancer patients, optimized scoring systems remain controversial. The Japanese Association for Chest Surgery (JACS) conducted a nationwide multicentre prospective cohort and enrolled a total of 1019 octogenarians with medically operable lung cancer. Details of the clinical factors, comorbidities and comprehensive geriatric assessment were recorded for 895 patients to develop a comprehensive risk scoring (RS) system capable of predicting severe complications. Operative (30 days) and hospital mortality rates were 1.0% and 1.6%, respectively. Complications were observed in 308 (34%) patients, of whom 81 (8.4%) had Grade 3-4 severe complications. Pneumonia was the most common severe complication, observed in 27 (3.0%) patients. Five predictive factors, gender, comprehensive geriatric assessment75: memory and Simplified Comorbidity Score (SCS): diabetes mellitus, albumin and percentage vital capacity, were identified as independent predictive factors for severe postoperative complications (odds ratio = 2.73, 1.86, 1.54, 1.66 and 1.61, respectively) through univariate and multivariate analyses. A 5-fold cross-validation was performed as an internal validation to reconfirm these 5 predictive factors (average area under the curve 0.70). We developed a simplified RS system as follows: RS = 3 (gender: male) + 2 (comprehensive geriatric assessment 75: memory: yes) + 2 (albumin: <3.8 ng/ml) + 1 (percentage vital capacity: ≤90) + 1 (SCS: diabetes mellitus: yes). The current series shows that octogenarians can be successfully treated for lung cancer with surgical resection with an acceptable rate of severe complications and mortality. We propose a simplified RS system to predict severe complications in octogenarian patients with medically operative lung cancer. JACS1303 (UMIN000016756).
Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities.

PubMed

Nguyen, Van-Nui; Huang, Kai-Yao; Huang, Chien-Hsun; Chang, Tzu-Hao; Bretaña, Neil; Lai, K; Weng, Julia; Lee, Tzong-Yi

2015-01-01

In eukaryotes, ubiquitin-conjugation is an important mechanism underlying proteasome-mediated degradation of proteins, and as such, plays an essential role in the regulation of many cellular processes. In the ubiquitin-proteasome pathway, E3 ligases play important roles by recognizing a specific protein substrate and catalyzing the attachment of ubiquitin to a lysine (K) residue. As more and more experimental data on ubiquitin conjugation sites become available, it becomes possible to develop prediction models that can be scaled to big data. However, no development that focuses on the investigation of ubiquitinated substrate specificities has existed. Herein, we present an approach that exploits an iteratively statistical method to identify ubiquitin conjugation sites with substrate site specificities. In this investigation, totally 6259 experimentally validated ubiquitinated proteins were obtained from dbPTM. After having filtered out homologous fragments with 40% sequence identity, the training data set contained 2658 ubiquitination sites (positive data) and 5532 non-ubiquitinated sites (negative data). Due to the difficulty in characterizing the substrate site specificities of E3 ligases by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. The profile hidden Markov model (profile HMM) was adopted to construct the predictive models learned from the identified substrate motifs. A five-fold cross validation was then used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 73.07%, 65.46%, and 67.93%, respectively. Additionally, an independent testing set, completely blind to the training data of the predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (76.13%) and outperform other ubiquitination site prediction tool. A case study demonstrated the effectiveness of the characterized substrate motifs for identifying ubiquitination sites. The proposed method presents a practical means of preliminary analysis and greatly diminishes the total number of potential targets required for further experimental confirmation. This method may help unravel their mechanisms and roles in E3 recognition and ubiquitin-mediated protein degradation.
From sensor data to animal behaviour: an oystercatcher example.

PubMed

Shamoun-Baranes, Judy; Bom, Roeland; van Loon, E Emiel; Ens, Bruno J; Oosterbeek, Kees; Bouten, Willem

2012-01-01

Animal-borne sensors enable researchers to remotely track animals, their physiological state and body movements. Accelerometers, for example, have been used in several studies to measure body movement, posture, and energy expenditure, although predominantly in marine animals. In many studies, behaviour is often inferred from expert interpretation of sensor data and not validated with direct observations of the animal. The aim of this study was to derive models that could be used to classify oystercatcher (Haematopus ostralegus) behaviour based on sensor data. We measured the location, speed, and tri-axial acceleration of three oystercatchers using a flexible GPS tracking system and conducted simultaneous visual observations of the behaviour of these birds in their natural environment. We then used these data to develop three supervised classification trees of behaviour and finally applied one of the models to calculate time-activity budgets. The model based on accelerometer data developed to classify three behaviours (fly, terrestrial locomotion, and no movement) was much more accurate (cross-validation error = 0.14) than the model based on GPS-speed alone (cross-validation error = 0.35). The most parsimonious acceleration model designed to classify eight behaviours could distinguish five: fly, forage, body care, stand, and sit (cross-validation error = 0.28); other behaviours that were observed, such as aggression or handling of prey, could not be distinguished. Model limitations and potential improvements are discussed. The workflow design presented in this study can facilitate model development, be adapted to a wide range of species, and together with the appropriate measurements, can foster the study of behaviour and habitat use of free living animals throughout their annual routine.
Cross-cultural adaptation and validation of a Bengali version of the modified fibromyalgia impact questionnaire.

PubMed

Muquith, Mohammed A; Islam, Md Nazrul; Haq, Syed A; Ten Klooster, Peter M; Rasker, Johannes J; Yunus, Muhammad B

2012-08-27

Currently, no validated instruments are available to measure the health status of Bangladeshi patients with fibromyalgia (FM). The aims of this study were to cross-culturally adapt the modified Fibromyalgia Impact Questionnaire (FIQ) into Bengali (B-FIQ) and to test its validity and reliability in Bangladeshi patients with FM. The FIQ was translated following cross-cultural adaptation guidelines and pretested in 30 female patients with FM. Next, the adapted B-FIQ was physician-administered to 102 consecutive female FM patients together with the Health Assessment Questionnaire (HAQ), selected subscales of the SF-36, and visual analog scales for current clinical symptoms. A tender point count (TPC) was performed by an experienced rheumatologist. Forty randomly selected patients completed the B-FIQ again after 7 days. Two control groups of 50 healthy people and 50 rheumatoid arthritis (RA) patients also completed the B-FIQ. For the final B-FIQ, five physical function sub-items were replaced with culturally appropriate equivalents. Internal consistency was adequate for both the 11-item physical function subscale (α = 0.73) and the total scale (α = 0.83). With exception of the physical function subscale, expected correlations were generally observed between the B-FIQ items and selected subscales of the SF-36, HAQ, clinical symptoms, and TPC. The B-FIQ was able to discriminate between FM patients and healthy controls and between FM patients and RA patients. Test-retest reliability was adequate for the physical function subscale (r = 0.86) and individual items (r = 0.73-0.86), except anxiety (r = 0.27) and morning tiredness (r = 0.64). This study supports the reliability and validity of the B-FIQ as a measure of functional disability and health status in Bangladeshi women with FM.
Cross-cultural adaptation, reliability, and validity of the work role functioning questionnaire to Brazilian Portuguese.

PubMed

Gallasch, Cristiane Helena; Alexandre, Neusa Maria Costa; Amick, Benjamin

2007-12-01

The study objectives were to translate and adapt the Work Role Functioning Questionnaire (WRFQ) into the Brazilian Portuguese language and evaluate its reliability in patients experiencing musculoskeletal disorders. The cross-cultural adaptation was performed according to the internationally recommended methodology, using the following guidelines: translation, back-translation, revision by a committee, and pretest. At first, the questionnaire was independently translated by two bilingual translators, who had Portuguese as their mother language. Subsequently, two other translators whose mother language was English did the back-translation. A committee composed of five specialists revised and compared the translations obtained, developing the final version for pretest application. The pretest was carried out with 30 patients experiencing musculoskeletal disorders. Psychometric properties were evaluated by administering the questionnaire to 105 subjects with musculoskeletal disorders and receiving physical therapy treatment. The reliability was estimated through stability and homogeneity assessment. The construct validity was tested comparing subjects experiencing musculoskeletal disorders to healthy workers. The results indicated good content validity and internal consistency (Cronbach alpha = 0.95). Cronbach alpha for each scale was >0.85, except for the social demand scale. The Intraclass Correlation Coefficient for the test-retest reliability was satisfactory for mental demands (ICC = 0.68) and excellent for the others (0.82-0.91). In relation to the construct validity, the mean score obtained for each scale was lower for physical, work scheduling, and output demands in the subjects with musculoskeletal disorders. There was a significant difference (p < 0.001) between the groups in comparison to work scheduling, physical, and output demands. The data showed that the cross-cultural adaptation process was successful and the adapted instrument demonstrated psychometric properties making it reliable to use in Brazilian culture.
Development of prediction equations for estimating appendicular skeletal muscle mass in Japanese men and women.

PubMed

Furushima, Taishi; Miyachi, Motohiko; Iemitsu, Motoyuki; Murakami, Haruka; Kawano, Hiroshi; Gando, Yuko; Kawakami, Ryoko; Sanada, Kiyoshi

2017-08-29

This study aimed to develop and cross-validate prediction equations for estimating appendicular skeletal muscle mass (ASM) and to examine the relationship between sarcopenia defined by the prediction equations and risk factors for cardiovascular diseases (CVD) or osteoporosis in Japanese men and women. Subjects were healthy men and women aged 20-90 years, who were randomly allocated to the following two groups: the development group (D group; 257 men, 913 women) and the cross-validation group (V group; 119 men, 112 women). To develop prediction equations, stepwise multiple regression analyses were performed on data obtained from the D group, using ASM measured by dual-energy X-ray absorptiometry (DXA) as a dependent variable and five easily obtainable measures (age, height, weight, waist circumference, and handgrip strength) as independent variables. When the prediction equations for ASM estimation were applied to the V group, a significant correlation was found between DXA-measured ASM and predicted ASM in both men and women (R 2 = 0.81 and R 2 = 0.72). Our prediction equations had higher R 2 values compared to previously developed equations (R 2 = 0.75-0.59 and R 2 = 0.69-0.40) in both men and women. Moreover, sarcopenia defined by predicted ASM was related to risk factors for osteoporosis and CVD, as well as sarcopenia defined by DXA-measured ASM. In this study, novel prediction equations were developed and cross-validated in Japanese men and women. Our analyses validated the clinical significance of these prediction equations and showed that previously reported equations were not applicable in a Japanese population.
Efficient selective screening for heart failure in elderly men and women from the community: A diagnostic individual participant data meta-analysis

PubMed Central

Kievit, Rogier F; Hoes, Arno W; Bots, Michiel L; van Riet, Evelien ES; van Mourik, Yvonne; Bertens, Loes CM; Boonman-de Winter, Leandra JM; den Ruijter, Hester M; Rutten, Frans H

2018-01-01

Background Prevalence of undetected heart failure in older individuals is high in the community, with patients being at increased risk of morbidity and mortality due to the chronic and progressive nature of this complex syndrome. An essential, yet currently unavailable, strategy to pre-select candidates eligible for echocardiography to confirm or exclude heart failure would identify patients earlier, enable targeted interventions and prevent disease progression. The aim of this study was therefore to develop and validate such a model that can be implemented clinically. Methods and results Individual patient data from four primary care screening studies were analysed. From 1941 participants >60 years old, 462 were diagnosed with heart failure, according to criteria of the European Society of Cardiology heart failure guidelines. Prediction models were developed in each cohort followed by cross-validation, omitting each of the four cohorts in turn. The model consisted of five independent predictors; age, history of ischaemic heart disease, exercise-related shortness of breath, body mass index and a laterally displaced/broadened apex beat, with no significant interaction with sex. The c-statistic ranged from 0.70 (95% confidence interval (CI) 0.64–0.76) to 0.82 (95% CI 0.78–0.87) at cross-validation and the calibration was reasonable with Observed/Expected ratios ranging from 0.86 to 1.15. The clinical model improved with the addition of N-terminal pro B-type natriuretic peptide with the c-statistic increasing from 0.76 (95% CI 0.70–0.81) to 0.89 (95% CI 0.86–0.92) at cross-validation. Conclusion Easily obtainable patient characteristics can select older men and women from the community who are candidates for echocardiography to confirm or refute heart failure. PMID:29327942
Efficient selective screening for heart failure in elderly men and women from the community: A diagnostic individual participant data meta-analysis.

PubMed

Kievit, Rogier F; Gohar, Aisha; Hoes, Arno W; Bots, Michiel L; van Riet, Evelien Es; van Mourik, Yvonne; Bertens, Loes Cm; Boonman-de Winter, Leandra Jm; den Ruijter, Hester M; Rutten, Frans H

2018-03-01

Background Prevalence of undetected heart failure in older individuals is high in the community, with patients being at increased risk of morbidity and mortality due to the chronic and progressive nature of this complex syndrome. An essential, yet currently unavailable, strategy to pre-select candidates eligible for echocardiography to confirm or exclude heart failure would identify patients earlier, enable targeted interventions and prevent disease progression. The aim of this study was therefore to develop and validate such a model that can be implemented clinically. Methods and results Individual patient data from four primary care screening studies were analysed. From 1941 participants >60 years old, 462 were diagnosed with heart failure, according to criteria of the European Society of Cardiology heart failure guidelines. Prediction models were developed in each cohort followed by cross-validation, omitting each of the four cohorts in turn. The model consisted of five independent predictors; age, history of ischaemic heart disease, exercise-related shortness of breath, body mass index and a laterally displaced/broadened apex beat, with no significant interaction with sex. The c-statistic ranged from 0.70 (95% confidence interval (CI) 0.64-0.76) to 0.82 (95% CI 0.78-0.87) at cross-validation and the calibration was reasonable with Observed/Expected ratios ranging from 0.86 to 1.15. The clinical model improved with the addition of N-terminal pro B-type natriuretic peptide with the c-statistic increasing from 0.76 (95% CI 0.70-0.81) to 0.89 (95% CI 0.86-0.92) at cross-validation. Conclusion Easily obtainable patient characteristics can select older men and women from the community who are candidates for echocardiography to confirm or refute heart failure.
Effect of Replacing Race with Apolipoprotein L1 Genotype in Calculation of Kidney Donor Risk Index

PubMed Central

Julian, B. A.; Gaston, R. S.; Brown, W. M.; Reeves-Daniel, A. M.; Israni, A. K.; Schladt, D. P.; Pastan, S. O.; Mohan, S.; Freedman, B. I.; Divers, J.

2016-01-01

Renal allografts from deceased African Americans with two apolipoprotein L1 gene (APOL1) renal-risk variants fail sooner than kidneys from donors with fewer variants. Kidney Donor Risk Index (KDRI) was developed to evaluate organ offers by predicting allograft longevity and includes African American race as a risk factor. Substituting APOL1 genotype for race may refine the KDRI. For 622 deceased African American kidney donors, we applied 10-fold cross-validation approach to estimate contribution of APOL1 variants to a revised KDRI. Cross-validation was repeated 10,000 times to generate distribution of effect size associated with APOL1 genotype. Average effect size was used to derive the revised KDRI weighting. Mean current-KDRI score for all donors was 1.4930 versus mean revised-KDRI score 1.2518 for 529 donors with 0/1 variant and 1.8527 for 93 donors with 2 variants. Original and revised KDRIs had comparable survival prediction errors after transplantation, but the spread in Kidney Donor Profile Index based on presence/absence of 2 APOL1 variants was 37 percentage points. Replacing donor race with APOL1 genotype in KDRI better defines risk associated with kidneys transplanted from deceased African American donors, substantially improves KDRI score for 85-90% of kidneys offered, and enhances the link between donor quality and recipient need. PMID:27862962
Evaluation of polygenic risk scores for predicting breast and prostate cancer risk.

PubMed

Machiela, Mitchell J; Chen, Chia-Yen; Chen, Constance; Chanock, Stephen J; Hunter, David J; Kraft, Peter

2011-09-01

Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores. © 2011 Wiley-Liss, Inc.
Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

PubMed Central

Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte; Specht, Lena; von Buchwald, Christian

2017-01-01

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection. PMID:28212555
The prediction of palmitoylation site locations using a multiple feature extraction method.

PubMed

Shi, Shao-Ping; Sun, Xing-Yu; Qiu, Jian-Ding; Suo, Sheng-Bao; Chen, Xiang; Huang, Shu-Yun; Liang, Ru-Ping

2013-03-01

As an extremely important and ubiquitous post-translational lipid modification, palmitoylation plays a significant role in a variety of biological and physiological processes. Unlike other lipid modifications, protein palmitoylation and depalmitoylation are highly dynamic and can regulate both protein function and localization. The dynamic nature of palmitoylation is poorly understood because of the limitations in current assay methods. The in vivo or in vitro experimental identification of palmitoylation sites is both time consuming and expensive. Due to the large volume of protein sequences generated in the post-genomic era, it is extraordinarily important in both basic research and drug discovery to rapidly identify the attributes of a new protein's palmitoylation sites. In this work, a new computational method, WAP-Palm, combining multiple feature extraction, has been developed to predict the palmitoylation sites of proteins. The performance of the WAP-Palm model is measured herein and was found to have a sensitivity of 81.53%, a specificity of 90.45%, an accuracy of 85.99% and a Matthews correlation coefficient of 72.26% in 10-fold cross-validation test. The results obtained from both the cross-validation and independent tests suggest that the WAP-Palm model might facilitate the identification and annotation of protein palmitoylation locations. The online service is available at http://bioinfo.ncu.edu.cn/WAP-Palm.aspx. Copyright © 2013 Elsevier Inc. All rights reserved.
H-Bond Self-Assembly: Folding versus Duplex Formation.

PubMed

Núñez-Villanueva, Diego; Iadevaia, Giulia; Stross, Alexander E; Jinks, Michael A; Swain, Jonathan A; Hunter, Christopher A

2017-05-17

Linear oligomers equipped with complementary H-bond donor (D) and acceptor (A) sites can interact via intermolecular H-bonds to form duplexes or fold via intramolecular H-bonds. These competing equilibria have been quantified using NMR titration and dilution experiments for seven systems featuring different recognition sites and backbones. For all seven architectures, duplex formation is observed for homo-sequence 2-mers (AA·DD) where there are no competing folding equilibria. The corresponding hetero-sequence AD 2-mers also form duplexes, but the observed self-association constants are strongly affected by folding equilibria in the monomeric states. When the backbone is flexible (five or more rotatable bonds separating the recognition sites), intramolecular H-bonding is favored, and the folded state is highly populated. For these systems, the stability of the AD·AD duplex is 1-2 orders of magnitude lower than that of the corresponding AA·DD duplex. However, for three architectures which have more rigid backbones (fewer than five rotatable bonds), intramolecular interactions are not observed, and folding does not compete with duplex formation. These systems are promising candidates for the development of longer, mixed-sequence synthetic information molecules that show sequence-selective duplex formation.
Examination of personality characteristics in a Turkish sample: development of Basic Personality Traits Inventory.

PubMed

Gençöz, Tülin; Öcül, Öznur

2012-01-01

The aim of the present study was to test the cross-cultural validity of the five-factor nature of personality. For this aim, an indigenous, psychometrically strong instrument measuring the basic personality dimensions within Turkish culture and language was developed through three consecutive studies. The first study aimed to reveal the adjectives that have been most frequently used to define people in the Turkish culture. In the second study, factor analysis of these personality characteristics revealed big five personality factors, along with the sixth factor, which had been called as the Negative Valence factor. The adjectives that most strongly represented and differentiated each factor constituted 45-item "Basic Personality Traits Inventory". Finally, in the third study, psychometric characteristics of the Basic Personality Traits Inventory were examined. Factor structure and psychometric properties of this instrument confirmed that five-factor nature of personality may not hold true in every culture.
The Multidimensional Loss Scale: validating a cross-cultural instrument for measuring loss.

PubMed

Vromans, Lyn; Schweitzer, Robert D; Brough, Mark

2012-04-01

The Multidimensional Loss Scale (MLS) represents the first instrument designed specifically to index Experience of Loss Events and Loss Distress across multiple domains (cultural, social, material, and intrapersonal) relevant to refugee settlement. Recently settled Burmese adult refugees (N = 70) completed a questionnaire battery, including MLS items. Analyses explored MLS internal consistency, convergent and divergent validity, and factor structure. Cronbach alphas indicated satisfactory internal consistency for Experience of Loss Events (0.85) and Loss Distress (0.92), reflecting a unitary construct of multidimensional loss. Loss Distress did not correlate with depression or anxiety symptoms and correlated moderately with interpersonal grief and trauma symptoms, supporting divergent and convergent validity. Factor analysis provided preliminary support for a five-factor model: Loss of Symbolic Self, Loss of Interdependence, Loss of Home, Interpersonal Loss, and Loss of Intrapersonal Integrity. Received well by participants, the new scale shows promise for application in future research and practice.
Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations.

PubMed

Moghaddar, N; van der Werf, J H J

2017-12-01

The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross-bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on "average information restricted maximum likelihood" using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10-fold cross-validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross-bred population. In the combined cross-bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross-bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross-bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross-bred population could be overestimated if heterosis is not fitted in the model. © 2017 Blackwell Verlag GmbH.
Three dimensional simulations of viscous folding in diverging microchannels

NASA Astrophysics Data System (ADS)

Xu, Bingrui; Chergui, Jalel; Shin, Seungwon; Juric, Damir

2016-11-01

Three dimensional simulations on the viscous folding in diverging microchannels reported by Cubaud and Mason are performed using the parallel code BLUE for multi-phase flows. The more viscous liquid L1 is injected into the channel from the center inlet, and the less viscous liquid L2 from two side inlets. Liquid L1 takes the form of a thin filament due to hydrodynamic focusing in the long channel that leads to the diverging region. The thread then becomes unstable to a folding instability, due to the longitudinal compressive stress applied to it by the diverging flow of liquid L2. We performed a parameter study in which the flow rate ratio, the viscosity ratio, the Reynolds number, and the shape of the channel were varied relative to a reference model. In our simulations, the cross section of the thread produced by focusing is elliptical rather than circular. The initial folding axis can be either parallel or perpendicular to the narrow dimension of the chamber. In the former case, the folding slowly transforms via twisting to perpendicular folding, or it may remain parallel. The direction of folding onset is determined by the velocity profile and the elliptical shape of the thread cross section in the channel that feeds the diverging part of the cell.
A Genome-Wide Knockout Screen to Identify Genes Involved in Acquired Carboplatin Resistance

DTIC Science & Technology

2016-07-01

library screen to identify genes that when knocked out render human ovarian cells > 2.5-fold resistant to CBDCA; 2) Validate the ability of...a GeCKOv2 library screen to identify genes that when knocked out render human ovarian cells > 2.5-fold resistant to CBDCA; 2) validate the ability of...resistance in either cell lines or clinical samples. The CRIPSR-cas9 technology now provides us with a major new tool to introduce knock out mutations
A new approach to geometrical measurements in an animal model of vocal fold scar.

PubMed

Jabbour, Noel; Krishna, Priya D; Osborne, James; Rosen, Clark A

2009-01-01

A standard method for quantifying the geometric properties of vocal folds has not been widely adopted. An ideal method of geometrical measurement should effectively quantify the dimensions of the medial vibratory portion of the vocal fold, should be easily performed, should yield consistent results, and should be readily available at little to no cost. We have developed a new approach for geometrical measurements to meet these goals. The objective of this study is to describe this new approach and to assess its effectiveness in a canine model of vocal fold scar. One hundred thirty-five mid-membranous coronal sections of vocal folds from 10 canines (five with unilateral surgical scarring) were examined by light microscopy; digital images were captured. ImageJ was used to measure a variety of described parameters. Comparison between scarred vocal folds and control vocal folds was made. At least 20% of the slides for each vocal fold were randomly selected (n=42) for repeat measurements of interrater and intrarater reliability. A statistically significant difference between scarred and control vocal folds was obtained for horizontal distance (P<0.001), vertical distance (P=0.005), area (P<0.001), mean optical density (OD) (P<0.001), and OD at defined points along the length of the vocal fold (P< or =0.009). Reliability calculations for intrarater and interrater measurements ranged from r=0.845 to r=0.994 and from r=0.734 to r=0.976, respectively. The proposed approach for geometrical measurements meets the intended objectives in a canine model of vocal fold scar. Future work is needed to apply this approach to other model systems.
Validation of the Behavioral Risk Factor Surveillance System Sleep Questions

PubMed Central

Jungquist, Carla R.; Mund, Jaime; Aquilina, Alan T.; Klingman, Karen; Pender, John; Ochs-Balcom, Heather; van Wijngaarden, Edwin; Dickerson, Suzanne S.

2016-01-01

Study Objective: Sleep problems may constitute a risk for health problems, including cardiovascular disease, depression, diabetes, poor work performance, and motor vehicle accidents. The primary purpose of this study was to assess the validity of the current Behavioral Risk Factor Surveillance System (BRFSS) sleep questions by establishing the sensitivity and specificity for detection of sleep/ wake disturbance. Methods: Repeated cross-sectional assessment of 300 community dwelling adults over the age of 18 who did not wear CPAP or oxygen during sleep. Reliability and validity testing of the BRFSS sleep questions was performed comparing to BFRSS responses to data from home sleep study, actigraphy for 14 days, Insomnia Severity Index, Epworth Sleepiness Scale, and PROMIS-57. Results: Only two of the five BRFSS sleep questions were found valid and reliable in determining total sleep time and excessive daytime sleepiness. Conclusions: Refinement of the BRFSS questions is recommended. Citation: Jungquist CR, Mund J, Aquilina AT, Klingman K, Pender J, Ochs-Balcom H, van Wijngaarden E, Dickerson SS. Validation of the behavioral risk factor surveillance system sleep questions. J Clin Sleep Med 2016;12(3):301–310. PMID:26446246

Enhancing the cross-cultural adaptation and validation process: linguistic and psychometric testing of the Brazilian-Portuguese version of a self-report measure for dry eye.

PubMed

Santo, Ruth Miyuki; Ribeiro-Ferreira, Felipe; Alves, Milton Ruiz; Epstein, Jonathan; Novaes, Priscila

2015-04-01

To provide a reliable, validated, and culturally adapted instrument that may be used in monitoring dry eye in Brazilian patients and to discuss the strategies for the enhancement of the cross-cultural adaptation and validation process of a self-report measure for dry eye. The cross-cultural adaptation process (CCAP) of the original Ocular Surface Disease Index (OSDI) into Brazilian-Portuguese was conducted using a 9-step guideline. The synthesis of translations was tested twice, for face and content validity, by different subjects (focus groups and cognitive interviews). The expert committee contributed on several steps, and back translations were based on the final rather than the prefinal version. For validation, the adapted version was applied in a prospective longitudinal study to 101 patients from the Dry Eye Clinic at the General Hospital of the University of São Paulo, Brazil. Simultaneously to the OSDI, patients answered the short form-36 health survey (SF-36) and the 25-item visual function questionnaire (VFQ-25) and underwent clinical evaluation. Internal consistency, test-retest reliability, and measure validity were assessed. Cronbach's alpha value of the cross-culturally adapted Brazilian-Portuguese version of the OSDI was 0.905, and the intraclass correlation coefficient was 0.801. There was a statistically significant difference between OSDI scores in patients with dry eye (41.15 ± 27.40) and without dry eye (17.88 ± 17.09). There was a negative association between OSDI and VFQ-25 total score (P < 0.01) and between the OSDI and five SF-36 domains. OSDI scores correlated positively with lissamine green and fluorescein staining scores (P < 0.001) and negatively with Schirmer test I and tear break-up time values (P < 0.001). Although most of the reviewed guidelines on CCAP involve well-defined steps (translation, synthesis/reconciliation, back translation, expert committee review, pretesting), the proposed methodological steps have not been applied in a uniform way. The translation and adaptation process requires skill, knowledge, experience, and a considerable investment of time to maximize the attainment of semantic, idiomatic, experiential, and conceptual equivalence between the source and target questionnaires. A well-established guideline resulted in a culturally adapted Brazilian-Portuguese version of the OSDI, tested and validated on a sample of Brazilian population, and proved to be a valid and reliable instrument for assessing patients with dry eye syndrome in Brazil. Copyright © 2015 Elsevier Inc. All rights reserved.
Ligand-promoted protein folding by biased kinetic partitioning.

PubMed

Hingorani, Karan S; Metcalf, Matthew C; Deming, Derrick T; Garman, Scott C; Powers, Evan T; Gierasch, Lila M

2017-04-01

Protein folding in cells occurs in the presence of high concentrations of endogenous binding partners, and exogenous binding partners have been exploited as pharmacological chaperones. A combined mathematical modeling and experimental approach shows that a ligand improves the folding of a destabilized protein by biasing the kinetic partitioning between folding and alternative fates (aggregation or degradation). Computationally predicted inhibition of test protein aggregation and degradation as a function of ligand concentration are validated by experiments in two disparate cellular systems.
Ligand-Promoted Protein Folding by Biased Kinetic Partitioning

PubMed Central

Hingorani, Karan S.; Metcalf, Matthew C.; Deming, Derrick T.; Garman, Scott C.; Powers, Evan T.; Gierasch, Lila M.

2017-01-01

Protein folding in cells occurs in the presence of high concentrations of endogenous binding partners, and exogenous binding partners have been exploited as pharmacological chaperones. A combined mathematical modeling and experimental approach shows that a ligand improves the folding of a destabilized protein by biasing the kinetic partitioning between folding and alternative fates (aggregation or degradation). Computationally predicted inhibition of test protein aggregation and degradation as a function of ligand concentration are validated by experiments in two disparate cellular systems. PMID:28218913
Determination of selected azaarenes in water by bonded-phase extraction and liquid chromatography

USGS Publications Warehouse

Steinheimer, T.R.; Ondrus, M.G.

1986-01-01

A method for the rapid and simple quantitative determination of quinoline, isoquinoline, and five selected three-ring azaarenes in water has been developed. The azaarene fraction is separated from its carbon analogues on n-octadecyl packing material by edition with acidified water/acetonitrile. Concentration as great as 1000-fold is achieved readily. Instrumental analysis involves high-speed liquid chromatography on flexible-walled, wide-bore columns with fluorescence and ultraviolet detection at several wavelengths employing filter photometers in series. Method-validation data is provided as azaarene recovery efficiency from fortified samples. Distilled water, river water, contaminated ground water, and secondary-treatment effluent have been tested. Recoveries at part-per-billion levels are nearly quantitative for the three-ring compounds, but they decrease for quinoline and isoquinoline. ?? 1986 American Chemical Society.
A preliminary examination of the validity and reliability of a new brief rating scale for symptom domains of psychosis: Brief Evaluation of Psychosis Symptom Domains (BE-PSD).

PubMed

Takeuchi, Hiroyoshi; Fervaha, Gagan; Lee, Jimmy; Agid, Ofer; Remington, Gary

2016-09-01

Brief assessments have the potential to be widely adopted as outcome measures in research but also routine clinical practice. Existing brief rating scales that assess symptoms of schizophrenia or psychosis have a number of limitations including inability to capture five symptom domains of psychosis and a lack of clearly defined operational anchor points for scoring. We developed a new brief rating scale for five symptom domains of psychosis with clearly defined operational anchor points - the Brief Evaluation of Psychosis Symptom Domains (BE-PSD). To examine the psychometric properties of the BE-PSD, fifty patients with schizophrenia or schizoaffective disorder were included in this preliminary cross-sectional study. To test the convergent and discriminant validity of the BE-PSD, correlational analyses were employed using the consensus Positive and Negative Syndrome Scale (PANSS) five-factor model. To examine the inter-rater reliability of the BE-PSD, single measures intraclass correlation coefficients (ICCs) were calculated for 11 patients. The BE-PSD domain scores demonstrated high convergent validity with the corresponding PANSS factor score (rs = 0.81-0.93) as well as good discriminant validity, as evidenced by lower correlations with the other PANSS factors (rs = 0.23-0.62). The BE-PSD also demonstrated excellent inter-rater reliability for each of the domain scores and the total scores (ICC(2,1) = 0.79-0.96). The present preliminary study found the BE-PSD measure to be valid and reliable; however, further studies are needed to establish the psychometric properties of the BE-PSD because of the limitations such as the small sample size and lacking data on test-retest reliability or sensitivity to change. Copyright © 2016 Elsevier Ltd. All rights reserved.
Psychological distress screening in cancer patients: psychometric properties of tools available in Italy.

PubMed

Muzzatti, Barbara; Annunziata, Maria Antonietta

2012-01-01

The main national and international organisms recommend continuous monitoring of psychological distress in cancer patients throughout the disease trajectory. The reasons for this concern are the high prevalence of psychological distress in cancer patients and its association with a worse quality of life, poor adherence to treatment, and stronger assistance needs. Most screening tools for psychological distress were developed in English-speaking countries. To be fit for use in different cultural contexts (like the Italian), they need to undergo accurate translation and specific validation. In the present work we summarized the validation studies for psychological distress screening tools available in Italian that are most widely employed internationally, with the aim of helping clinicians choose the adequate instrument. With knowledge of the properties of the corresponding Italian versions, researchers would be better able to identify the instruments that deserve further investigation. We carried out a systematic review of the literature. Results. Twenty-nine studies of eight different instruments (five relating to psychological distress, three to its depressive component) were identified. Ten of these studies involved cancer patients and 19 referred to the general population or to non-cancer, non-psychiatric subjects. For seven of the eight tools, data on concurrent and discriminant validity were available. For five instruments data on criterion validity were available, for four there were data on construct validity, and for one tool divergent and cross-cultural validity data were provided. For six of the eight tools the literature provided data on reliability (mostly about internal consistency). Since none of the eight instruments for which we found validation studies relative to the Italian context had undergone a complete and organic validation process, their use in the clinical context must be cautious. Italian researchers should be proactive and make a valid and reliable screening tool for Italian patients available.
A cross-national study on the multidimensional characteristics of the five-item psychological demands scale of the Job Content Questionnaire.

PubMed

Choi, BongKyoo; Kawakami, Norito; Chang, SeiJin; Koh, SangBaek; Bjorner, Jakob; Punnett, Laura; Karasek, Robert

2008-01-01

The five-item psychological demands scale of the Job Content Questionnaire (JCQ) has been assumed to be one-dimensional in practice. To examine whether the scale has sufficient internal consistency and external validity to be treated as a single scale, using the cross-national JCQ datasets from the United States, Korea, and Japan. Exploratory factor analyses with 22 JCQ items, confirmatory factor analyses with the five psychological demands items, and correlations analyses with mental health indexes. Generally, exploratory factor analyses displayed the predicted demand/control/support structure with three and four factors extracted. However, at more detailed levels of exploratory and confirmatory factor analyses, the demands scale showed clear evidence of multi-factor structure. The correlations of items and subscales of the demands scale with mental health indexes were similar to those of the full scale in the Korean and Japanese datasets, but not in the U.S. data. In 4 out of 16 sub-samples of the U.S. data, several significant correlations of the components of the demands scale with job dissatisfaction and life dissatisfaction were obscured by the full scale. The multidimensionality of the psychological demands scale should be considered in psychometric analysis and interpretation, occupational epidemiologic studies, and future scale extension.
The efficiency of health care production in OECD countries: A systematic review and meta-analysis of cross-country comparisons.

PubMed

Varabyova, Yauheniya; Müller, Julia-Maria

2016-03-01

There has been an ongoing interest in the analysis and comparison of the efficiency of health care systems using nonparametric and parametric applications. The objective of this study was to review the current state of the literature and to synthesize the findings on health system efficiency in OECD countries. We systematically searched five electronic databases through August 2014 and identified 22 studies that analyzed the efficiency of health care production at the country level. We summarized these studies with view on their sample, methods, and utilized variables. We developed and applied a checklist of 14 items to assess the quality of the reviewed studies along four dimensions: reporting, external validity, bias, and power. Moreover, to examine the internal validity of findings we meta-analyzed the efficiency estimates reported in 35 models from ten studies. The qualitative synthesis of the literature indicated large differences in study designs and methods. The meta-analysis revealed low correlations between country rankings suggesting a lack of internal validity of the efficiency estimates. In conclusion, methodological problems of existing cross-country comparisons of the efficiency of health care systems draw into question the ability of these comparisons to provide meaningful guidance to policy-makers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
[Resistance risk, cross-resistance and biochemical resistance mechanism of Laodelphax striatellus to buprofezin].

PubMed

Mao, Xu-lian; Liu, Jin; Li, Xu-ke; Chi, Jia-jia; Liu, Yong-jie

2016-01-01

In order to investigate the resistance development law and biochemical resistance mechanism of Laodelphax striatellus to buprofezin, spraying rice seedlings was used to continuously screen resistant strains of L. striatellus and dipping rice seedlings was applied to determine the toxicity and cross-resistance of L. striatellus to insecticides. After 32-generation screening with buprofezin, L. striatellus developed 168.49 folds resistance and its reality heritability (h2) was 0.11. If the killing rate was 80%-90%, L. striatellus was expected to develop 10-fold resistance to buprofezin only after 5 to 6 generations breeding. Because the actual reality heritability of field populations was usually lower than that of the resistant strains, the production of field populations increasing with 10-fold resistance would need much longer time. The results of cross-resistance showed that resistant strain had high level cross-resistance with thiamethoxam and imidacloprid, low level cross-resistance with acetamiprid, and no cross-resistance with pymetrozine and chlorpyrifos. The activity of detoxification enzymes of different strains and the syergism of synergist were measured. The results showed that cytochrome P450 monooxygenase played a major role in the resistance of L. striatellus to buprofezin, the esterase played a minor role and the GSH-S-transferase had no effect. Therefore, L. striatellus would have high risk to develop resistance to buprofezin when used in the field and might be delayed by using pymetrozine and chlorpyrifos.
Measurement of alienation among adolescents: construct validity of three scales on powerlessness, meaninglessness and social isolation.

PubMed

Rayce, Signe Boe; Kreiner, Svend; Damsgaard, Mogens Trab; Nielsen, Tine; Holstein, Bjørn Evald

2017-01-01

Psychological alienation is an important concept in the study of adolescents' health and behavior but no gold standard for measuring alienation among adolescents exists. There is a need for new scales with high validity for use in adolescent health and social research. The purpose of the present study was to develop and validate alienation scales in accordance with Seeman's conceptualization of alienation focusing on three independent variants specifically relevant in adolescent health research: powerlessness, meaninglessness and social isolation. Cross-sectional data from 3083 adolescents aged 13 to 15 years from the Danish contribution to the cross-national study Health Behaviour in School-aged Children (HBSC) were used. We identified and developed items, addressed content and face validity through interviews, and examined the criterion-related construct validity of the scales using graphical loglinear Rasch models (GLLRM). The three scales each comprised three to five face valid items. The powerlessness scale reflected the adolescent's expectancy as to whether his/her behavior can determine the outcome or reinforcement he/she seeks. The meaninglessness scale reflected the expectancy as to whether satisfactory predictions regarding the effects of one's behavior are possible. Finally, the social isolation scale reflected whether the adolescent had a low expectancy for inclusion and social acceptance. All scales contained some uniform local dependency and differential item functioning. However, only to a limited degree, which could be accounted for using GLLRM. Thus the scales fitted GLLRMs and can therefore be considered to be essentially construct valid and essentially objective. The three alienation scales appear to be content and face valid and fulfill the psychometric properties of a good construct valid reflective scale. This suggests that the scales may be appropriate in future large-scale surveys to examine the relation between alienation and a range of adolescent health outcomes such as health, behavior and wellbeing.
Correlation of VHI-10 to voice laboratory measurements across five common voice disorders.

PubMed

Gillespie, Amanda I; Gooding, William; Rosen, Clark; Gartner-Schmidt, Jackie

2014-07-01

To correlate change in Voice Handicap Index (VHI)-10 scores with corresponding voice laboratory measures across five voice disorders. Retrospective study. One hundred fifty patients aged >18 years with primary diagnosis of vocal fold lesions, primary muscle tension dysphonia-1, atrophy, unilateral vocal fold paralysis (UVFP), and scar. For each group, participants with the largest change in VHI-10 between two periods (TA and TB) were selected. The dates of the VHI-10 values were linked to corresponding acoustic/aerodynamic and audio-perceptual measures. Change in voice laboratory values were analyzed for correlation with each other and with VHI-10. VHI-10 scores were greater for patients with UVFP than other disorders. The only disorder-specific correlation between voice laboratory measure and VHI-10 was average phonatory airflow in speech for patients with UVFP. Average airflow in repeated phonemes was strongly correlated with average airflow in speech (r=0.75). Acoustic measures did not significantly change between time points. The lack of correlations between the VHI-10 change scores and voice laboratory measures may be due to differing constructs of each measure; namely, handicap versus physiological function. Presuming corroboration between these measures may be faulty. Average airflow in speech may be the most ecologically valid measure for patients with UVFP. Although aerodynamic measures changed between the time points, acoustic measures did not. Correlations to VHI-10 and change between time points may be found with other acoustic measures. Copyright © 2014 The Voice Foundation. Published by Mosby, Inc. All rights reserved.
Screening of lymph nodes metastasis associated lncRNAs in colorectal cancer patients

PubMed Central

Han, Jun; Rong, Long-Fei; Shi, Chuan-Bin; Dong, Xiao-Gang; Wang, Jie; Wang, Bao-Lin; Wen, Hao; He, Zhen-Yu

2014-01-01

AIM: To screen lymph nodes metastasis associated long noncoding RNAs (lncRNAs) in colorectal cancer through microarray analysis. METHODS: Metastatic lymph node (MLN), normal lymph node (NLN) and tumor tissues of 3 colorectal cancer (CRC) patients were collected during the operation and validated by pathological examinations. RNAs were extracted from MLN, NLN, and cancer tissues separately. RNA quantity and quality were measured with a NanoDrop ND-1000 spectrophotometer and RNA integrity was assessed by standard denaturing agarose electrophoresis. Agilent Feature Extraction Software (Version 11.0.1.1) was used to analyze acquired array images. Four differently expressed lncRNAs were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) in 26 subsets of MLN, NLN, and tumor tissues. RESULTS: Of 33045 lncRNAs, 1133 were differentially expressed in MLN compared with NLN, of which 260 were up-regulated and 873 down-regulated (≥ 2 fold-change). Five hundred and forty-five lncRNAs were differentially expressed in MLN compared with tumor tissues, of which 460 were up-regulated and 85 down-regulated (≥ 2 fold-change). Compared with NLN and cancer tissues, 14 lncRNAs were specifically up-regulated and 5 specifically down-regulated in MLN. AK307796, ENST00000425785, and AK021444 were confirmed to be specifically up-regulated in MLN and ENST00000465846 specifically down-regulated in MLN by qRT-PCR in 26 CRC patients. CONCLUSION: The specifically expressed lncRNAs in MLN may exert a partial or key role in the progress of lymph nodes metastasis of CRC. PMID:25009386
Importance of α–helix N–capping motif in stabilization of ββα fold

PubMed Central

Koscielska-Kasprzak, Katarzyna; Cierpicki, Tomasz; Otlewski, Jacek

2003-01-01

FSD-1 (full sequence design 1) is a protein folded in a ββα motif, designed on the basis of the second zinc finger domain of Zif268 by a substitution of its metal coordination site with a hydrophobic core. In this work, we analyzed the possibility of introducing the DNA recognition motif of the template zinc finger (S13RSDH17) into FSD-1 sequence in order to obtain a small DNA-binding module devoid of cross-link(s) or metal cofactors. The hybrid protein was unfolded, as judged by CD and NMR criteria. To reveal the role of each of the five amino acids, which form the N-capping motif of the α-helix, we analyzed conformational and stability properties of eight FSD-1 mutants. We used a shielded methyl group of Leu 18 and a CD signal at 215 nm as a convenient measure of the folded state. Glu 17→His substitution at the N3 in S13NEKE17 variant decreased the folded structure content from 90% to 25% (equivalent to 1.8 kcal • mole−1 destabilization) by disruption of N-capping interactions, and had the most significant effect among single mutants studied here. The Ncap Asn 14 substitution with Arg considerably decreased stability, reducing structure content from 90% to 40% (1.4 kcal • mole−1 destabilization) by disruption of a helix-capping hydrogen bond and destabilization of a helix macrodipole. The N1 Glu 15→Ser mutation also produced a considerable effect (1.0 kcal • mole−1 destabilization), again emphasizing the significance of electrostatic interactions in α-helix stabilization. PMID:12761399
Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

PubMed

Kalderstam, Jonas; Edén, Patrik; Ohlsson, Mattias

2015-01-01

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.
A Computationally Efficient Hypothesis Testing Method for Epistasis Analysis using Multifactor Dimensionality Reduction

PubMed Central

Pattin, Kristine A.; White, Bill C.; Barney, Nate; Gui, Jiang; Nelson, Heather H.; Kelsey, Karl R.; Andrew, Angeline S.; Karagas, Margaret R.; Moore, Jason H.

2008-01-01

Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free data mining method for detecting, characterizing, and interpreting epistasis in the absence of significant main effects in genetic and epidemiologic studies of complex traits such as disease susceptibility. The goal of MDR is to change the representation of the data using a constructive induction algorithm to make nonadditive interactions easier to detect using any classification method such as naïve Bayes or logistic regression. Traditionally, MDR constructed variables have been evaluated with a naïve Bayes classifier that is combined with 10-fold cross validation to obtain an estimate of predictive accuracy or generalizability of epistasis models. Traditionally, we have used permutation testing to statistically evaluate the significance of models obtained through MDR. The advantage of permutation testing is that it controls for false-positives due to multiple testing. The disadvantage is that permutation testing is computationally expensive. This is in an important issue that arises in the context of detecting epistasis on a genome-wide scale. The goal of the present study was to develop and evaluate several alternatives to large-scale permutation testing for assessing the statistical significance of MDR models. Using data simulated from 70 different epistasis models, we compared the power and type I error rate of MDR using a 1000-fold permutation test with hypothesis testing using an extreme value distribution (EVD). We find that this new hypothesis testing method provides a reasonable alternative to the computationally expensive 1000-fold permutation test and is 50 times faster. We then demonstrate this new method by applying it to a genetic epidemiology study of bladder cancer susceptibility that was previously analyzed using MDR and assessed using a 1000-fold permutation test. PMID:18671250
Predicting introductory programming performance: A multi-institutional multivariate study

NASA Astrophysics Data System (ADS)

Bergin, Susan; Reilly, Ronan

2006-12-01

A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.
RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.

PubMed

Ismail, Hamid D; Jones, Ahoi; Kim, Jung H; Newman, Robert H; Kc, Dukka B

2016-01-01

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.
Emotional Sentence Annotation Helps Predict Fiction Genre

PubMed Central

Samothrakis, Spyridon; Fasli, Maria

2015-01-01

Fiction, a prime form of entertainment, has evolved into multiple genres which one can broadly attribute to different forms of stories. In this paper, we examine the hypothesis that works of fiction can be characterised by the emotions they portray. To investigate this hypothesis, we use the work of fictions in the Project Gutenberg and we attribute basic emotional content to each individual sentence using Ekman’s model. A time-smoothed version of the emotional content for each basic emotion is used to train extremely randomized trees. We show through 10-fold Cross-Validation that the emotional content of each work of fiction can help identify each genre with significantly higher probability than random. We also show that the most important differentiator between genre novels is fear. PMID:26524352
Experimental study and neural network modeling of sugarcane bagasse pretreatment with H2SO4 and O3 for cellulosic material conversion to sugar.

PubMed

Gitifar, Vahid; Eslamloueyan, Reza; Sarshar, Mohammad

2013-11-01

In this study, pretreatment of sugarcane bagasse and subsequent enzymatic hydrolysis is investigated using two categories of pretreatment methods: dilute acid (DA) pretreatment and combined DA-ozonolysis (DAO) method. Both methods are accomplished at different solid ratios, sulfuric acid concentrations, autoclave residence times, bagasse moisture content, and ozonolysis time. The results show that the DAO pretreatment can significantly increase the production of glucose compared to DA method. Applying k-fold cross validation method, two optimal artificial neural networks (ANNs) are trained for estimations of glucose concentrations for DA and DAO pretreatment methods. Comparing the modeling results with experimental data indicates that the proposed ANNs have good estimation abilities. Copyright © 2013 Elsevier Ltd. All rights reserved.
A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease

NASA Astrophysics Data System (ADS)

Babaoğlu, Ismail; Baykan, Ömer Kaan; Aygül, Nazif; Özdemir, Kurtuluş; Bayrak, Mehmet

The aim of this study is to show a comparison of multi-layered perceptron neural network (MLPNN) and support vector machine (SVM) on determination of coronary artery disease existence upon exercise stress testing (EST) data. EST and coronary angiography were performed on 480 patients with acquiring 23 verifying features from each. The robustness of the proposed methods is examined using classification accuracy, k-fold cross-validation method and Cohen's kappa coefficient. The obtained classification accuracies are approximately 78% and 79% for MLPNN and SVM respectively. Both MLPNN and SVM methods are rather satisfactory than human-based method looking to Cohen's kappa coefficients. Besides, SVM is slightly better than MLPNN when looking to the diagnostic accuracy, average of sensitivity and specificity, and also Cohen's kappa coefficient.

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