Predictive factors of esophageal stenosis associated with tumor regression in radiation therapy for locally advanced esophageal cancer.

PubMed

Atsumi, Kazushige; Shioyama, Yoshiyuki; Nakamura, Katsumasa; Nomoto, Satoshi; Ohga, Saiji; Yoshitake, Tadamasa; Nonoshita, Takeshi; Ueda, Masanobu; Hirata, Hideki; Honda, Hiroshi

2010-01-01

The purpose of this retrospective study was to clarify the predictive factors correlated with esophageal stenosis within three months after radiation therapy for locally advanced esophageal cancer. We enrolled 47 patients with advanced esophageal cancer with T2-4 and stage II-III who were treated with definitive radiation therapy and achieving complete response of primary lesion at Kyushu University Hospital between January 1998 and December 2005. Esophagography was performed for all patients before treatment and within three months after completion of the radiation therapy, the esophageal stenotic ratio was evaluated. The stenotic ratio was used to define four levels of stenosis: stenosis level 1, stenotic ratio of 0-25%; 2, 25-50%; 3,50-75%; 4,75-100%. We then estimated the correlation between the esophageal stenosis level after radiation therapy and each of numerous factors. The numbers and total percentages of patients at each stenosis level were as follows: level 1: n = 14 (30%); level 2: 8 (17%); level 3: 14 (30%); and level 4: 11 (23%). Esophageal stenosis in the case of full circumference involvement tended to be more severe and more frequent. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. The extent of involved circumference and wall thickness of tumor region were significantly correlated with esophageal stenosis associated with tumor regression in radiation therapy (p = 0.0006, p = 0.005). For predicting the possibility of esophageal stenosis with tumor regression within three months in radiation therapy, the extent of involved circumference and esophageal wall thickness of the tumor region may be useful.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

PubMed

Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

2010-07-01

PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets

PubMed Central

Lee, KiYoung; Kim, Dae-Won; Na, DoKyun; Lee, Kwang H.; Lee, Doheon

2006-01-01

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003). PMID:16966337

Evaluation of non-negative matrix factorization of grey matter in age prediction.

PubMed

Varikuti, Deepthi P; Genon, Sarah; Sotiras, Aristeidis; Schwender, Holger; Hoffstaedter, Felix; Patil, Kaustubh R; Jockwitz, Christiane; Caspers, Svenja; Moebus, Susanne; Amunts, Katrin; Davatzikos, Christos; Eickhoff, Simon B

2018-06-01

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that

Predicting local field potentials with recurrent neural networks.

PubMed

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

Modeling the influence of local environmental factors on malaria transmission in Benin and its implications for cohort study.

PubMed

Cottrell, Gilles; Kouwaye, Bienvenue; Pierrat, Charlotte; le Port, Agnès; Bouraïma, Aziz; Fonton, Noël; Hounkonnou, Mahouton Norbert; Massougbodji, Achille; Corbel, Vincent; Garcia, André

2012-01-01

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission--even at a very local scale--is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors.As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages.This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics.

Local backbone structure prediction of proteins

PubMed Central

De Brevern, Alexandre G.; Benros, Cristina; Gautier, Romain; Valadié, Hélène; Hazout, Serge; Etchebest, Catherine

2004-01-01

Summary A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (φ, Ψ) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically. PMID:15724288

The development of local calibration factors - phase II : Maryland freeways and ramps : final report.

DOT National Transportation Integrated Search

2016-11-01

The goal of the study was to develop local calibration factors (LCFs) for Maryland freeways in order to apply the predictive methods of the Highway Safety Manual (HSM) to the state. LCFs were computed for freeway segments, speed-change lanes, and sig...

LocTree2 predicts localization for all domains of life

PubMed Central

Goldberg, Tatyana; Hamp, Tobias; Rost, Burkhard

2012-01-01

Motivation: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled. Results: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data. Availability: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2. Contact: localization@rostlab.org Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22962467

Inter-kingdom prediction certainty evaluation of protein subcellular localization tools: microbial pathogenesis approach for deciphering host microbe interaction.

PubMed

Khan, Abdul Arif; Khan, Zakir; Kalam, Mohd Abul; Khan, Azmat Ali

2018-01-01

Microbial pathogenesis involves several aspects of host-pathogen interactions, including microbial proteins targeting host subcellular compartments and subsequent effects on host physiology. Such studies are supported by experimental data, but recent detection of bacterial proteins localization through computational eukaryotic subcellular protein targeting prediction tools has also come into practice. We evaluated inter-kingdom prediction certainty of these tools. The bacterial proteins experimentally known to target host subcellular compartments were predicted with eukaryotic subcellular targeting prediction tools, and prediction certainty was assessed. The results indicate that these tools alone are not sufficient for inter-kingdom protein targeting prediction. The correct prediction of pathogen's protein subcellular targeting depends on several factors, including presence of localization signal, transmembrane domain and molecular weight, etc., in addition to approach for subcellular targeting prediction. The detection of protein targeting in endomembrane system is comparatively difficult, as the proteins in this location are channelized to different compartments. In addition, the high specificity of training data set also creates low inter-kingdom prediction accuracy. Current data can help to suggest strategy for correct prediction of bacterial protein's subcellular localization in host cell. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Modeling the Influence of Local Environmental Factors on Malaria Transmission in Benin and Its Implications for Cohort Study

PubMed Central

Pierrat, Charlotte; le Port, Agnès; Bouraïma, Aziz; Fonton, Noël; Hounkonnou, Mahouton Norbert; Massougbodji, Achille; Corbel, Vincent; Garcia, André

2012-01-01

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission—even at a very local scale—is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors. As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages. This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics. PMID:22238582

Minimalist ensemble algorithms for genome-wide protein localization prediction.

PubMed

Lin, Jhih-Rong; Mondal, Ananda Mohan; Liu, Rong; Hu, Jianjun

2012-07-03

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. We proposed a method for rational design

Minimalist ensemble algorithms for genome-wide protein localization prediction

PubMed Central

2012-01-01

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We

DeepLoc: prediction of protein subcellular localization using deep learning.

PubMed

Almagro Armenteros, José Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae; Nielsen, Henrik; Winther, Ole

2017-11-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information. At its core, the prediction model uses a recurrent neural network that processes the entire protein sequence and an attention mechanism identifying protein regions important for the subcellular localization. The model was trained and tested on a protein dataset extracted from one of the latest UniProt releases, in which experimentally annotated proteins follow more stringent criteria than previously. We demonstrate that our model achieves a good accuracy (78% for 10 categories; 92% for membrane-bound or soluble), outperforming current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc. Example code is available at https://github.com/JJAlmagro/subcellular_localization. The dataset is available at http://www.cbs.dtu.dk/services/DeepLoc/data.php. jjalma@dtu.dk. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Stereotactic body radiation therapy for liver oligometastases: predictive factors of local response by 18F-FDG-PET/CT.

PubMed

Mazzola, Rosario; Fersino, Sergio; Alongi, Pierpaolo; Di Paola, Gioacchino; Gregucci, Fabiana; Aiello, Dario; Tebano, Umberto; Pasetto, Stefano; Ruggieri, Ruggero; Salgarello, Matteo; Alongi, Filippo

2018-05-23

To investigate metabolic parameters as predictive of local response after stereotactic body radiation therapy (SBRT) for liver-oligometastases. Inclusion criteria of the present retrospective study were: (a) liver oligometastases with controlled primary tumor; (b) absence of progressive disease ≥6 months; (c) metastases ≤ 3; (d) evaluation of SBRT-response by means of 18-fludeoxyglucose-PET/CT for at least two subsequent evaluations; (e) Karnofsky performance status >80; (f) life-expectancy >6 months. The following metabolic parameters were defined semi-quantitatively for each metastases: (1) standardized uptake value (SUVmax; (2) SUV-mean; (3) metabolic tumor volume (MTV), tumor volume with a SUV ≥3, threshold 40%; (4) total lesion glycolysis (TLG), i.e. the product of SUV-mean and MTV. Local control was defined as absence of recurrence in the field of irradiation. 41 liver metastases were analyzed. Pre-SBRT, median SUV-max was 8.7 (range, 4.5-23.59), median SUV-mean was 4.6 (range, 3-7.5), median MTV was 5.7 cc (range, 0.9-80.6) and median total lesion glycolysis was 24.1 (range, 3.6-601.5). At statistical analysis, metastases with SUV-mean >5 (p 0.04; odds ratio 4.75, sensitivity = 50%, specificity = 82.6%, area under the curve 0.66) and SUV-max >12 (p 0.02; odds ratio 5.03, sensitivity = 69%, specificity = 70%, area under the curve = 0.69) showed higher rates of infield-failure compared to the remaining lesions. According to current findings, pre-SBRT SUV-max and SUV-mean could be predictable of local response in liver oligometastases. Advances in knowledge: Present findings could support the hypothesis that fludeoxyglucose-PET/CT may be a powerful tool to predict tumor control. Specifically, current results might be helpful for clinicians in the decision-making process regarding liver oligometastatic patient selection as well as the individual therapy stratification distinguishing between slowly local progressing patients and rapidly

Localized delivery of growth factors for periodontal tissue regeneration: role, strategies, and perspectives.

PubMed

Chen, Fa-Ming; Shelton, Richard M; Jin, Yan; Chapple, Iain L C

2009-05-01

Difficulties associated with achieving predictable periodontal regeneration, means that novel techniques need to be developed in order to regenerate the extensive soft and hard tissue destruction that results from periodontitis. Localized delivery of growth factors to the periodontium is an emerging and versatile therapeutic approach, with the potential to become a powerful tool in future regenerative periodontal therapy. Optimized delivery regimes and well-defined release kinetics appear to be logical prerequisites for safe and efficacious clinical application of growth factors and to avoid unwanted side effects and toxicity. While adequate concentrations of growth factor(s) need to be appropriately localized, delivery vehicles are also expected to possess properties such as protein protection, precision in controlled release, biocompatibility and biodegradability, self-regulated therapeutic activity, potential for multiple delivery, and good cell/tissue penetration. Here, current knowledge, recent advances, and future possibilities of growth factor delivery strategies are outlined for periodontal regeneration. First, the role of those growth factors that have been implicated in the periodontal healing/regeneration process, general requirements for their delivery, and the different material types available are described. A detailed discussion follows of current strategies for the selection of devices for localized growth factor delivery, with particular emphasis placed upon their advantages and disadvantages and future prospects for ongoing studies in reconstructing the tooth supporting apparatus.

Machine learning prediction for classification of outcomes in local minimisation

NASA Astrophysics Data System (ADS)

Das, Ritankar; Wales, David J.

2017-01-01

Machine learning schemes are employed to predict which local minimum will result from local energy minimisation of random starting configurations for a triatomic cluster. The input data consists of structural information at one or more of the configurations in optimisation sequences that converge to one of four distinct local minima. The ability to make reliable predictions, in terms of the energy or other properties of interest, could save significant computational resources in sampling procedures that involve systematic geometry optimisation. Results are compared for two energy minimisation schemes, and for neural network and quadratic functions of the inputs.

A range-based predictive localization algorithm for WSID networks

NASA Astrophysics Data System (ADS)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Pretreatment tables predicting pathologic stage of locally advanced prostate cancer.

PubMed

Joniau, Steven; Spahn, Martin; Briganti, Alberto; Gandaglia, Giorgio; Tombal, Bertrand; Tosco, Lorenzo; Marchioro, Giansilvio; Hsu, Chao-Yu; Walz, Jochen; Kneitz, Burkhard; Bader, Pia; Frohneberg, Detlef; Tizzani, Alessandro; Graefen, Markus; van Cangh, Paul; Karnes, R Jeffrey; Montorsi, Francesco; van Poppel, Hein; Gontero, Paolo

2015-02-01

Pretreatment tables for the prediction of pathologic stage have been published and validated for localized prostate cancer (PCa). No such tables are available for locally advanced (cT3a) PCa. To construct tables predicting pathologic outcome after radical prostatectomy (RP) for patients with cT3a PCa with the aim to help guide treatment decisions in clinical practice. This was a multicenter retrospective cohort study including 759 consecutive patients with cT3a PCa treated with RP between 1987 and 2010. Retropubic RP and pelvic lymphadenectomy. Patients were divided into pretreatment prostate-specific antigen (PSA) and biopsy Gleason score (GS) subgroups. These parameters were used to construct tables predicting pathologic outcome and the presence of positive lymph nodes (LNs) after RP for cT3a PCa using ordinal logistic regression. In the model predicting pathologic outcome, the main effects of biopsy GS and pretreatment PSA were significant. A higher GS and/or higher PSA level was associated with a more unfavorable pathologic outcome. The validation procedure, using a repeated split-sample method, showed good predictive ability. Regression analysis also showed an increasing probability of positive LNs with increasing PSA levels and/or higher GS. Limitations of the study are the retrospective design and the long study period. These novel tables predict pathologic stage after RP for patients with cT3a PCa based on pretreatment PSA level and biopsy GS. They can be used to guide decision making in men with locally advanced PCa. Our study might provide physicians with a useful tool to predict pathologic stage in locally advanced prostate cancer that might help select patients who may need multimodal treatment. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Protein subcellular localization prediction using multiple kernel learning based support vector machine.

PubMed

Hasan, Md Al Mehedi; Ahmad, Shamim; Molla, Md Khademul Islam

2017-03-28

Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in turns, makes the subcellular localization prediction by purely laboratory tests prohibitively laborious and expensive. In this context, to tackle the challenges, computational methods are being developed as an alternative choice to aid biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging issue, particularly, when query proteins have multi-label characteristics, i.e., if they exist simultaneously in more than one subcellular location or if they move between two or more different subcellular locations. To date, to address this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions to the protein subcellular localization prediction problem. However, the practicability of an SVM is affected by the challenges of selecting an appropriate kernel and selecting the parameters of the selected kernel. To address this difficulty, in this study, we aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM. We evaluated MKLoc using a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). Note that this dataset was used by Briesemeister et al. in their extensive comparison of multi-localization prediction systems. Finally, our experimental results indicate that

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

SubCellProt: predicting protein subcellular localization using machine learning approaches.

PubMed

Garg, Prabha; Sharma, Virag; Chaudhari, Pradeep; Roy, Nilanjan

2009-01-01

High-throughput genome sequencing projects continue to churn out enormous amounts of raw sequence data. However, most of this raw sequence data is unannotated and, hence, not very useful. Among the various approaches to decipher the function of a protein, one is to determine its localization. Experimental approaches for proteome annotation including determination of a protein's subcellular localizations are very costly and labor intensive. Besides the available experimental methods, in silico methods present alternative approaches to accomplish this task. Here, we present two machine learning approaches for prediction of the subcellular localization of a protein from the primary sequence information. Two machine learning algorithms, k Nearest Neighbor (k-NN) and Probabilistic Neural Network (PNN) were used to classify an unknown protein into one of the 11 subcellular localizations. The final prediction is made on the basis of a consensus of the predictions made by two algorithms and a probability is assigned to it. The results indicate that the primary sequence derived features like amino acid composition, sequence order and physicochemical properties can be used to assign subcellular localization with a fair degree of accuracy. Moreover, with the enhanced accuracy of our approach and the definition of a prediction domain, this method can be used for proteome annotation in a high throughput manner. SubCellProt is available at www.databases.niper.ac.in/SubCellProt.

Parafoveal Target Detectability Reversal Predicted by Local Luminance and Contrast Gain Control

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Beard, Bettina L.; Null, Cynthia H. (Technical Monitor)

1996-01-01

This project is part of a program to develop image discrimination models for the prediction of the detectability of objects in a range of backgrounds. We wanted to see if the models could predict parafoveal object detection as well as they predict detection in foveal vision. We also wanted to make our simplified models more general by local computation of luminance and contrast gain control. A signal image (0.78 x 0.17 deg) was made by subtracting a simulated airport runway scene background image (2.7 deg square) from the same scene containing an obstructing aircraft. Signal visibility contrast thresholds were measured in a fully crossed factorial design with three factors: eccentricity (0 deg or 4 deg), background (uniform or runway scene background), and fixed-pattern white noise contrast (0%, 5%, or 10%). Three experienced observers responded to three repetitions of 60 2IFC trials in each condition and thresholds were estimated by maximum likelihood probit analysis. In the fovea the average detection contrast threshold was 4 dB lower for the runway background than for the uniform background, but in the parafovea, the average threshold was 6 dB higher for the runway background than for the uniform background. This interaction was similar across the different noise levels and for all three observers. A likely reason for the runway background giving a lower threshold in the fovea is the low luminance near the signal in that scene. In our model, the local luminance computation is controlled by a spatial spread parameter. When this parameter and a corresponding parameter for the spatial spread of contrast gain were increased for the parafoveal predictions, the model predicts the interaction of background with eccentricity.

Prediction of protein subcellular localization by weighted gene ontology terms.

PubMed

Chi, Sang-Mun

2010-08-27

We develop a new weighting approach of gene ontology (GO) terms for predicting protein subcellular localization. The weights of individual GO terms, corresponding to their contribution to the prediction algorithm, are determined by the term-weighting methods used in text categorization. We evaluate several term-weighting methods, which are based on inverse document frequency, information gain, gain ratio, odds ratio, and chi-square and its variants. Additionally, we propose a new term-weighting method based on the logarithmic transformation of chi-square. The proposed term-weighting method performs better than other term-weighting methods, and also outperforms state-of-the-art subcellular prediction methods. Our proposed method achieves 98.1%, 99.3%, 98.1%, 98.1%, and 95.9% overall accuracies for the animal BaCelLo independent dataset (IDS), fungal BaCelLo IDS, animal Höglund IDS, fungal Höglund IDS, and PLOC dataset, respectively. Furthermore, the close correlation between high-weighted GO terms and subcellular localizations suggests that our proposed method appropriately weights GO terms according to their relevance to the localizations. Copyright 2010 Elsevier Inc. All rights reserved.

The unrestricted local properties: application in nanoelectronics and for predicting radicals reactivity.

PubMed

Dral, Pavlo O

2014-03-01

The local electron affinity (EA(L)) and the local ionization energy (IE(L)) are successfully used for predicting properties of closed-shell species for drug design and for nanoelectronics. Here the respective unrestricted Hartree-Fock variants of EA(L) and IE(L), i.e., the unrestricted local electron affinity (UHF-EA(L)) and ionization energy (UHF-IE(L)), have been shown to be useful for predicting properties of open-shell species. UHF-EA(L) and UHF-IE(L) have been applied for explaining unique electronic properties of an exemplary nanomaterial carbon peapod. It is also demonstrated that UHF-EA(L) is useful for predicting and better understanding reactivity of radicals related to alkanes activation.

[The importance of local and general factors in development of inflammatory periodontal diseases in children and adolescents].

PubMed

Shishinashvili, T E; Tsagareli, Z G; Khimshiashvili, N B

2012-10-01

The aim of the study was to investigate the influence of local and general adverse risk factors and their role in the development of inflammatory periodontal diseases in children and adolescents. The study of the dental status among 618 school children, 9 to 15 years of age has been performed. The obtained results revealed an ambiguous influence of general and local risk factors on the development of inflammatory periodontal diseases. Namely, among the general risk factors the main role is given to hormonal functioning state of juvenile age (26.5%) - (arhythmia formation of hormonal activity). Among the local risk factors inducing inflammatory periodontal diseases at young age, the most significant are tooth-jaw anomalies (32.2%), especially - dental occlusion pathology, lips' bridle attachment anomalies, absence of interdental contacts, small vestibule of the mouth and so on. Poor oral hygiene, however, is also a significant factor in all age groups. Definition of the role and importance of general and local risk factors, taking into consideration patient's age, is of great importance in organization of early prevention, giving the possibility to predict disease possible development, choose most appropriate way to treat the specific situation, reduce the need of complex treatment and improve treatment outcomes.

Oncologic outcome after local recurrence of chondrosarcoma: Analysis of prognostic factors.

PubMed

Kim, Han-Soo; Bindiganavile, Srimanth S; Han, Ilkyu

2015-06-01

Literature on outcome after local recurrence (LR) in chondrosarcoma is scarce and better appreciation of prognostic factors is needed. (1) To evaluate post-LR oncologic outcomes of disease-specific survival and subsequent LR and (2) to identify prognostic factors for post-LR oncologic outcomes. Review of 28 patients with locally recurrent chondrosarcoma from the original cohort of 150 patients, who were treated surgically with or without adjuvants between 1982 and 2011, was performed. Mean age was 46 years (range, 21-73) which included 20 males and 8 females with mean follow up of 8.4 ± 7.5 years (range, 1.2-31.0). Post-LR survival at 5 years was 58.6 ± 10.3%. Age greater than 50 years (P = 0.011) and LR occurring within 1 year of primary surgery (P = 0.011) independently predicted poor survival. Seven patients suffered subsequent LR, which was significantly affected by surgical margin for LR (P = 0.038). Long-term survival of locally recurrent chondrosarcoma is achievable in a substantial number of patients. Older age at onset of LR and shorter interval from primary surgery to LR identifies high risk patients for poor post-LR survival while, wide surgical margins at LR surgery reduces the risk of subsequent LR. © 2015 Wiley Periodicals, Inc.

Protein Sub-Nuclear Localization Prediction Using SVM and Pfam Domain Information

PubMed Central

Kumar, Ravindra; Jain, Sohni; Kumari, Bandana; Kumar, Manish

2014-01-01

The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139.227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server. PMID:24897370

Consistent prediction of GO protein localization.

PubMed

Spetale, Flavio E; Arce, Debora; Krsticevic, Flavia; Bulacio, Pilar; Tapia, Elizabeth

2018-05-17

The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automated GO-CC annotation of proteins suffer from the inconsistency of individual GO-CC term predictions. Here, we present FGGA-CC + , a class of hierarchical graph-based classifiers for the consistent GO-CC annotation of protein coding genes at the subcellular compartment or macromolecular complex levels. Aiming to boost the accuracy of GO-CC predictions, we make use of the protein localization knowledge in the GO-Biological Process (GO-BP) annotations to boost the accuracy of GO-CC prediction. As a result, FGGA-CC + classifiers are built from annotation data in both the GO-CC and GO-BP ontologies. Due to their graph-based design, FGGA-CC + classifiers are fully interpretable and their predictions amenable to expert analysis. Promising results on protein annotation data from five model organisms were obtained. Additionally, successful validation results in the annotation of a challenging subset of tandem duplicated genes in the tomato non-model organism were accomplished. Overall, these results suggest that FGGA-CC + classifiers can indeed be useful for satisfying the huge demand of GO-CC annotation arising from ubiquitous high throughout sequencing and proteomic projects.

Factors which predict violence victimization in Nigeria

PubMed Central

Fry, Lincoln J.

2014-01-01

Background: Violence is a major public health issue, globally as well as in the African continent. This paper looks at Nigeria and begins the process of identifying the factors that predict interpersonal violence in that country. The purpose is to interpret the implications of the results presented here for violence prevention programmes in Nigeria. Materials and Methods: The study is based on the responses of 2324 Nigerians included in Round Four of the Afrobarometer surveys. The study concentrates on 579 respondents who reported either they or someone else in their family had been the victim of violence, defined as being physically attacked, in the past year. Results: A logistical regression analysis revealed five significant factors that predicted interpersonal violence: being the victim of a property crime, the fear of crime, the respondents faith, whethera police station was in the local area and poverty. The findings revealed that 43.7% of the sample had been victimised within the past year and 18.8% had been the victim of both violent and property crimes. One surprising findingwas the number of respondents who were re-victimised; 75% of violence victims also had been property crime victims. Conclusions: These findings suggest that target hardening should be the basis to plan, implement and evaluate violence prevention programmes in Nigeria. Prevention personnel and/or law enforcement need to respond to reported incidents of property and/or violence victimisation and attempt to prepare victims to protect both their premises and their persons in the future. PMID:24970968

ACUTE PANCREATITIS GRAVITY PREDICTIVE FACTORS: WHICH AND WHEN TO USE THEM?

PubMed Central

FERREIRA, Alexandre de Figueiredo; BARTELEGA, Janaina Alves; URBANO, Hugo Corrêa de Andrade; de SOUZA, Iure Kalinine Ferraz

2015-01-01

Introduction: Acute pancreatitis has as its main causes lithiasic biliary disease and alcohol abuse. Most of the time, the disease shows a self-limiting course, with a rapid recovery, only with supportive treatment. However, in a significant percentage of cases, it runs with important local and systemic complications associated with high mortality rates. Aim: To present the current state of the use of these prognostic factors (predictive scores) of gravity, as the time of application, complexity and specificity. Method: A non-systematic literature review through 28 papers, with emphasis on 13 articles published in indexed journals between 2008 and 2013 using Lilacs, Medline, Pubmed. Results: Several clinical, laboratory analysis, molecular and image variables can predict the development of severe acute pancreatitis. Some of them by themselves can be determinant to the progression of the disease to a more severe form, such as obesity, hematocrit, age and smoking. Hematocrit with a value lower than 44% and serum urea lower than 20 mg/dl, both at admission, appear as risk factors for pancreatic necrosis. But the PCR differentiates mild cases of serious ones in the first 24 h. Multifactorial scores measured on admission and during the first 48 h of hospitalization have been used in intensive care units, being the most ones used: Ranson, Apache II, Glasgow, Iget and Saps II. Conclusion: Acute pancreatitis is a disease in which several prognostic factors are employed being useful in predicting mortality and on the development of the severe form. It is suggested that the association of a multifactorial score, especially the Saps II associated with Iget, may increase the prognosis accuracy. However, the professional's preferences, the experience on the service as well as the available tools, are factors that have determined the choice of the most suitable predictive score. PMID:26537149

Analytical model for local scour prediction around hydrokinetic turbine foundations

NASA Astrophysics Data System (ADS)

Musa, M.; Heisel, M.; Hill, C.; Guala, M.

2017-12-01

Marine and Hydrokinetic renewable energy is an emerging sustainable and secure technology which produces clean energy harnessing water currents from mostly tidal and fluvial waterways. Hydrokinetic turbines are typically anchored at the bottom of the channel, which can be erodible or non-erodible. Recent experiments demonstrated the interactions between operating turbines and an erodible surface with sediment transport, resulting in a remarkable localized erosion-deposition pattern significantly larger than those observed by static in-river construction such as bridge piers, etc. Predicting local scour geometry at the base of hydrokinetic devices is extremely important during foundation design, installation, operation, and maintenance (IO&M), and long-term structural integrity. An analytical modeling framework is proposed applying the phenomenological theory of turbulence to the flow structures that promote the scouring process at the base of a turbine. The evolution of scour is directly linked to device operating conditions through the turbine drag force, which is inferred to locally dictate the energy dissipation rate in the scour region. The predictive model is validated using experimental data obtained at the University of Minnesota's St. Anthony Falls Laboratory (SAFL), covering two sediment mobility regimes (clear water and live bed), different turbine designs, hydraulic parameters, grain size distribution and bedform types. The model is applied to a potential prototype scale deployment in the lower Mississippi River, demonstrating its practical relevance and endorsing the feasibility of hydrokinetic energy power plants in large sandy rivers. Multi-turbine deployments are further studied experimentally by monitoring both local and non-local geomorphic effects introduced by a twelve turbine staggered array model installed in a wide channel at SAFL. Local scour behind each turbine is well captured by the theoretical predictive model. However, multi

Binding ligand prediction for proteins using partial matching of local surface patches.

PubMed

Sael, Lee; Kihara, Daisuke

2010-01-01

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

PubMed Central

Sael, Lee; Kihara, Daisuke

2010-01-01

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group. PMID:21614188

Family history does not predict angiographic localization or severity of coronary artery disease.

PubMed

Banerjee, Amitava; Lim, Chris C S; Silver, Louise E; Heneghan, Carl; Welch, Sarah J V; Mehta, Ziyah; Banning, Adrian P; Rothwell, Peter M

2012-04-01

Family history of MI is an established risk factor for coronary artery disease and subclinical atherosclerosis. Maternal MI and maternal stroke are more common in females than males presenting with acute coronary syndromes (ACS), suggesting sex-specific heritability, but the effects of family history on location and extent of coronary artery disease are unknown. In a prospective, population-based study (Oxford Vascular Study) of all patients with ACS, family history data for stroke and MI were analysed by sex of proband and affected first degree relatives (FDRs), and coronary angiograms were reviewed, where available. Of 835 probands with one or more ACS, 623 (420 males) had incident events and complete family history data. 351 patients with incident events (56.3%; 266 males) underwent coronary angiography. Neither angiographic disease localization nor severity were associated with sex-of-parent/sex-of-offspring in men or women. Sex-specific family history data do not predict angiographic localization of coronary disease in patients presenting with ACS. Maternal stroke and maternal MI probably affect ACS in females by a mechanism unrelated to atherosclerosis or coronary anatomy. However, family history data may still be useful in risk prediction and prognosis of ACS. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

BUSCA: an integrative web server to predict subcellular localization of proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Profiti, Giuseppe; Casadio, Rita

2018-04-30

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.

Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers

PubMed Central

Jiang, Yong; Schmidt, Renate H.; Reif, Jochen C.

2018-01-01

Genome-wide prediction approaches represent versatile tools for the analysis and prediction of complex traits. Mostly they rely on marker-based information, but scenarios have been reported in which models capitalizing on closely-linked markers that were combined into haplotypes outperformed marker-based models. Detailed comparisons were undertaken to reveal under which circumstances haplotype-based genome-wide prediction models are superior to marker-based models. Specifically, it was of interest to analyze whether and how haplotype-based models may take local epistatic effects between markers into account. Assuming that populations consisted of fully homozygous individuals, a marker-based model in which local epistatic effects inside haplotype blocks were exploited (LEGBLUP) was linearly transformable into a haplotype-based model (HGBLUP). This theoretical derivation formally revealed that haplotype-based genome-wide prediction models capitalize on local epistatic effects among markers. Simulation studies corroborated this finding. Due to its computational efficiency the HGBLUP model promises to be an interesting tool for studies in which ultra-high-density SNP data sets are studied. Applying the HGBLUP model to empirical data sets revealed higher prediction accuracies than for marker-based models for both traits studied using a mouse panel. In contrast, only a small subset of the traits analyzed in crop populations showed such a benefit. Cases in which higher prediction accuracies are observed for HGBLUP than for marker-based models are expected to be of immediate relevance for breeders, due to the tight linkage a beneficial haplotype will be preserved for many generations. In this respect the inheritance of local epistatic effects very much resembles the one of additive effects. PMID:29549092

Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers.

PubMed

Jiang, Yong; Schmidt, Renate H; Reif, Jochen C

2018-05-04

Genome-wide prediction approaches represent versatile tools for the analysis and prediction of complex traits. Mostly they rely on marker-based information, but scenarios have been reported in which models capitalizing on closely-linked markers that were combined into haplotypes outperformed marker-based models. Detailed comparisons were undertaken to reveal under which circumstances haplotype-based genome-wide prediction models are superior to marker-based models. Specifically, it was of interest to analyze whether and how haplotype-based models may take local epistatic effects between markers into account. Assuming that populations consisted of fully homozygous individuals, a marker-based model in which local epistatic effects inside haplotype blocks were exploited (LEGBLUP) was linearly transformable into a haplotype-based model (HGBLUP). This theoretical derivation formally revealed that haplotype-based genome-wide prediction models capitalize on local epistatic effects among markers. Simulation studies corroborated this finding. Due to its computational efficiency the HGBLUP model promises to be an interesting tool for studies in which ultra-high-density SNP data sets are studied. Applying the HGBLUP model to empirical data sets revealed higher prediction accuracies than for marker-based models for both traits studied using a mouse panel. In contrast, only a small subset of the traits analyzed in crop populations showed such a benefit. Cases in which higher prediction accuracies are observed for HGBLUP than for marker-based models are expected to be of immediate relevance for breeders, due to the tight linkage a beneficial haplotype will be preserved for many generations. In this respect the inheritance of local epistatic effects very much resembles the one of additive effects. Copyright © 2018 Jiang et al.

Prediction of resource volumes at untested locations using simple local prediction models

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

Do traditional risk factors predict whether men who have sex with men engage in unprotected anal intercourse? The need for locally based research to guide interventions.

PubMed

Berg, Rigmor C; Grimes, Richard

2011-09-01

A great deal of research effort has been expended in an effort to identify the variables which most influence men who have sex with men's (MSM) unsafe sexual behaviors.While a set of predictor variables has emerged, these predict the unsafe behaviors of MSM in some locations but not in others, suggesting the need to investigate the predictive ability of these variables among MSM in previously understudied populations. Therefore, this study examined the ability of previously identified factors to predict unsafe sexual behaviors among MSM in Houston, Texas. Data were collected through a short self-report survey completed by MSM attending the Houston pride festival. The multiethnic participants (N = 109) represented a range of age, educational, and income backgrounds. Fifty-seven percent of the survey respondents had been drunk and/or high in sexual contexts, 19 percent evidenced alcohol dependency, 26 percent reported finding sex partners online and sex with serodiscordant or unknown serostatus partners was common. Compared to men who did not report unprotected anal intercourse (UAI) in the preceding two months, MSM who engaged in UAI were younger and more likely to use alcohol in sexual contexts, meet men online for offline sex, and perceive lower safer sex norms in their community. Although these results were statistically significant, the strength of the relationships was too small to have any practical value. The lack of useful explanatory power underscores the importance of accelerated HIV research that identifies the unique, local factors associated with unsafe sex in other previously understudied populations.

Adaptation of clinical prediction models for application in local settings.

PubMed

Kappen, Teus H; Vergouwe, Yvonne; van Klei, Wilton A; van Wolfswinkel, Leo; Kalkman, Cor J; Moons, Karel G M

2012-01-01

When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

The status of perineural invasion predicts the outcomes of postoperative radiotherapy in locally advanced esophageal squamous cell carcinoma.

PubMed

Ning, Zhong-Hua; Zhao, Wei; Li, Xiao-Dong; Chen, Lu-Jun; Xu, Bin; Gu, Wen-Dong; Shao, Ying-Jie; Xu, Yun; Huang, Jin; Pei, Hong-Lei; Jiang, Jing-Ting

2015-01-01

Prognosis of locally advanced esophageal squamous cell carcinoma (ESCC) remains dismal even after curative resection and adjuvant radiotherapy. New biomarkers for predicting prognosis and treatment outcomes are needed for improved treatment stratification of patients with locally advanced ESCC. The prognostic and treatment predictive significance of perineural invasion (PNI) in the locally advanced ESCC remains unclear. This study aimed to examine the effect of PNI on the outcomes of locally advanced ESCC patients after curative resection with or without postoperative radiotherapy (PORT). We retrospectively reviewed 262 consecutive locally advanced ESCC patients who underwent curative resection. Tumors sections were re-evaluated for PNI by an independent pathologist blinded to the patients' outcomes. Overall survival (OS) and disease-free survival (DFS) were determined using the Kaplan-Meier method; univariate log-rank test and multivariate Cox proportional hazard model were used to evaluate the prognostic value of PNI. Finally, 243 patients were analyzed and enrolled into this study, of which 132 received PORT. PNI was identified in 22.2% (54/243) of the pathologic sections. The 5-year DFS was favorable for PNI-negative patients versus PNI-positive patients (21.3% vs. 36.7%, respectively; P = 0.005). The 5-year OS was 40.3% for PNI-negative patients versus 21.7% for PNI-positive patients (P < 0.001). On multivariate analysis, PNI was an independent prognostic factor. In a subset analysis for patients received PORT, PNI was evaluated as a prognostic predictor as well (P < 0.05). In contrast to patients without PORT, PORT couldn't improve the disease recurrence and survival in locally advanced ESCC patients with PNI-positive (P > 0.05). PNI could serve as an independent prognostic factor and prognosticate treatment outcomes in locally advanced ESCC patients. The PNI status should be considered when stratifying high-risk locally advanced ESCC patients for adjuvant

The predictive power of local properties of financial networks

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2017-01-01

The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

Morbidity predicting factors of penetrating colon injuries.

PubMed

Mickevicius, A; Valeikaite, G; Tamelis, A; Saladzinskas, Z; Svagzdys, S; Pavalkis, D

2010-01-01

To analyze patients suffering from penetrating colon injuries management, clinical outcomes and factors, which predict higher morbidity and complications rate. this was a retrospective analysis of prospectively collected data from patients with injured colon from 1995 to 2008. Age, time till operation, systolic blood pressure, part of injured colon, fecal contamination, PATI were registered. Monovariate and multivariate logistic regression was performed to determine higher morbidity predictive factors. 61 patients had penetrating colon injuries. Major fecal contamination of the peritoneal cavity and systolic blood pressure lower than 90 mmHg are independent factors determining the fecal diversion operation. Primary repair group analysis establish that major fecal contamination and systolic blood pressure lower than 90 mmHg OR = 4.2 and 0.96 were significant risk factors, which have contributed to the development of postoperative complications. And systolic blood pressure lower than 90 mmHg and PATI 20 predict OR = 0.05 and 2.61 higher morbidity. Fecal contamination of the peritoneal cavity and hypotension were determined to be crucial in choice of performing fecal diversion or primary repair. But the same criteria and PATI predict higher rate of postoperative complications and higher morbidity.

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-02-24

Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved. This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed. Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers' convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

Predicting the next local supernova

NASA Astrophysics Data System (ADS)

Middleditch, John

2018-06-01

It has been over 31 years since Supernova 1987A, and we have learned many things from the neutrinos, light curve, evolving spectrum including the “Bochum Event” at day 19.2, the associated “Bright Spot” or “Mystery Spot,” and its motion away from the position of the progenitor, Sk -69o202, the mixing, rings, X- and gamma-rays, polarization, and the 467.5 Hz pulsation and its associated ~1,000 s precession (or the lack of any strongly magnetized remnant). Finally, our understanding of this event has progressed to the point where we have a time interval of a few months during which we can predict which supergiant star in our local neighborhood out to 5 Megaparsecs will be the next to die in a supernova explosion.

Micro Finite Element models of the vertebral body: Validation of local displacement predictions.

PubMed

Costa, Maria Cristiana; Tozzi, Gianluca; Cristofolini, Luca; Danesi, Valentina; Viceconti, Marco; Dall'Ara, Enrico

2017-01-01

The estimation of local and structural mechanical properties of bones with micro Finite Element (microFE) models based on Micro Computed Tomography images depends on the quality bone geometry is captured, reconstructed and modelled. The aim of this study was to validate microFE models predictions of local displacements for vertebral bodies and to evaluate the effect of the elastic tissue modulus on model's predictions of axial forces. Four porcine thoracic vertebrae were axially compressed in situ, in a step-wise fashion and scanned at approximately 39μm resolution in preloaded and loaded conditions. A global digital volume correlation (DVC) approach was used to compute the full-field displacements. Homogeneous, isotropic and linear elastic microFE models were generated with boundary conditions assigned from the interpolated displacement field measured from the DVC. Measured and predicted local displacements were compared for the cortical and trabecular compartments in the middle of the specimens. Models were run with two different tissue moduli defined from microindentation data (12.0GPa) and a back-calculation procedure (4.6GPa). The predicted sum of axial reaction forces was compared to the experimental values for each specimen. MicroFE models predicted more than 87% of the variation in the displacement measurements (R2 = 0.87-0.99). However, model predictions of axial forces were largely overestimated (80-369%) for a tissue modulus of 12.0GPa, whereas differences in the range 10-80% were found for a back-calculated tissue modulus. The specimen with the lowest density showed a large number of elements strained beyond yield and the highest predictive errors. This study shows that the simplest microFE models can accurately predict quantitatively the local displacements and qualitatively the strain distribution within the vertebral body, independently from the considered bone types.

Micro Finite Element models of the vertebral body: Validation of local displacement predictions

PubMed Central

Costa, Maria Cristiana; Tozzi, Gianluca; Cristofolini, Luca; Danesi, Valentina; Viceconti, Marco

2017-01-01

The estimation of local and structural mechanical properties of bones with micro Finite Element (microFE) models based on Micro Computed Tomography images depends on the quality bone geometry is captured, reconstructed and modelled. The aim of this study was to validate microFE models predictions of local displacements for vertebral bodies and to evaluate the effect of the elastic tissue modulus on model’s predictions of axial forces. Four porcine thoracic vertebrae were axially compressed in situ, in a step-wise fashion and scanned at approximately 39μm resolution in preloaded and loaded conditions. A global digital volume correlation (DVC) approach was used to compute the full-field displacements. Homogeneous, isotropic and linear elastic microFE models were generated with boundary conditions assigned from the interpolated displacement field measured from the DVC. Measured and predicted local displacements were compared for the cortical and trabecular compartments in the middle of the specimens. Models were run with two different tissue moduli defined from microindentation data (12.0GPa) and a back-calculation procedure (4.6GPa). The predicted sum of axial reaction forces was compared to the experimental values for each specimen. MicroFE models predicted more than 87% of the variation in the displacement measurements (R2 = 0.87–0.99). However, model predictions of axial forces were largely overestimated (80–369%) for a tissue modulus of 12.0GPa, whereas differences in the range 10–80% were found for a back-calculated tissue modulus. The specimen with the lowest density showed a large number of elements strained beyond yield and the highest predictive errors. This study shows that the simplest microFE models can accurately predict quantitatively the local displacements and qualitatively the strain distribution within the vertebral body, independently from the considered bone types. PMID:28700618

Factors Predictive of Sentinel Lymph Node Involvement in Primary Breast Cancer.

PubMed

Malter, Wolfram; Hellmich, Martin; Badian, Mayhar; Kirn, Verena; Mallmann, Peter; Krämer, Stefan

2018-06-01

Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for axillary staging in patients with early-stage breast cancer. The need for therapeutic ALND is the subject of ongoing debate especially after the publication of the ACOSOG Z0011 trial. In a retrospective trial with univariate and multivariate analyses, factors predictive of sentinel lymph node involvement should be analyzed in order to define tumor characteristics of breast cancer patients, where SLNB should not be spared to receive important indicators for adjuvant treatment decisions (e.g. thoracic wall irradiation after mastectomy with or without reconstruction). Between 2006 and 2010, 1,360 patients with primary breast cancer underwent SLNB with/without ALND with evaluation of tumor localization, multicentricity and multifocality, histological subtype, tumor size, grading, lymphovascular invasion (LVI), and estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status. These characteristics were retrospectively analyzed in univariate and multivariate logistic regression models to define significant predictive factors for sentinel lymph node involvement. The multivariate analysis demonstrated that tumor size and LVI (p<0.001) were independent predictive factors for metastatic sentinel lymph node involvement in patients with early-stage breast cancer. Because of the increased risk for metastatic involvement of axillary sentinel nodes in cases with larger breast cancer or diagnosis of LVI, patients with these breast cancer characteristics should not be spared from SLNB in a clinically node-negative situation in order to avoid false-negative results with a high potential for wrong indication of primary breast reconstruction or wrong non-indication of necessary post-mastectomy radiation therapy. The prognostic impact of avoidance of axillary staging with SLNB is analyzed in the ongoing prospective INSEMA trial. Copyright© 2018, International

Predicting Posttraumatic Stress Symptom Prevalence and Local Distribution after an Earthquake with Scarce Data.

PubMed

Dussaillant, Francisca; Apablaza, Mauricio

2017-08-01

After a major earthquake, the assignment of scarce mental health emergency personnel to different geographic areas is crucial to the effective management of the crisis. The scarce information that is available in the aftermath of a disaster may be valuable in helping predict where are the populations that are in most need. The objectives of this study were to derive algorithms to predict posttraumatic stress (PTS) symptom prevalence and local distribution after an earthquake and to test whether there are algorithms that require few input data and are still reasonably predictive. A rich database of PTS symptoms, informed after Chile's 2010 earthquake and tsunami, was used. Several model specifications for the mean and centiles of the distribution of PTS symptoms, together with posttraumatic stress disorder (PTSD) prevalence, were estimated via linear and quantile regressions. The models varied in the set of covariates included. Adjusted R2 for the most liberal specifications (in terms of numbers of covariates included) ranged from 0.62 to 0.74, depending on the outcome. When only including peak ground acceleration (PGA), poverty rate, and household damage in linear and quadratic form, predictive capacity was still good (adjusted R2 from 0.59 to 0.67 were obtained). Information about local poverty, household damage, and PGA can be used as an aid to predict PTS symptom prevalence and local distribution after an earthquake. This can be of help to improve the assignment of mental health personnel to the affected localities. Dussaillant F , Apablaza M . Predicting posttraumatic stress symptom prevalence and local distribution after an earthquake with scarce data. Prehosp Disaster Med. 2017;32(4):357-367.

Multiple measures of dispositional global/local bias predict attentional blink magnitude.

PubMed

Dale, Gillian; Arnell, Karen M

2015-07-01

When the second of two targets (T2) is presented temporally close to the first target (T1) in a rapid serial visual presentation stream, accuracy to identify T2 is markedly reduced-an attentional blink (AB). While most individuals show an AB, Dale and Arnell (Atten Percept Psychophys 72(3):602-606, 2010) demonstrated that individual differences in dispositional attentional focus predicted AB performance, such that individuals who showed a natural bias toward the global level of Navon letter stimuli were less susceptible to the AB and showed a smaller AB effect. For the current study, we extended the findings of Dale and Arnell (Atten Percept Psychophys 72(3):602-606, 2010) through two experiments. In Experiment 1, we examined the relationship between dispositional global/local bias and the AB using a highly reliable hierarchical shape task measure. In Experiment 2, we examined whether three distinct global/local measures could predict AB performance. In both experiments, performance on the global/local tasks predicted subsequent AB performance, such that individuals with a greater preference for the global information showed a reduced AB. This supports previous findings, as well as recent models which discuss the role of attentional breadth in selective attention.

A systematic analysis of factors localized to damaged chromatin reveals PARP-dependent recruitment of transcription factors

PubMed Central

Izhar, Lior; Adamson, Britt; Ciccia, Alberto; Lewis, Jedd; Pontano-Vaites, Laura; Leng, Yumei; Liang, Anthony C.; Westbrook, Thomas F.; Harper, J. Wade; Elledge, Stephen J.

2015-01-01

Localization to sites of DNA damage is a hallmark of DNA damage response (DDR) proteins. To identify new DDR factors, we screened epitope-tagged proteins for localization to sites of chromatin damaged by UV laser microirradiation and found >120 proteins that localize to damaged chromatin. These include the BAF tumor suppressor complex and the ALS candidate protein TAF15. TAF15 contains multiple domains that bind damaged chromatin in a PARP-dependent manner, suggesting a possible role as glue that tethers multiple PAR chains together. Many positives were transcription factors and >70% of randomly tested transcription factors localized to sites of DNA damage and approximately 90% were PARP-dependent for localization. Mutational analyses showed that localization to damaged chromatin is DNA-binding domain-dependent. By examining Hoechst staining patterns at damage sites, we see evidence of chromatin decompaction that is PARP-dependent. We propose that PARP-regulated chromatin remodeling at sites of damage allows transient accessibility of DNA-binding proteins. PMID:26004182

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Calculation of Local Volume Factors for Relascope Cruising

Treesearch

Charles B. Briscoe

1957-01-01

In these days of climbing stumpage prices it is frequently desirable to attain more precision from a relascope cruise than is possible using ready-made volume factors. Like any factors made to be approximately applicalble over a wide range of conditions, volume factors may give very misleading results under certain local condition. For this reason it is desirable to...

Molecular markers to complement sentinel node status in predicting survival in patients with high-risk locally invasive melanoma.

PubMed

Rowe, Casey J; Tang, Fiona; Hughes, Maria Celia B; Rodero, Mathieu P; Malt, Maryrose; Lambie, Duncan; Barbour, Andrew; Hayward, Nicholas K; Smithers, B Mark; Green, Adele C; Khosrotehrani, Kiarash

2016-08-01

Sentinel lymph node status is a major prognostic marker in locally invasive cutaneous melanoma. However, this procedure is not always feasible, requires advanced logistics and carries rare but significant morbidity. Previous studies have linked markers of tumour biology to patient survival. In this study, we aimed to combine the predictive value of established biomarkers in addition to clinical parameters as indicators of survival in addition to or instead of sentinel node biopsy in a cohort of high-risk melanoma patients. Patients with locally invasive melanomas undergoing sentinel lymph node biopsy were ascertained and prospectively followed. Information on mortality was validated through the National Death Index. Immunohistochemistry was used to analyse proteins previously reported to be associated with melanoma survival, namely Ki67, p16 and CD163. Evaluation and multivariate analyses according to REMARK criteria were used to generate models to predict disease-free and melanoma-specific survival. A total of 189 patients with available archival material of their primary tumour were analysed. Our study sample was representative of the entire cohort (N = 559). Average Breslow thickness was 2.5 mm. Thirty-two (17%) patients in the study sample died from melanoma during the follow-up period. A prognostic score was developed and was strongly predictive of survival, independent of sentinel node status. The score allowed classification of risk of melanoma death in sentinel node-negative patients. Combining clinicopathological factors and established biomarkers allows prediction of outcome in locally invasive melanoma and might be implemented in addition to or in cases when sentinel node biopsy cannot be performed. © 2016 UICC.

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

NASA Astrophysics Data System (ADS)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-11-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

A Systematic Analysis of Factors Localized to Damaged Chromatin Reveals PARP-Dependent Recruitment of Transcription Factors.

PubMed

Izhar, Lior; Adamson, Britt; Ciccia, Alberto; Lewis, Jedd; Pontano-Vaites, Laura; Leng, Yumei; Liang, Anthony C; Westbrook, Thomas F; Harper, J Wade; Elledge, Stephen J

2015-06-09

Localization to sites of DNA damage is a hallmark of DNA damage response (DDR) proteins. To identify DDR factors, we screened epitope-tagged proteins for localization to sites of chromatin damaged by UV laser microirradiation and found >120 proteins that localize to damaged chromatin. These include the BAF tumor suppressor complex and the amyotrophic lateral sclerosis (ALS) candidate protein TAF15. TAF15 contains multiple domains that bind damaged chromatin in a poly-(ADP-ribose) polymerase (PARP)-dependent manner, suggesting a possible role as glue that tethers multiple PAR chains together. Many positives were transcription factors; > 70% of randomly tested transcription factors localized to sites of DNA damage, and of these, ∼90% were PARP dependent for localization. Mutational analyses showed that localization to damaged chromatin is DNA-binding-domain dependent. By examining Hoechst staining patterns at damage sites, we see evidence of chromatin decompaction that is PARP dependent. We propose that PARP-regulated chromatin remodeling at sites of damage allows transient accessibility of DNA-binding proteins. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

PubMed

Kulinkina, Alexandra V; Walz, Yvonne; Koch, Magaly; Biritwum, Nana-Kwadwo; Utzinger, Jürg; Naumova, Elena N

2018-06-04

Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies, indirectly increasing

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

Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study.

PubMed

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-02-28

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of "chimera proteins." In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.

Localized Density/Drag Prediction for Improved Onboard Orbit Propagation

DTIC Science & Technology

2009-09-01

Localized Density/Drag Prediction for Improved Onboard Orbit Propagation Nathan B. Stastny, Frank R. Chavez, Chin Lin, T. Alan Lovell , Robert A...Terrestrial Physics, Vol. 70, 774-793, 2008 3. Storz, M.F, Bowman, B.R., Branson, J.I., High Accuracy Satellite Drag Model (HASDM), AIAA/ AAS ...Geomagnetic Indices, AIAA/ AAS Astrodynamics Specialist Conference, Honolulu, HI, Aug. 2008 5. Bruinsma, S., Biancale, R., Total Densities Derived from

Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments.

PubMed

Gorter, Florien A; Aarts, Mark G M; Zwaan, Bas J; de Visser, J Arjan G M

2018-01-01

The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change. Copyright © 2018 by the Genetics Society of America.

Can Childhood Factors Predict Workplace Deviance?

PubMed Central

Piquero, Nicole Leeper; Moffitt, Terrie E.

2013-01-01

Compared to the more common focus on street crime, empirical research on workplace deviance has been hampered by highly select samples, cross-sectional research designs, and limited inclusion of relevant predictor variables that bear on important theoretical debates. A key debate concerns the extent to which childhood conduct-problem trajectories influence crime over the life-course, including adults’ workplace crime, whether childhood low self-control is a more important determinant than trajectories, and/or whether each or both of these childhood factors relate to later criminal activity. This paper provides evidence on this debate by examining two types of workplace deviance: production and property deviance separately for males and females. We use data from the Dunedin Multidisciplinary Health and Development Study, a birth cohort followed into adulthood, to examine how childhood factors (conduct-problem trajectories and low self-control) and then adult job characteristics predict workplace deviance at age 32. Analyses revealed that none of the childhood factors matter for predicting female deviance in the workplace but that conduct-problem trajectories did account for male workplace deviance. PMID:24882937

Sexual selection affects local extinction and turnover in bird communities

USGS Publications Warehouse

Doherty, P.F.; Sorci, G.; Royle, J. Andrew; Hines, J.E.; Nichols, J.D.; Boulinier, T.

2003-01-01

Predicting extinction risks has become a central goal for conservation and evolutionary biologists interested in population and community dynamics. Several factors have been put forward to explain risks of extinction, including ecological and life history characteristics of individuals. For instance, factors that affect the balance between natality and mortality can have profound effects on population persistence. Sexual selection has been identified as one such factor. Populations under strong sexual selection experience a number of costs ranging from increased predation and parasitism to enhanced sensitivity to environmental and demographic stochasticity. These findings have led to the prediction that local extinction rates should be higher for species/populations with intense sexual selection. We tested this prediction by analyzing the dynamics of natural bird communities at a continental scale over a period of 21 years (1975-1996), using relevant statistical tools. In agreement with the theoretical prediction, we found that sexual selection increased risks of local extinction (dichromatic birds had on average a 23% higher local extinction rate than monochromatic species). However, despite higher local extinction probabilities, the number of dichromatic species did not decrease over the period considered in this study. This pattern was caused by higher local turnover rates of dichromatic species, resulting in relatively stable communities for both groups of species. Our results suggest that these communities function as metacommunities, with frequent local extinctions followed by colonization. Anthropogenic factors impeding dispersal might therefore have a significant impact on the global persistence of sexually selected species.

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study

PubMed Central

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-01-01

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of “chimera proteins.” In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape. PMID:16488978

Predicting local adaptation in fragmented plant populations: implications for restoration genetics

PubMed Central

Pickup, Melinda; Field, David L; Rowell, David M; Young, Andrew G

2012-01-01

Understanding patterns and correlates of local adaptation in heterogeneous landscapes can provide important information in the selection of appropriate seed sources for restoration. We assessed the extent of local adaptation of fitness components in 12 population pairs of the perennial herb Rutidosis leptorrhynchoides (Asteraceae) and examined whether spatial scale (0.7–600 km), environmental distance, quantitative (QST) and neutral (FST) genetic differentiation, and size of the local and foreign populations could predict patterns of adaptive differentiation. Local adaptation varied among populations and fitness components. Including all population pairs, local adaptation was observed for seedling survival, but not for biomass, while foreign genotype advantage was observed for reproduction (number of inflorescences). Among population pairs, local adaptation increased with QST and local population size for biomass. QST was associated with environmental distance, suggesting ecological selection for phenotypic divergence. However, low FST and variation in population structure in small populations demonstrates the interaction of gene flow and drift in constraining local adaptation in R. leptorrhynchoides. Our study indicates that for species in heterogeneous landscapes, collecting seed from large populations from similar environments to candidate sites is likely to provide the most appropriate seed sources for restoration. PMID:23346235

[Predictive factors of mortality of the burnt persons: study on 221 adults hospitalized between 2004 and 2009].

PubMed

Elkafssaoui, S; Hami, H; Mrabet, M; Bouaiti, E; Tourabi, K; Quyou, A; Soulaymani, A; Ihrai, H

2014-06-01

The objective of the present study is the evaluation of the predictive factors of mortality to a troop of Moroccan grown-up serious burnt persons. Variables analyzed in the study are: the age, the sex, the localization of the burn, the degree of burn, indicates Total Body Surface Area (TBSA), indicate Unit of Standard Burn (UBS) and the indication of leases, sepsis and the medical histories (tobacco, diabetes). Factors associated significantly to a mortality raised at the burned patients were the female genital organ, the localization of the burn at the level of the head, the sepsis, one TBSA greater or equal to 20%, an UBS greater or equal to 200 and an indication of leases greater or equal to 75. Other factors such as the age, the degree of burn and the histories did not show a significant difference. An evaluation and a good knowledge of factors associated to a high risk of death allow an adequate coverage of this category of patients. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Lack of congruence in species diversity indices and community structures of planktonic groups based on local environmental factors.

PubMed

Doi, Hideyuki; Chang, Kwang-Hyeon; Nishibe, Yuichiro; Imai, Hiroyuki; Nakano, Shin-ichi

2013-01-01

The importance of analyzing the determinants of biodiversity and community composition by using multiple trophic levels is well recognized; however, relevant data are lacking. In the present study, we investigated variations in species diversity indices and community structures of the plankton taxonomic groups-zooplankton, rotifers, ciliates, and phytoplankton-under a range of local environmental factors in pond ecosystems. For each planktonic group, we estimated the species diversity index by using linear models and analyzed the community structure by using canonical correspondence analysis. We showed that the species diversity indices and community structures varied among the planktonic groups and according to local environmental factors. The observed lack of congruence among the planktonic groups may have been caused by niche competition between groups with similar trophic guilds or by weak trophic interactions. Our findings highlight the difficulty of predicting total biodiversity within a system, based upon a single taxonomic group. Thus, to conserve the biodiversity of an ecosystem, it is crucial to consider variations in species diversity indices and community structures of different taxonomic groups, under a range of local conditions.

Expression, fermentation and purification of a predicted intrinsically disordered region of the transcription factor, NFAT5.

PubMed

DuMond, Jenna F; He, Yi; Burg, Maurice B; Ferraris, Joan D

2015-11-01

Hypertonicity stimulates Nuclear Factor of Activated T-cells 5 (NFAT5) nuclear localization and transactivating activity. Many transcription factors are known to contain intrinsically disordered regions (IDRs) which become more structured with local environmental changes such as osmolality, temperature and tonicity. The transactivating domain of NFAT5 is predicted to be intrinsically disordered under normal tonicity, and under high NaCl, the activity of this domain is increased. To study the binding of co-regulatory proteins at IDRs a cDNA construct expressing the NFAT5 TAD was created and transformed into Escherichia coli cells. Transformed E. coli cells were mass produced by fermentation and extracted by cell lysis to release the NFAT5 TAD. The NFAT5 TAD was subsequently purified using a His-tag column, cation exchange chromatography as well as hydrophobic interaction chromatography and then characterized by mass spectrometry (MS). Published by Elsevier Inc.

Family Factors Predicting Categories of Suicide Risk

ERIC Educational Resources Information Center

Randell, Brooke P.; Wang, Wen-Ling; Herting, Jerald R.; Eggert, Leona L.

2006-01-01

We compared family risk and protective factors among potential high school dropouts with and without suicide-risk behaviors (SRB) and examined the extent to which these factors predict categories of SRB. Subjects were randomly selected from among potential dropouts in 14 high schools. Based upon suicide-risk status, 1,083 potential high school…

Performance predictions for a parabolic localizer antenna on Runway 28R - San Francisco Airport.

DOT National Transportation Integrated Search

1973-06-01

The TSC ILS localizer model is used to predict the performance of the Texas Instruments "wide aperture" parabolic antenna as a localizer system for runway 28R at San Francisco Airport. Course derogation caused by the new American Airlines hangar is c...

Clinical Factors Predict Atezolizumab Response.

PubMed

2018-04-01

Researchers have presented a new model that uses six readily available clinical factors to predict whether a patient with advanced bladder cancer who has already received platinum chemotherapy will respond to treatment with the PD-L1 inhibitor atezolizumab. The results may help patients and their doctors decide how to proceed with treatment. ©2018 American Association for Cancer Research.

Predictive Factors for Death After Snake Envenomation in Myanmar.

PubMed

Aye, Kyi-Phyu; Thanachartwet, Vipa; Soe, Chit; Desakorn, Varunee; Chamnanchanunt, Supat; Sahassananda, Duangjai; Supaporn, Thanom; Sitprija, Visith

2018-06-01

Factors predictive for death from snake envenomation vary between studies, possibly due to variation in host genetic factors and venom composition. This study aimed to evaluate predictive factors for death from snake envenomation in Myanmar. A prospective study was performed among adult patients with snakebite admitted to tertiary hospitals in Yangon, Myanmar, from May 2015 to August 2016. Data including clinical variables and laboratory parameters, management, and outcomes were evaluated. Multivariate regression analysis was performed to evaluate factors predictive for death at the time of presentation to the hospital. Of the 246 patients with snake envenomation recruited into the study, 225 (92%) survived and 21 (8%) died during hospitalization. The snake species responsible for a bite was identified in 74 (30%) of the patients; the majority of bites were from Russell's vipers (63 patients, 85%). The independent factors predictive for death included 1) duration from bite to arrival at the hospital >1 h (odds ratio [OR]: 9.0, 95% confidence interval [CI]: 1.1-75.2; P=0.04); 2) white blood cell counts >20 ×10 3 cells·μL -1 (OR: 8.9, 95% CI: 2.3-33.7; P=0.001); and 3) the presence of capillary leakage (OR: 3.7, 95% CI: 1.2-11.2; P=0.02). A delay in antivenom administration >4 h increases risk of death (11/21 deaths). Patients who present with these independent predictive factors should be recognized and provided with early appropriate intervention to reduce the mortality rate among adults with snake envenomation in Myanmar. Copyright © 2018 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information.

PubMed

Zahiri, Javad; Mohammad-Noori, Morteza; Ebrahimpour, Reza; Saadat, Samaneh; Bozorgmehr, Joseph H; Goldberg, Tatyana; Masoudi-Nejad, Ali

2014-12-01

Protein-protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse. The results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems. Copyright © 2014 Elsevier Inc. All rights reserved.

k-Nearest neighbour local linear prediction of scalp EEG activity during intermittent photic stimulation.

PubMed

Erla, Silvia; Faes, Luca; Tranquillini, Enzo; Orrico, Daniele; Nollo, Giandomenico

2011-05-01

The characterization of the EEG response to photic stimulation (PS) is an important issue with significant clinical relevance. This study aims to quantify and map the complexity of the EEG during PS, where complexity is measured as the degree of unpredictability resulting from local linear prediction. EEG activity was recorded with eyes closed (EC) and eyes open (EO) during resting and PS at 5, 10, and 15 Hz in a group of 30 healthy subjects and in a case-report of a patient suffering from cerebral ischemia. The mean squared prediction error (MSPE) resulting from k-nearest neighbour local linear prediction was calculated in each condition as an index of EEG unpredictability. The linear or nonlinear nature of the system underlying EEG activity was evaluated quantifying MSPE as a function of the neighbourhood size during local linear prediction, and by surrogate data analysis as well. Unpredictability maps were obtained for each subject interpolating MSPE values over a schematic head representation. Results on healthy subjects evidenced: (i) the prevalence of linear mechanisms in the generation of EEG dynamics, (ii) the lower predictability of EO EEG, (iii) the desynchronization of oscillatory mechanisms during PS leading to increased EEG complexity, (iv) the entrainment of alpha rhythm during EC obtained by 10 Hz PS, and (v) differences of EEG predictability among different scalp regions. Ischemic patient showed different MSPE values in healthy and damaged regions. The EEG predictability decreased moving from the early acute stage to a stage of partial recovery. These results suggest that nonlinear prediction can be a useful tool to characterize EEG dynamics during PS protocols, and may consequently constitute a complement of quantitative EEG analysis in clinical applications. Copyright © 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

[Predictive factors associated with severity of asthma exacerbations].

PubMed

Atiş, Sibel; Kaplan, Eylem Sercan; Ozge, Cengiz; Bayindir, Suzan

2008-01-01

Several factors have been accused for asthma exacerbations, however, very few studies have evaluated whether different factors predict severity of asthma exacerbation. We aimed to determine the predictive factors for severity of asthma exacerbation. Retrospective analysis of data on 93 patients visited our emergency-department because of asthma exacerbation was reviewed. Hospitalization in intensive care unit and/or intubation because of asthma was accepted as the criteria for severe exacerbation. Logistic regression analysis estimated the strength of association of each variable, potentially related to severe asthmatic exacerbation, with severe/very severe as compared to mild/moderate asthmatic exacerbation. Independent variables included in the analysis were age, sex, smoking history, inhaler steroid using, compliance with medication, chronic asthma severity, presence of additional atopic diseases, prick test positivity, provocative factors, number of short-acting beta(2)-agonist using, number of visits to emergency department for asthma over one year period, previous severe exacerbation, pulmonary functions, and blood eosinophil count. 20 were severe/very severe and 73 mild/moderate asthmatic exacerbation. Frequent using of short-acting beta(2)-agonist (OR= 1.5, 95% CI= 1.08-5.3, p= 0.003), noncompliance with medication (OR= 3.6, 95% CI= 1.3-9.9, p= 0.013), previous severe asthmatic exacerbation (OR= 3.8, 95% CI= 1.48-10.01, p= 0.005) and recent admission to hospital (OR= 2.9, 95% CI= 1.07-8.09, p= 0.037) were found to be predictive factors for severe asthmatic exacerbation. Different predictive factors, in particular frequent using of short-acting beta(2)-agonist and noncompliance with medication may be associated with severe asthma exacerbations compared to milder exacerbations. This suggests different mechanisms are responsible for severity of asthma exacerbation.

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

PubMed

Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

2017-11-15

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting

Predictive factors for cosmetic surgery: a hospital-based investigation.

PubMed

Li, Jun; Li, Qian; Zhou, Bei; Gao, Yanli; Ma, Jiehua; Li, Jingyun

2016-01-01

Cosmetic surgery is becoming increasingly popular in China. However, reports on the predictive factors for cosmetic surgery in Chinese individuals are scarce in the literature. We retrospectively analyzed 4550 cosmetic surgeries performed from January 2010 to December 2014 at a single center in China. Data collection included patient demographics and type of cosmetic surgery. Predictive factors were age, sex, marital status, occupational status, educational degree, and having had children. Predictive factors for the three major cosmetic surgeries were determined using a logistic regression analysis. Patients aged 19-34 years accounted for the most popular surgical procedures (76.9 %). The most commonly requested procedures were eye surgery, Botox injection, and nevus removal. Logistic regression analysis showed that higher education level (college, P = 0.01, OR 1.21) was predictive for eye surgery. Age (19-34 years, P = 0.00, OR 33.39; 35-50, P = 0.00, OR 31.34; ≥51, P = 0.00, OR 16.42), female sex (P = 0.00, OR 9.19), employment (service occupations, P = 0.00, OR 2.31; non-service occupations, P = 0.00, OR 1.76), and higher education level (college, P = 0.00, OR 1.39) were independent predictive factors for Botox injection. Married status (P = 0.00, OR 1.57), employment (non-service occupations, P = 0.00, OR 1.50), higher education level (masters, P = 0.00, OR 6.61), and having children (P = 0.00, OR 1.45) were independent predictive factors for nevus removal. The principal three cosmetic surgeries (eye surgery, Botox injection, and nevus removal) were associated with multiple variables. Patients employed in non-service occupations were more inclined to undergo Botox injection and nevus removal. Cohort study, Level III.

Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2016-12-01

Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization. Copyright © 2016 Elsevier Inc. All rights reserved.

Validation of the thermophysiological model by Fiala for prediction of local skin temperatures

NASA Astrophysics Data System (ADS)

Martínez, Natividad; Psikuta, Agnes; Kuklane, Kalev; Quesada, José Ignacio Priego; de Anda, Rosa María Cibrián Ortiz; Soriano, Pedro Pérez; Palmer, Rosario Salvador; Corberán, José Miguel; Rossi, René Michel; Annaheim, Simon

2016-12-01

The most complete and realistic physiological data are derived from direct measurements during human experiments; however, they present some limitations such as ethical concerns, time and cost burden. Thermophysiological models are able to predict human thermal response in a wide range of environmental conditions, but their use is limited due to lack of validation. The aim of this work was to validate the thermophysiological model by Fiala for prediction of local skin temperatures against a dedicated database containing 43 different human experiments representing a wide range of conditions. The validation was conducted based on root-mean-square deviation (rmsd) and bias. The thermophysiological model by Fiala showed a good precision when predicting core and mean skin temperature (rmsd 0.26 and 0.92 °C, respectively) and also local skin temperatures for most body sites (average rmsd for local skin temperatures 1.32 °C). However, an increased deviation of the predictions was observed for the forehead skin temperature (rmsd of 1.63 °C) and for the thigh during exercising exposures (rmsd of 1.41 °C). Possible reasons for the observed deviations are lack of information on measurement circumstances (hair, head coverage interference) or an overestimation of the sweat evaporative cooling capacity for the head and thigh, respectively. This work has highlighted the importance of collecting details about the clothing worn and how and where the sensors were attached to the skin for achieving more precise results in the simulations.

Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.

PubMed

Wu, Yao; Yang, Wei; Lu, Lijun; Lu, Zhentai; Zhong, Liming; Huang, Meiyan; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan

2016-10-01

Attenuation correction is important for PET reconstruction. In PET/MR, MR intensities are not directly related to attenuation coefficients that are needed in PET imaging. The attenuation coefficient map can be derived from CT images. Therefore, prediction of CT substitutes from MR images is desired for attenuation correction in PET/MR. This study presents a patch-based method for CT prediction from MR images, generating attenuation maps for PET reconstruction. Because no global relation exists between MR and CT intensities, we propose local diffeomorphic mapping (LDM) for CT prediction. In LDM, we assume that MR and CT patches are located on 2 nonlinear manifolds, and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Locality is important in LDM and is constrained by the following techniques. The first is local dictionary construction, wherein, for each patch in the testing MR image, a local search window is used to extract patches from training MR/CT pairs to construct MR and CT dictionaries. The k-nearest neighbors and an outlier detection strategy are then used to constrain the locality in MR and CT dictionaries. Second is local linear representation, wherein, local anchor embedding is used to solve MR dictionary coefficients when representing the MR testing sample. Under these local constraints, dictionary coefficients are linearly transferred from the MR manifold to the CT manifold and used to combine CT training samples to generate CT predictions. Our dataset contains 13 healthy subjects, each with T1- and T2-weighted MR and CT brain images. This method provides CT predictions with a mean absolute error of 110.1 Hounsfield units, Pearson linear correlation of 0.82, peak signal-to-noise ratio of 24.81 dB, and Dice in bone regions of 0.84 as compared with real CTs. CT substitute-based PET reconstruction has a regression slope of 1.0084 and R 2 of 0.9903 compared with real CT-based PET. In this method, no

Novel Approach for Prediction of Localized Necking in Case of Nonlinear Strain Paths

NASA Astrophysics Data System (ADS)

Drotleff, K.; Liewald, M.

2017-09-01

Rising customer expectations regarding design complexity and weight reduction of sheet metal components alongside with further reduced time to market implicate increased demand for process validation using numerical forming simulation. Formability prediction though often is still based on the forming limit diagram first presented in the 1960s. Despite many drawbacks in case of nonlinear strain paths and major advances in research in the recent years, the forming limit curve (FLC) is still one of the most commonly used criteria for assessing formability of sheet metal materials. Especially when forming complex part geometries nonlinear strain paths may occur, which cannot be predicted using the conventional FLC-Concept. In this paper a novel approach for calculation of FLCs for nonlinear strain paths is presented. Combining an interesting approach for prediction of FLC using tensile test data and IFU-FLC-Criterion a model for prediction of localized necking for nonlinear strain paths can be derived. Presented model is purely based on experimental tensile test data making it easy to calibrate for any given material. Resulting prediction of localized necking is validated using an experimental deep drawing specimen made of AA6014 material having a sheet thickness of 1.04 mm. The results are compared to IFU-FLC-Criterion based on data of pre-stretched Nakajima specimen.

A Novel Risk Stratification to Predict Local-Regional Failures in Urothelial Carcinoma of the Bladder After Radical Cystectomy

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

Baumann, Brian C.; Guzzo, Thomas J.; He Jiwei

2013-01-01

Purpose: Local-regional failures (LF) following radical cystectomy (RC) plus pelvic lymph node dissection (PLND) with or without chemotherapy for invasive urothelial bladder carcinoma are more common than previously reported. Adjuvant radiation therapy (RT) could reduce LF but currently has no defined role because of previously reported morbidity. Modern techniques with improved normal tissue sparing have rekindled interest in RT. We assessed the risk of LF and determined those factors that predict recurrence to facilitate patient selection for future adjuvant RT trials. Methods and Materials: From 1990-2008, 442 patients with urothelial bladder carcinoma at University of Pennsylvania were prospectively followed aftermore » RC plus PLND with or without chemotherapy with routine pelvic computed tomography (CT) or magnetic resonance imaging (MRI). One hundred thirty (29%) patients received chemotherapy. LF was any pelvic failure detected before or within 3 months of distant failure. Competing risk analyses identified factors predicting increased LF risk. Results: On univariate analysis, pathologic stage {>=}pT3, <10 nodes removed, positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology significantly predicted LF; node density was marginally predictive, but use of chemotherapy, number of positive nodes, type of surgical diversion, age, gender, race, smoking history, and body mass index were not. On multivariate analysis, only stage {>=}pT3 and <10 nodes removed were significant independent LF predictors with hazard ratios of 3.17 and 2.37, respectively (P<.01). Analysis identified 3 patient subgroups with significantly different LF risks: low-risk ({<=}pT2), intermediate-risk ({>=}pT3 and {>=}10 nodes removed), and high-risk ({>=}pT3 and <10 nodes) with 5-year LF rates of 8%, 23%, and 42%, respectively (P<.01). Conclusions: This series using routine CT and MRI surveillance to detect LF confirms that such failures are relatively

Diffusion in the presence of a local attracting factor: Theory and interdisciplinary applications.

PubMed

Veermäe, Hardi; Patriarca, Marco

2017-06-01

In many complex diffusion processes the drift of random walkers is not caused by an external force, as in the case of Brownian motion, but by local variations of fitness perceived by the random walkers. In this paper, a simple but general framework is presented that describes such a type of random motion and may be of relevance in different problems, such as opinion dynamics, cultural spreading, and animal movement. To this aim, we study the problem of a random walker in d dimensions moving in the presence of a local heterogeneous attracting factor expressed in terms of an assigned position-dependent "attractiveness function." At variance with standard Brownian motion, the attractiveness function introduced here regulates both the advection and diffusion of the random walker, thus providing testable predictions for a specific form of fluctuation-relations. We discuss the relation between the drift-diffusion equation based on the attractiveness function and that describing standard Brownian motion, and we provide some explicit examples illustrating its relevance in different fields, such as animal movement, chemotactic diffusion, and social dynamics.

Diffusion in the presence of a local attracting factor: Theory and interdisciplinary applications

NASA Astrophysics Data System (ADS)

Veermäe, Hardi; Patriarca, Marco

2017-06-01

In many complex diffusion processes the drift of random walkers is not caused by an external force, as in the case of Brownian motion, but by local variations of fitness perceived by the random walkers. In this paper, a simple but general framework is presented that describes such a type of random motion and may be of relevance in different problems, such as opinion dynamics, cultural spreading, and animal movement. To this aim, we study the problem of a random walker in d dimensions moving in the presence of a local heterogeneous attracting factor expressed in terms of an assigned position-dependent "attractiveness function." At variance with standard Brownian motion, the attractiveness function introduced here regulates both the advection and diffusion of the random walker, thus providing testable predictions for a specific form of fluctuation-relations. We discuss the relation between the drift-diffusion equation based on the attractiveness function and that describing standard Brownian motion, and we provide some explicit examples illustrating its relevance in different fields, such as animal movement, chemotactic diffusion, and social dynamics.

Link prediction based on local weighted paths for complex networks

NASA Astrophysics Data System (ADS)

Yao, Yabing; Zhang, Ruisheng; Yang, Fan; Yuan, Yongna; Hu, Rongjing; Zhao, Zhili

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

An observer's guide to the (Local Group) dwarf galaxies: predictions for their own dwarf satellite populations

NASA Astrophysics Data System (ADS)

Dooley, Gregory A.; Peter, Annika H. G.; Yang, Tianyi; Willman, Beth; Griffen, Brendan F.; Frebel, Anna

2017-11-01

A recent surge in the discovery of new ultrafaint dwarf satellites of the Milky Way has inspired the idea of searching for faint satellites, 103 M⊙ Local Group. Such satellites would be subject to weaker environmental influences than Milky Way satellites, and could lead to new insights on low-mass galaxy formation. In this paper, we predict the number of luminous satellites expected around field dwarf galaxies by applying several abundance-matching models and a reionization model to the dark-matter only Caterpillar simulation suite. For three of the four abundance-matching models used, we find a >99 per cent chance that at least one satellite with stellar mass M* > 105 M⊙ exists around the combined five Local Group field dwarf galaxies with the largest stellar mass. When considering satellites with M* > 104 M⊙, we predict a combined 5-25 satellites for the five largest field dwarfs, and 10-50 for the whole Local Group field dwarf population. Because of the relatively small number of predicted dwarfs, and their extended spatial distribution, a large fraction each Local Group dwarf's virial volume will need to be surveyed to guarantee discoveries. We compute the predicted number of satellites in a given field of view of specific Local Group galaxies, as a function of minimum satellite luminosity, and explicitly obtain such values for the Solitary Local dwarfs survey. Uncertainties in abundance-matching and reionization models are large, implying that comprehensive searches could lead to refinements of both models.

ILS Localizer Performance Prediction of an Alford 1B Array Near a Limited Access Road at the New Orleans Airport.

DOT National Transportation Integrated Search

1974-08-01

The Transportation Systems Center (TSC) ILS Localizer Performance Prediction Model was used to predict the derogation to an Alford 1B Localizer caused by vehicular traffic traveling on a roadway to be located in front of the localizer. Several differ...

[Predictive factors of mortality in extremely preterm infants].

PubMed

Lin, L; Fang, M C; Jiang, H; Zhu, M L; Chen, S Q; Lin, Z L

2018-04-02

Objective: To investigate the predictive factors of mortality in extremely preterm infants. Methods: The retrospective case-control study was accomplished in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. A total of 268 extremely preterm infants seen from January 1, 1999 to December 31, 2015 were divided into survival group (192 cases) and death group (76 cases). The potential predictive factors of mortality were identified by univariate analysis, and then analyzed by multivariate unconditional Logistic regression analysis. The mortality and predictive factors were also compared between two time periods, which were January 1, 1999 to December 31, 2007 (65 cases) and January 1, 2008 to December 31, 2015 (203 cases). Results: The median gestational age (GA) of extremely preterm infants was 27 weeks (23 +3 -27 +6 weeks). The mortality was higher in infants with GA of 25-<26 weeks ( OR= 2.659, 95% CI: 1.211-5.840) and<25 weeks ( OR= 10.029, 95% CI: 3.266-30.792) compared to that in infants with GA> 26 weeks. From January 1, 2008 to December 31, 2015, the number of extremely preterm infants was increased significantly compared to the previous 9 years, while the mortality decreased significantly ( OR= 0.490, 95% CI: 0.272-0.884). Multivariate unconditional Logistic regression analysis showed that GA below 25 weeks ( OR= 6.033, 95% CI: 1.393-26.133), lower birth weight ( OR= 0.997, 95% CI: 0.995-1.000), stage Ⅲ necrotizing enterocolitis (NEC) ( OR= 15.907, 95% CI: 3.613-70.033), grade Ⅰ and Ⅱ intraventricular hemorrhage (IVH) ( OR= 0.260, 95% CI: 0.117-0.575) and dependence on invasive mechanical ventilation ( OR= 3.630, 95% CI: 1.111-11.867) were predictive factors of mortality in extremely preterm infants. Conclusions: GA below 25 weeks, lower birth weight, stage Ⅲ NEC and dependence on invasive mechanical ventilation are risk factors of mortality in extremely preterm infants. But grade ⅠandⅡ IVH is protective

Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

PubMed

Miranian, A; Abdollahzade, M

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

Examining Factors Predicting Students' Digital Competence

ERIC Educational Resources Information Center

Hatlevik, Ove Edvard; Guðmundsdóttir, Gréta Björk; Loi, Massimo

2015-01-01

The purpose of this study was to examine factors predicting lower secondary school students' digital competence and to explore differences between students when it comes to digital competence. Results from a digital competence test and survey in lower secondary school will be presented. It is important to learn more about and investigate what…

Local sharpening and subspace wavefront correction with predictive dynamic digital holography

NASA Astrophysics Data System (ADS)

Sulaiman, Sennan; Gibson, Steve

2017-09-01

Digital holography holds several advantages over conventional imaging and wavefront sensing, chief among these being significantly fewer and simpler optical components and the retrieval of complex field. Consequently, many imaging and sensing applications including microscopy and optical tweezing have turned to using digital holography. A significant obstacle for digital holography in real-time applications, such as wavefront sensing for high energy laser systems and high speed imaging for target racking, is the fact that digital holography is computationally intensive; it requires iterative virtual wavefront propagation and hill-climbing to optimize some sharpness criteria. It has been shown recently that minimum-variance wavefront prediction can be integrated with digital holography and image sharpening to reduce significantly large number of costly sharpening iterations required to achieve near-optimal wavefront correction. This paper demonstrates further gains in computational efficiency with localized sharpening in conjunction with predictive dynamic digital holography for real-time applications. The method optimizes sharpness of local regions in a detector plane by parallel independent wavefront correction on reduced-dimension subspaces of the complex field in a spectral plane.

Predictive and Prognostic Factors in Definition of Risk Groups in Endometrial Carcinoma

PubMed Central

Sorbe, Bengt

2012-01-01

Background. The aim was to evaluate predictive and prognostic factors in a large consecutive series of endometrial carcinomas and to discuss pre- and postoperative risk groups based on these factors. Material and Methods. In a consecutive series of 4,543 endometrial carcinomas predictive and prognostic factors were analyzed with regard to recurrence rate and survival. The patients were treated with primary surgery and adjuvant radiotherapy. Two preoperative and three postoperative risk groups were defined. DNA ploidy was included in the definitions. Eight predictive or prognostic factors were used in multivariate analyses. Results. The overall recurrence rate of the complete series was 11.4%. Median time to relapse was 19.7 months. In a multivariate logistic regression analysis, FIGO grade, myometrial infiltration, and DNA ploidy were independent and statistically predictive factors with regard to recurrence rate. The 5-year overall survival rate was 73%. Tumor stage was the single most important factor with FIGO grade on the second place. DNA ploidy was also a significant prognostic factor. In the preoperative risk group definitions three factors were used: histology, FIGO grade, and DNA ploidy. Conclusions. DNA ploidy was an important and significant predictive and prognostic factor and should be used both in preoperative and postoperative risk group definitions. PMID:23209924

Predictive factors for work capacity in patients with musculoskeletal disorders.

PubMed

Lydell, Marie; Baigi, Amir; Marklund, Bertil; Månsson, Jörgen

2005-09-01

To identify predictive factors for work capacity in patients with musculoskeletal disorders. A descriptive, evaluative, quantitative study. The study was based on 385 patients who participated in a rehabilitation programme. Patients were divided into 2 groups depending on their ability to work. The groups were compared with each other with regard to sociodemographic factors, diagnoses, disability pension and number of sick days. The patient's level of exercise habits, ability to undertake activities, physical capacity, pain and quality of life were compared further using logistic regression analysis. Predictive factors for work capacity, such as ability to undertake activities, quality of life and fitness on exercise, were identified as important independent factors. Other well-known factors, i.e. gender, age, education, pain and earlier sickness certification periods, were also identified. Factors that were not significantly different between the groups were employment status, profession, diagnosis and levels of exercise habits. Identifying predictors for ability to return to work is an essential task for deciding on suitable individual rehabilitation. This study identified new predictive factors, such as ability to undertake activities, quality of life and fitness on exercise.

Predictive factors for intrauterine growth restriction.

PubMed

Albu, A R; Anca, A F; Horhoianu, V V; Horhoianu, I A

2014-06-15

Reduced fetal growth is seen in about 10% of the pregnancies but only a minority has a pathological background and is known as intrauterine growth restriction or fetal growth restriction (IUGR / FGR). Increased fetal and neonatal mortality and morbidity as well as adult pathologic conditions are often associated to IUGR. Risk factors for IUGR are easy to assess but have poor predictive value. For the diagnostic purpose, biochemical serum markers, ultrasound and Doppler study of uterine and spiral arteries, placental volume and vascularization, first trimester growth pattern are object of assessment today. Modern evaluations propose combined algorithms using these strategies, all with the goal of a better prediction of risk pregnancies.

ADOT state-specific crash prediction models : an Arizona needs study.

DOT National Transportation Integrated Search

2016-12-01

The predictive method in the Highway Safety Manual (HSM) includes a safety performance function (SPF), : crash modification factors (CMFs), and a local calibration factor (C), if available. Two alternatives exist for : applying the HSM prediction met...

Predictive factors for poor prognosis febrile neutropenia.

PubMed

Ahn, Shin; Lee, Yoon-Seon

2012-07-01

Most patients with chemotherapy-induced febrile neutropenia recover rapidly without serious complications. However, it still remains a life-threatening treatment-related toxicity, and is associated with dose reductions and delays of chemotherapeutic agents that may compromise treatment outcomes. Recent developments of risk stratification enabled early discharge with oral antibiotics for low-risk patients. However, even in low-risk patients, medical complications including bacteremia could happen. The authors reviewed recent literature to provide an update on research regarding predictive factors for poor prognosis in patients with febrile neutropenia. Various prognostic factors have been suggested with controversies. Hematological parameters, prophylactic measurements and patient-specific risk factors showed inconsistent results. MASCC risk-index score, which was originally developed to identify low-risk patients, in turn showed that the lower the MASCC score, the poorer the prognosis of febrile neutropenia, with very low levels (<15), the rate of complications was high. Patients with severe sepsis and septic shock commonly had procalcitonin concentration above 2.0 ng/ml, and this level should be considered at high risk of poor prognosis. Lower MASCC score and higher procalcitonin concentration can predict poor outcomes in febrile neutropenia. More research is required with regard to the other factors showing controversies.

Predictive local receptive fields based respiratory motion tracking for motion-adaptive radiotherapy.

PubMed

Yubo Wang; Tatinati, Sivanagaraja; Liyu Huang; Kim Jeong Hong; Shafiq, Ghufran; Veluvolu, Kalyana C; Khong, Andy W H

2017-07-01

Extracranial robotic radiotherapy employs external markers and a correlation model to trace the tumor motion caused by the respiration. The real-time tracking of tumor motion however requires a prediction model to compensate the latencies induced by the software (image data acquisition and processing) and hardware (mechanical and kinematic) limitations of the treatment system. A new prediction algorithm based on local receptive fields extreme learning machines (pLRF-ELM) is proposed for respiratory motion prediction. All the existing respiratory motion prediction methods model the non-stationary respiratory motion traces directly to predict the future values. Unlike these existing methods, the pLRF-ELM performs prediction by modeling the higher-level features obtained by mapping the raw respiratory motion into the random feature space of ELM instead of directly modeling the raw respiratory motion. The developed method is evaluated using the dataset acquired from 31 patients for two horizons in-line with the latencies of treatment systems like CyberKnife. Results showed that pLRF-ELM is superior to that of existing prediction methods. Results further highlight that the abstracted higher-level features are suitable to approximate the nonlinear and non-stationary characteristics of respiratory motion for accurate prediction.

Predictive factors for intrauterine growth restriction

PubMed Central

Albu, AR; Anca, AF; Horhoianu, VV; Horhoianu, IA

2014-01-01

Abstract Reduced fetal growth is seen in about 10% of the pregnancies but only a minority has a pathological background and is known as intrauterine growth restriction or fetal growth restriction (IUGR / FGR). Increased fetal and neonatal mortality and morbidity as well as adult pathologic conditions are often associated to IUGR. Risk factors for IUGR are easy to assess but have poor predictive value. For the diagnostic purpose, biochemical serum markers, ultrasound and Doppler study of uterine and spiral arteries, placental volume and vascularization, first trimester growth pattern are object of assessment today. Modern evaluations propose combined algorithms using these strategies, all with the goal of a better prediction of risk pregnancies. Abbreviations: SGA = small for gestational age; IUGR = intrauterine growth restriction; FGR = fetal growth restriction; IUFD = intrauterine fetal demise; HIV = human immunodeficiency virus; PAPP-A = pregnancy associated plasmatic protein A; β-hCG = beta human chorionic gonadotropin; MoM = multiple of median; ADAM-12 = A-disintegrin and metalloprotease 12; PP-13 = placental protein 13; VEGF = vascular endothelial growth factor; PlGF = placental growth factor; sFlt-1 = soluble fms-like tyrosine kinase-1; UAD = uterine arteries Doppler ultrasound; RI = resistence index; PI = pulsatility index; VOCAL = Virtual Organ Computer–Aided Analysis software; VI = vascularization index; FI = flow index; VFI = vascularization flow index; PQ = placental quotient PMID:25408721

Predictive Factors of Anxiety and Depression in Patients with Acute Coronary Syndrome.

PubMed

Altino, Denise Meira; Nogueira-Martins, Luiz Antônio; de Barros, Alba Lucia Bottura Leite; Lopes, Juliana de Lima

2017-12-01

To identify the predictive factors of anxiety and depression in patients with acute coronary syndrome. Cross-sectional and retrospective study conducted with 120 patients hospitalized with acute coronary syndrome. Factors interfering with anxiety and depression were assessed. Anxiety was related to sex, stress, years of education, and depression, while depression was related to sex, diabetes mellitus, obesity, years of education, and trait-anxiety. Obesity and anxiety were considered predictive factors for depression, while depression and fewer years of education were considered predictive factors for anxiety. Copyright © 2017. Published by Elsevier Inc.

Validation of a multifactorial risk factor model used for predicting future caries risk with Nevada adolescents.

PubMed

Ditmyer, Marcia M; Dounis, Georgia; Howard, Katherine M; Mobley, Connie; Cappelli, David

2011-05-20

The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting. This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008). The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural), tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), and prevalence across all six years of screening to assess the validity of the Risk Factor Model. Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%. Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.

Prediction and Informative Risk Factor Selection of Bone Diseases.

PubMed

Li, Hui; Li, Xiaoyi; Ramanathan, Murali; Zhang, Aidong

2015-01-01

With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not only models the progression of the disease, but also predicts the risk of the disease for early disease control or prevention. Existing models for answering these questions usually fall into two categories: the expert knowledge based model or the handcrafted feature set based model. To fully utilize the whole EHR data, we will build a framework to construct an integrated representation of features from all available risk factors in the EHR data and use these integrated features to effectively predict osteoporosis and bone fractures. We will also develop a framework for informative risk factor selection of bone diseases. A pair of models for two contrast cohorts (e.g., diseased patients versus non-diseased patients) will be established to discriminate their characteristics and find the most informative risk factors. Several empirical results on a real bone disease data set show that the proposed framework can successfully predict bone diseases and select informative risk factors that are beneficial and useful to guide clinical decisions.

Predictive factors for complications in children with esophageal atresia and tracheoesophageal fistula.

PubMed

Shah, R; Varjavandi, V; Krishnan, U

2015-04-01

The objective of this study was to describe the incidence of complications in children with esophageal atresia (EA) with or without tracheoesophageal fistula (TEF) at a tertiary pediatric hospital and to identify predictive factors for their occurrence. A retrospective chart review of 110 patients born in or transferred to Sydney Children's Hospital with EA/TEF between January 1999 and December 2010 was done. Univariate and multivariate regression analyses were performed to identify predictive factors for the occurrence of complications in these children. From univariate analysis, early esophageal stricture formation was more likely in children with 'long-gap' EA (odds ratio [OR] = 16.32). Patients with early strictures were more likely to develop chest infections (OR = 3.33). Patients with severe tracheomalacia were more likely to experience 'cyanotic/dying' (OR = 180) and undergo aortopexy (OR = 549). Patients who had gastroesophageal reflux disease were significantly more likely to require fundoplication (OR = 10.83) and undergo aortopexy (OR = 6.417). From multivariate analysis, 'long-gap' EA was a significant predictive factor for late esophageal stricture formation (P = 0.007) and for gastrostomy insertion (P = 0.001). Reflux was a significant predictive factor for requiring fundoplication (P = 0.007) and gastrostomy (P = 0.002). Gastrostomy insertion (P = 0.000) was a significant predictive factor for undergoing fundoplication. Having a prior fundoplication (P = 0.001) was a significant predictive factor for undergoing a subsequent aortopexy. Predictive factors for the occurrence of complications post EA/TEF repair were identified in this large single centre pediatric study. © 2014 International Society for Diseases of the Esophagus.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

[Constitutional factors of resistance to the effects of local vibration].

PubMed

Shalaurov, A V; Shchedrina, A G

1989-01-01

The study directed at the improvement of vibration disease prevention shows that not only functional, but also constitutional and somatotypologic body characteristics should be taken into account in predicting resistance to the impact of local occupational vibration. Such approach is specified by the fact that the relation of the main body components affects the amount of the zone of tissues and organs involved into vibration process. The study of 300 assemblerriveters and metal workers engaged in mechanical assembly shows that workers with elevated content of fat tissue and relatively small amount of osteal and muscular tissue, i.e., representatives of the abdominal somatype, are most resistant to local vibration. When the content of osteal and muscular components increases and that of the fat one decreases (thoracic somatotype), resistance to local vibration experiences a significant decline. The dimensions of skin and fat folds of upper extremities are greatly correlated to local vibration resistance.

Predictive factors for somatization in a trauma sample

PubMed Central

2009-01-01

Background Unexplained somatic symptoms are common among trauma survivors. The relationship between trauma and somatization appears to be mediated by posttraumatic stress disorder (PTSD). However, only few studies have focused on what other psychological risk factors may predispose a trauma victim towards developing somatoform symptoms. Methods The present paper examines the predictive value of PTSD severity, dissociation, negative affectivity, depression, anxiety, and feeling incompetent on somatization in a Danish sample of 169 adult men and women who were affected by a series of explosions in a firework factory settled in a residential area. Results Negative affectivity and feelings of incompetence significantly predicted somatization, explaining 42% of the variance. PTSD was significant until negative affectivity was controlled for. Conclusion Negative affectivity and feelings of incompetence significantly predicted somatization in the trauma sample whereas dissociation, depression, and anxiety were not associated with degree of somatization. PTSD as a risk factor was mediated by negative affectivity. PMID:19126224

PAMPA--critical factors for better predictions of absorption.

PubMed

Avdeef, Alex; Bendels, Stefanie; Di, Li; Faller, Bernard; Kansy, Manfred; Sugano, Kiyohiko; Yamauchi, Yukinori

2007-11-01

PAMPA, log P(OCT), and Caco-2 are useful tools in drug discovery for the prediction of oral absorption, brain penetration and for the development of structure-permeability relationships. Each approach has its advantages and limitations. Selection criteria for methods are based on many different factors: predictability, throughput, cost and personal preferences (people factor). The PAMPA concerns raised by Galinis-Luciani et al. (Galinis-Luciani et al., 2007, J Pharm Sci, this issue) are answered by experienced PAMPA practitioners, inventors and developers from diverse research organizations. Guidelines on how to use PAMPA are discussed. PAMPA and PAMPA-BBB have much better predictivity for oral absorption and brain penetration than log P(OCT) for real-world drug discovery compounds. PAMPA and Caco-2 have similar predictivity for passive oral absorption. However, it is not advisable to use PAMPA to predict absorption involving transporter-mediated processes, such as active uptake or efflux. Measurement of PAMPA is much more rapid and cost effective than Caco-2 and log P(OCT). PAMPA assay conditions are critical in order to generate high quality and relevant data, including permeation time, assay pH, stirring, use of cosolvents and selection of detection techniques. The success of using PAMPA in drug discovery depends on careful data interpretation, use of optimal assay conditions, implementation and integration strategies, and education of users. Copyright 2007 Wiley-Liss, Inc.

Scanning laser Doppler imaging may predict disease progression of localized scleroderma in children and young adults.

PubMed

Shaw, L J; Shipley, J; Newell, E L; Harris, N; Clinch, J G; Lovell, C R

2013-07-01

Localized scleroderma is a rare but potentially disfiguring and disabling condition. Systemic treatment should be started early in those with active disease in key functional and cosmetic sites, but disease activity is difficult to determine clinically. Superficial blood flow has been shown to correlate with disease activity in localized scleroderma. To examine whether superficial blood flow measured by laser Doppler imaging (LDI) has the potential to predict disease progression and therefore select patients for early systemic treatment. A group of 20 individuals had clinical assessment and scanning LDI blood-flow measurements of 32 affected body sites. After a mean follow-up of 8.7 months their clinical outcome was compared with the results of the initial LDI assessment. Eleven out of 15 patients with an assessment of active LDI had progressed clinically, and 16 out of the 17 scans with inactive LDI assessment had not progressed, giving a positive predictive value of 73% and a negative predictive value of 94%. We believe that LDI can be a useful tool in predicting disease progression in localized scleroderma, and it may help clinicians to decide which patients to treat early. © 2013 The Authors BJD © 2013 British Association of Dermatologists.

[Predictive factors of virological response in chronically HCV infected].

PubMed

Lapiński, Tadeusz Wojciech; Flisiak, Robert

2012-09-01

Research on new antivirals drugs applied in the treatment of chronically HCV infected indicate that even the most perfect therapeutic molecules do not guarantee 100% efficacy. Since the beginning of the history of HCV infection treatment clinicians looked for predictors of treatment efficacy. Numerous studies confirm the high probability of cure in patients who cleared HCVinfectional 4 and 12 weeks of therapy. However despite of viral factors, recent research demonstrated predictive role of some host dependent factors. The most important role seems to play genetic factors including polymorphism rs12979860, as well as chemokins including first of all CXCL10 (IP-10). Very interesting seems to be also results of studies on association between vitamine D concentration and treatment efficacy. However in the future the most important predictive factor remain probably early on-treatment viral response.

[Predictive factors of complications during CT-guided transthoracic biopsy].

PubMed

Fontaine-Delaruelle, C; Souquet, P-J; Gamondes, D; Pradat, E; de Leusse, A; Ferretti, G R; Couraud, S

2017-04-01

CT-guided transthoracic core-needle biopsy (TTNB) is frequently used for the diagnosis of lung nodules. The aim of this study is to describe TTNBs' complications and to investigate predictive factors of complications. All consecutive TTNBs performed in three centers between 2006 and 2012 were included. Binary logistic regression was used for multivariate analysis. Overall, 970 TTNBs were performed in 929 patients. The complication rate was 34% (life-threatening complication in 6%). The most frequent complications were pneumothorax (29% included 4% which required chest-tube) and hemoptysis (5%). The mortality rate was 0.1% (n=1). In multivariate analysis, predictive factor for a complication was small target size (AOR=0.984; 95% CI [0.976-0.992]; P<0.001). This predictive factor was also found for occurrence of life-threatening complication (AOR=0.982; [0.965-0.999]; P=0.037), of pneumothorax (AOR=0.987; [0.978-0.995]; P=0.002) and of hemoptysis (AOR=0.973; [0.951-0.997]; P=0.024). One complication occurred in one-third of TTNBs. The proportion of life-threatening complication was 6%. A small lesion size was predictive of complication occurrence. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Long Term Mean Local Time of the Ascending Node Prediction

NASA Technical Reports Server (NTRS)

McKinley, David P.

2007-01-01

Significant error has been observed in the long term prediction of the Mean Local Time of the Ascending Node on the Aqua spacecraft. This error of approximately 90 seconds over a two year prediction is a complication in planning and timing of maneuvers for all members of the Earth Observing System Afternoon Constellation, which use Aqua's MLTAN as the reference for their inclination maneuvers. It was determined that the source of the prediction error was the lack of a solid Earth tide model in the operational force models. The Love Model of the solid Earth tide potential was used to derive analytic corrections to the inclination and right ascension of the ascending node of Aqua's Sun-synchronous orbit. Additionally, it was determined that the resonance between the Sun and orbit plane of the Sun-synchronous orbit is the primary driver of this error. The analytic corrections have been added to the operational force models for the Aqua spacecraft reducing the two-year 90-second error to less than 7 seconds.

Predictive Modeling of Risk Factors and Complications of Cataract Surgery

PubMed Central

Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S; Shah, Nigam H

2016-01-01

Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. The predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements. PMID:26692059

Prediction of STN-DBS Electrode Implantation Track in Parkinson's Disease by Using Local Field Potentials

PubMed Central

Telkes, Ilknur; Jimenez-Shahed, Joohi; Viswanathan, Ashwin; Abosch, Aviva; Ince, Nuri F.

2016-01-01

Optimal electrophysiological placement of the DBS electrode may lead to better long term clinical outcomes. Inter-subject anatomical variability and limitations in stereotaxic neuroimaging increase the complexity of physiological mapping performed in the operating room. Microelectrode single unit neuronal recording remains the most common intraoperative mapping technique, but requires significant expertise and is fraught by potential technical difficulties including robust measurement of the signal. In contrast, local field potentials (LFPs), owing to their oscillatory and robust nature and being more correlated with the disease symptoms, can overcome these technical issues. Therefore, we hypothesized that multiple spectral features extracted from microelectrode-recorded LFPs could be used to automate the identification of the optimal track and the STN localization. In this regard, we recorded LFPs from microelectrodes in three tracks from 22 patients during DBS electrode implantation surgery at different depths and aimed to predict the track selected by the neurosurgeon based on the interpretation of single unit recordings. A least mean square (LMS) algorithm was used to de-correlate LFPs in each track, in order to remove common activity between channels and increase their spatial specificity. Subband power in the beta band (11–32 Hz) and high frequency range (200–450 Hz) were extracted from the de-correlated LFP data and used as features. A linear discriminant analysis (LDA) method was applied both for the localization of the dorsal border of STN and the prediction of the optimal track. By fusing the information from these low and high frequency bands, the dorsal border of STN was localized with a root mean square (RMS) error of 1.22 mm. The prediction accuracy for the optimal track was 80%. Individual beta band (11–32 Hz) and the range of high frequency oscillations (200–450 Hz) provided prediction accuracies of 72 and 68% respectively. The best

Prediction of margin involvement and local recurrence after skin-sparing and simple mastectomy.

PubMed

Al-Himdani, S; Timbrell, S; Tan, K T; Morris, J; Bundred, N J

2016-07-01

Skin-sparing mastectomy (SSM) facilitates immediate breast reconstruction. We investigated locoregional recurrence rates after SSM compared with simple mastectomy and the factors predicting oncological failure. Patients with early breast cancer that underwent mastectomy between 2000 and 2005 at a single institution were studied to ascertain local and systemic recurrence rates between groups. Kaplan-Meier curves and log-rank test were used to evaluate disease-free survival. Patients (n = 577) underwent simple mastectomy (80%) or SSM (20%). Median follow up was 80 months. Patients undergoing SSM were of younger average age, less often had involved lymph nodes (22% vs 44%, p < 0.001), more often had DCIS present (79% vs 53%, p < 0.001) and involved margins (29% vs 15%, p = 0.001). Involved surgical margins were associated with large size (p = 0.001). The 8-year local recurrence (LR) rates were 7.9% for SSM and 5% for simple mastectomy respectively (p = 0.35). Predictors of locoregional recurrence were lymph node involvement (HR 8.0, for >4 nodes, p < 0.001) and involved surgical margins (HR 3.3, p = 0.002). In node negative patients, SSM was a predictor of locoregional recurrence (HR 4.8 [1.1, 19.9], p = 0.033). Delayed reconstruction is more appropriate for node positive early breast cancer after post-mastectomy radiotherapy. Re-excision of involved margins is essential to prevent local recurrence after mastectomy. Copyright © 2016 Elsevier Ltd, BASO ~ the Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Meta-analysis of the predictive factors of postpartum fatigue.

PubMed

Badr, Hanan A; Zauszniewski, Jaclene A

2017-08-01

Nearly 64% of new mothers are affected by fatigue during the postpartum period, making it the most common problem that a woman faces as she adapts to motherhood. Postpartum fatigue can lead to serious negative effects on the mother's health and the newborn's development and interfere with mother-infant interaction. The aim of this meta-analysis was to identify predictive factors of postpartum fatigue and to document the magnitude of their effects using effect sizes. We used two search engines, PubMed and Google Scholar, to identify studies that met three inclusion criteria: (a) the article was written in English, (b) the article studied the predictive factors of postpartum fatigue, and (c) the article included information about the validity and reliability of the instruments used in the research. Nine articles met these inclusion criteria. The direction and strength of correlation coefficients between predictive factors and postpartum fatigue were examined across the studies to determine their effect sizes. Measurement of predictor variables occurred from 3days to 6months postpartum. Correlations reported between predictive factors and postpartum fatigue were as follows: small effect size (r range =0.10 to 0.29) for education level, age, postpartum hemorrhage, infection, and child care difficulties; medium effect size (r range =0.30 to 0.49) for physiological illness, low ferritin level, low hemoglobin level, sleeping problems, stress and anxiety, and breastfeeding problems; and large effect size (r range =0.50+) for depression. Postpartum fatigue is a common condition that can lead to serious health problems for a new mother and her newborn. Therefore, increased knowledge concerning factors that influence the onset of postpartum fatigue is needed for early identification of new mothers who may be at risk. Appropriate treatments, interventions, information, and support can then be initiated to prevent or minimize the postpartum fatigue. Copyright © 2017 Elsevier

WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

PubMed

Chi, Sang-Mun; Nam, Dougu

2012-04-01

We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

Review of image defined risk factors in localized neuroblastoma patients: Results of the GPOH NB97 trial.

PubMed

Simon, Thorsten; Hero, Barbara; Benz-Bohm, Gabriele; von Schweinitz, Dietrich; Berthold, Frank

2008-05-01

Recently, an international expert group proposed revision of the International Neuroblastoma Staging System (INSS). Localized disease can be classified as L1 without and as L2 with image defined risk factors (IDRF published in JCO 2005; 23:8483-8489). Our aim was to evaluate IDRF for the prediction of resectability, complications, and outcome. Records of 520 localized neuroblastoma patients of the NB97 trial were reviewed. Patients were retrospectively classified as having IDRF or not. A total of 366 evaluable patients were then analyzed for extent and complications of surgery and the prognostic value of IDRF. Any IDRF was present in 26/160 of stage 1, 49/113 of stage 2, and 64/93 of stage 3 patients. Complete primary resection was achieved in 156/227 patients without IDRF and 43/139 patients with IDRF (P < 0.001). The frequency of complications was higher if any IDRF was present: 37/139 versus 33/227 (P = 0.006). Lack of IDRF was associated with better event free survival (3-year-EFS 86 +/- 2% vs. 75 +/- 4%, P = 0.010), whereas overall survival was similar (3-year-OS 98 +/- 1% vs. 96 +/- 2%, P = 0.462). EFS clearly depended on INSS stage (3-year-EFS 93 +/- 2% in stage 1, 78 +/- 4% in stage 2, and 69 +/- 5% in stage 3, P < 0.001). OS was not different (3-year-OS 98 +/- 1% vs. 99 +/- 1% vs. 94 +/- 2%, P = 0.056). Multivariate analysis demonstrated an impact of INSS stage on EFS only. IDRF were not shown to be significant for predicting EFS or OS. IDRF were useful in predicting risk and completeness of operation. IDRF failed as independent risk predictors in localized neuroblastoma. INSS more precisely identified patients with poor prognosis. (c) 2008 Wiley-Liss, Inc.

Climate and local abundance in freshwater fishes

PubMed Central

Knouft, Jason H.; Anthony, Melissa M.

2016-01-01

Identifying factors regulating variation in numbers of individuals among populations across a species' distribution is a fundamental goal in ecology. A common prediction, often referred to as the abundant-centre hypothesis, suggests that abundance is highest near the centre of a species' range. However, because of the primary focus on the geographical position of a population, this framework provides little insight into the environmental factors regulating local abundance. While range-wide variation in population abundance associated with environmental conditions has been investigated in terrestrial species, the relationship between climate and local abundance in freshwater taxa across species' distributions is not well understood. We used GIS-based temperature and precipitation data to determine the relationships between climatic conditions and range-wide variation in local abundance for 19 species of North American freshwater fishes. Climate predicted a portion of the variation in local abundance among populations for 18 species. In addition, the relationship between climatic conditions and local abundance varied among species, which is expected as lineages partition the environment across geographical space. The influence of local habitat quality on species persistence is well documented; however, our results also indicate the importance of climate in regulating population sizes across a species geographical range, even in aquatic taxa. PMID:27429769

Factors Predicting Post-High School Employment for Young Adults with Visual Impairments

ERIC Educational Resources Information Center

McDonnall, Michele Capella

2010-01-01

Although low levels of employment among transition-age youth with visual impairments (VI) have long been a concern, empirical research in this area is very limited. The purpose of this study was to identify factors that predict future employment for this population and to compare these factors to the factors that predict employment for the general…

Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction

PubMed Central

Ashworth, Justin; Plaisier, Christopher L.; Lo, Fang Yin; Reiss, David J.; Baliga, Nitin S.

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer. PMID:25255272

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

PubMed

Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

Prediction of compensatory hyperhidrosis with botulinum toxin A and local anesthetic.

PubMed

Jeong, Jin Yong; Park, Soo Seog; Sim, Sung Bo; Jo, Keon Hyon; Lee, Jongho; Oh, Saecheol; Shin, Jae Seong

2015-08-01

Compensatory hyperhidrosis (CH) is one of the most problematic complications of sympathectomy, which occurs often and is hard to treat. A predictive procedure (PP) for CH can help patients experience compensatory sweating before sympathectomy to determine whether or not to perform sympathectomy. Our study aimed to evaluate the CH after the PP and sympathectomy in patients with primary palmar hyperhidrosis using multiple drugs. We reviewed 83 patients who underwent a PP between July 2009 and August 2013 with primary palmar hyperhidrosis. In group A, we used levobupivacaine (n = 39). In group B, we used botulinum toxin A plus ropivacaine for the PP in group B (n = 44). The CH rate after the PP was 44 % (group A) and 25 % (group B), and after sympathectomy 80 % (group A) and 75 % (group B). The prediction value between the PP and the sympathectomy was statistically significant in group A (p < 0.05). The positive prediction rate was 73 % and the negative prediction rate was 27 % in group A. Local anesthetic alone has a better predictive value. From our finding, patients should be made aware that CH after sympathectomy is less severe in 73 % of cases than that experienced in the PP.

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Enhancing membrane protein subcellular localization prediction by parallel fusion of multi-view features.

PubMed

Yu, Dongjun; Wu, Xiaowei; Shen, Hongbin; Yang, Jian; Tang, Zhenmin; Qi, Yong; Yang, Jingyu

2012-12-01

Membrane proteins are encoded by ~ 30% in the genome and function importantly in the living organisms. Previous studies have revealed that membrane proteins' structures and functions show obvious cell organelle-specific properties. Hence, it is highly desired to predict membrane protein's subcellular location from the primary sequence considering the extreme difficulties of membrane protein wet-lab studies. Although many models have been developed for predicting protein subcellular locations, only a few are specific to membrane proteins. Existing prediction approaches were constructed based on statistical machine learning algorithms with serial combination of multi-view features, i.e., different feature vectors are simply serially combined to form a super feature vector. However, such simple combination of features will simultaneously increase the information redundancy that could, in turn, deteriorate the final prediction accuracy. That's why it was often found that prediction success rates in the serial super space were even lower than those in a single-view space. The purpose of this paper is investigation of a proper method for fusing multiple multi-view protein sequential features for subcellular location predictions. Instead of serial strategy, we propose a novel parallel framework for fusing multiple membrane protein multi-view attributes that will represent protein samples in complex spaces. We also proposed generalized principle component analysis (GPCA) for feature reduction purpose in the complex geometry. All the experimental results through different machine learning algorithms on benchmark membrane protein subcellular localization datasets demonstrate that the newly proposed parallel strategy outperforms the traditional serial approach. We also demonstrate the efficacy of the parallel strategy on a soluble protein subcellular localization dataset indicating the parallel technique is flexible to suite for other computational biology problems. The

Using Emotional and Social Factors To Predict Student Success.

ERIC Educational Resources Information Center

Pritchard, Mary E.; Wilson, Gregory S.

2003-01-01

College academic success and retention have traditionally been predicted using demographic and academic variables. This study moved beyond traditional predictors. A survey of 218 undergraduate students revealed that emotional and social factors (e.g., stress, frequency of alcohol consumption) related to GPA and emotional factors (e.g.,…

Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics.

PubMed

Zhao, Meng; Ding, Baocang

2015-03-01

This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

PubMed

Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

2015-05-01

Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial. PMID:23563395

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

PubMed

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

Predicting Gender-Role Attitudes in Adolescent Females: Ability, Agency, and Parental Factors.

ERIC Educational Resources Information Center

Ahrens, Julia A.; O'Brien, Karen M.

1996-01-01

Investigated the contribution of ability, agency, and parental factors to the prediction of gender-role attitudes of 409 adolescent females in a private, college-preparatory high school. Findings indicate that ability and agency were predictive of gender-role attitudes, whereas parental factors were not significant contributors. Recommendations…

Support vector machine-based prediction of local tumor control after stereotactic body radiation therapy for early-stage non-small cell lung cancer.

PubMed

Klement, Rainer J; Allgäuer, Michael; Appold, Steffen; Dieckmann, Karin; Ernst, Iris; Ganswindt, Ute; Holy, Richard; Nestle, Ursula; Nevinny-Stickel, Meinhard; Semrau, Sabine; Sterzing, Florian; Wittig, Andrea; Andratschke, Nicolaus; Guckenberger, Matthias

2014-03-01

Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control. We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection. Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED(ISO)) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED(ISO) and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED(ISO), age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively. These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are expected through a nonlinear combination of multiple features

Prediction of global and local model quality in CASP8 using the ModFOLD server.

PubMed

McGuffin, Liam J

2009-01-01

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0--an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/. Copyright 2009 Wiley-Liss, Inc.

Identification of novel nuclear localization signals of Drosophila myeloid leukemia factor.

PubMed

Sugano, Wakana; Yamaguchi, Masamitsu

2007-01-01

Myeloid leukemia factor 1 (MLF1) was first identified as part of a leukemic fusion protein produced by a chromosomal translocation, and MLF family proteins are present in many animals. In mammalian cells, MLF1 has been described as mainly cytoplasmic, but in Drosophila, one of the dMLF isoforms (dMLFA) localized mainly in the nucleus while the other isoform (dMLFB), that appears to be produced by the alternative splicing, displays both nuclear and cytoplasmic localization. To investigate the difference in subcellular localization between MLF family members, we examined the subcellular localization of deletion mutants of dMLFA isoform. The analyses showed that the C-terminal 40 amino acid region of dMLFA is necessary and sufficient for nuclear localization. Based on amino acid sequences, we hypothesized that two nuclear localization signals (NLSs) are present within the region. Site-directed mutagenesis of critical residues within the two putative NLSs leads to loss of nuclear localization, suggesting that both NLS motifs are necessary for nuclear localization.

Psychological factors that predict reaction to abortion.

PubMed

Moseley, D T; Follingstad, D R; Harley, H; Heckel, R V

1981-04-01

Investigated demographic and psychological factors related to positive or negative reactions to legal abortions performed during the first trimester of pregnancy in 62 females in an urban southern community. Results suggest that the social context and the degree of support from a series of significant persons rather than demographic variables were most predictive of a positive reaction.

Factors that predict adolescent motivation for substance abuse treatment.

PubMed

Battjes, Robert J; Gordon, Michael S; O'Grady, Kevin E; Kinlock, Timothy W; Carswell, Melissa A

2003-04-01

Many adolescent substance abusers enter treatment because of external pressures and thus lack motivation to change their behavior and engage in treatment. Because an understanding of adolescent motivation may contribute to improved treatment, an investigation of factors that predict motivation was undertaken with youth admitted to an adolescent outpatient substance abuse treatment program (N=196). At admission, these subjects received a comprehensive biopsychosocial assessment. Using multiple regression analysis, factors considered to potentially predict motivation were assessed. Of the factors examined, those that involved experiencing various negative consequences of substance use emerged as important predictors of motivation, whereas severity of substance use did not. Diminished awareness of negative consequences of use was consonant with lower motivation, suggesting the importance of interventions to help youth recognize negative consequences of their substance use. Interventions to enhance motivation are likely to become more important as the juvenile justice system increasingly refers troubled youth to treatment.

Shoulder dystocia: risk factors, predictability, and preventability.

PubMed

Mehta, Shobha H; Sokol, Robert J

2014-06-01

Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

Local temperatures predict breeding phenology but do not result in breeding synchrony among a community of resident cavity-nesting birds.

PubMed

Drake, Anna; Martin, Kathy

2018-02-09

Weather and ecological factors are known to influence breeding phenology and thus individual fitness. We predicted concordance between weather conditions and annual variation in phenology within a community of eight resident, cavity-nesting bird species over a 17-year period. We show that, although clutch initiation dates for six of our eight species are correlated with local daily maximum temperatures, this common driver does not produce a high degree of breeding synchrony due to species-specific responses to conditions during different periods of the preceding winter or spring. These "critical temperature periods" were positively associated with average lay date for each species, although the interval between critical periods and clutch initiation varied from 4-78 days. The ecological factors we examined (cavity availability and a food pulse) had an additional influence on timing in only one of our eight focal species. Our results have strong implications for understanding heterogeneous wildlife responses to climate change: divergent responses would be expected within communities where species respond to local conditions within different temporal windows, due to differing warming trends between winter and spring. Our system therefore indicates that climate change could alter relative breeding phenology among sympatric species in temperate ecosystems.

Computational prediction of strain-dependent diffusion of transcription factors through the cell nucleus.

PubMed

Nava, Michele M; Fedele, Roberto; Raimondi, Manuela T

2016-08-01

Nuclear spreading plays a crucial role in stem cell fate determination. In previous works, we reported evidence of multipotency maintenance for mesenchymal stromal cells cultured on three-dimensional engineered niche substrates, fabricated via two-photon laser polymerization. We correlated maintenance of multipotency to a more roundish morphology of these cells with respect to those cultured on conventional flat substrates. To interpret these findings, here we present a multiphysics model coupling nuclear strains induced by cell adhesion to passive diffusion across the cell nucleus. Fully three-dimensional reconstructions of cultured cells were developed on the basis of confocal images: in particular, the level of nuclear spreading resulted significantly dependent on the cell localization within the niche architecture. We assumed that the cell diffusivity varies as a function of the local volumetric strain. The model predictions indicate that the higher the level of spreading of the cell, the higher the flux across the nucleus of small solutes such as transcription factors. Our results point toward nuclear spreading as a primary mechanism by which the stem cell translates its shape into a fate decision, i.e., by amplifying the diffusive flow of transcriptional activators into the nucleus.

Predictive Success Factors in Selective Upper Airway Stimulation.

PubMed

Heiser, Clemens; Hofauer, Benedikt

2017-01-01

Obstructive sleep apnea is one of the most common diseases in Western industrialized countries. A variety of conservative and surgical treatment options are available for its treatment. In recent years, selective upper airway stimulation (sUAS) has been shown to be effective and safe. Different biomarkers have been investigated as predictive clinical success factors in a number of clinical trials. Age does not matter in sUAS, as compared to its predictive role in other therapies. Weight seems to play a limited role, depending on drug-induced sleep endoscopy to rule out a complete concentric collapse with an increased body mass index. For surgical success and the related postoperative tongue motions, a nerve integrity monitoring methodology has been developed for predicting correct cuff placement. Postoperative sonography remains a promising method for the future assessment of predictive markers in sUAS. © 2017 S. Karger AG, Basel.

Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

PubMed Central

Sang, Shaowei; Yin, Wenwu; Bi, Peng; Zhang, Honglong; Wang, Chenggang; Liu, Xiaobo; Chen, Bin; Yang, Weizhong; Liu, Qiyong

2014-01-01

Introduction Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. Methodology and Principal Findings Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

PubMed

Sang, Shaowei; Yin, Wenwu; Bi, Peng; Zhang, Honglong; Wang, Chenggang; Liu, Xiaobo; Chen, Bin; Yang, Weizhong; Liu, Qiyong

2014-01-01

Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue's control and prevention purpose. Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Imported DF cases and mosquito density play a

Developing Local Oral Reading Fluency Cut Scores for Predicting High-Stakes Test Performance

ERIC Educational Resources Information Center

Grapin, Sally L.; Kranzler, John H.; Waldron, Nancy; Joyce-Beaulieu, Diana; Algina, James

2017-01-01

This study evaluated the classification accuracy of a second grade oral reading fluency curriculum-based measure (R-CBM) in predicting third grade state test performance. It also compared the long-term classification accuracy of local and publisher-recommended R-CBM cut scores. Participants were 266 students who were divided into a calibration…

Factors predicting survival following noninvasive ventilation in amyotrophic lateral sclerosis.

PubMed

Peysson, S; Vandenberghe, N; Philit, F; Vial, C; Petitjean, T; Bouhour, F; Bayle, J Y; Broussolle, E

2008-01-01

The involvement of respiratory muscles is a major predicting factor for survival in amyotrophic lateral sclerosis (ALS). Recent studies show that noninvasive ventilation (NIV) can relieve symptoms of alveolar hypoventilation. However, factors predicting survival in ALS patients when treated with NIV need to be clarified. We conducted a retrospective study of 33 consecutive ALS patients receiving NIV. Ten patients had bulbar onset. We determined the median survivals from onset, diagnosis and initiation of NIV and factors predicting survival. Statistical analysis was performed using the Kaplan-Meier test and Cox proportional hazard models. The median initial and maximal total uses of NIV were 10 and 14 h/24h. The overall median survival from ALS onset was 34.2 months and worsened with increasing age and bulbar onset of the disease. The median survival from initiation of NIV was 8.4 months and was significantly poorer in patients with advanced age or with airway mucus accumulation. Survival from initiation of NIV was not influenced by respiratory parameters or bulbar symptoms. Advanced age at diagnosis and airway mucus accumulation represent poorer prognostic factors of ALS patients treated with NIV. NIV is a helpful treatment of sleep-disordered breathing, including patients with bulbar involvement. Copyright 2008 S. Karger AG, Basel.

Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response.

PubMed

Cho, Seung Hyun; Kim, Gab Chul; Jang, Yun-Jin; Ryeom, Hunkyu; Kim, Hye Jung; Shin, Kyung-Min; Park, Jun Seok; Choi, Gyu-Seog; Kim, See Hyung

2015-09-01

The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC. © The Foundation Acta Radiologica 2014.

Comparisons of predicted steady-state levels in rooms with extended- and local-reaction bounding surfaces

NASA Astrophysics Data System (ADS)

Hodgson, Murray; Wareing, Andrew

2008-01-01

A combined beam-tracing and transfer-matrix model for predicting steady-state sound-pressure levels in rooms with multilayer bounding surfaces was used to compare the effect of extended- and local-reaction surfaces, and the accuracy of the local-reaction approximation. Three rooms—an office, a corridor and a workshop—with one or more multilayer test surfaces were considered. The test surfaces were a single-glass panel, a double-drywall panel, a carpeted floor, a suspended-acoustical ceiling, a double-steel panel, and glass fibre on a hard backing. Each test surface was modeled as of extended or of local reaction. Sound-pressure levels were predicted and compared to determine the significance of the surface-reaction assumption. The main conclusions were that the difference between modeling a room surface as of extended or of local reaction is not significant when the surface is a single plate or a single layer of material (solid or porous) with a hard backing. The difference is significant when the surface consists of multilayers of solid or porous material and includes a layer of fluid with a large thickness relative to the other layers. The results are partially explained by considering the surface-reflection coefficients at the first-reflection angles.

Rupture Predictions of Notched Ti-6Al-4V Using Local Approaches

PubMed Central

Peron, Mirco; Berto, Filippo

2018-01-01

Ti-6Al-4V has been extensively used in structural applications in various engineering fields, from naval to automotive and from aerospace to biomedical. Structural applications are characterized by geometrical discontinuities such as notches, which are widely known to harmfully affect their tensile strength. In recent years, many attempts have been done to define solid criteria with which to reliably predict the tensile strength of materials. Among these criteria, two local approaches are worth mentioning due to the accuracy of their predictions, i.e., the strain energy density (SED) approach and the theory of critical distance (TCD) method. In this manuscript, the robustness of these two methods in predicting the tensile behavior of notched Ti-6Al-4V specimens has been compared. To this aim, two very dissimilar notch geometries have been tested, i.e., semi-circular and blunt V-notch with a notch root radius equal to 1 mm, and the experimental results have been compared with those predicted by the two models. The experimental values have been estimated with low discrepancies by either the SED approach and the TCD method, but the former results in better predictions. The deviations for the SED are in fact lower than 1.3%, while the TCD provides predictions with errors almost up to 8.5%. Finally, the weaknesses and the strengths of the two models have been reported. PMID:29693565

The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction.

PubMed

Salvatore, M; Shu, N; Elofsson, A

2018-01-01

SubCons is a recently developed method that predicts the subcellular localization of a protein. It combines predictions from four predictors using a Random Forest classifier. Here, we present the user-friendly web-interface implementation of SubCons. Starting from a protein sequence, the server rapidly predicts the subcellular localizations of an individual protein. In addition, the server accepts the submission of sets of proteins either by uploading the files or programmatically by using command line WSDL API scripts. This makes SubCons ideal for proteome wide analyses allowing the user to scan a whole proteome in few days. From the web page, it is also possible to download precalculated predictions for several eukaryotic organisms. To evaluate the performance of SubCons we present a benchmark of LocTree3 and SubCons using two recent mass-spectrometry based datasets of mouse and drosophila proteins. The server is available at http://subcons.bioinfo.se/. © 2017 The Protein Society.

Vasodilator factors in the systemic and local adaptations to pregnancy

PubMed Central

Valdes, Gloria; Kaufmann, Peter; Corthorn, Jenny; Erices, Rafaela; Brosnihan, K Bridget; Joyner-Grantham, JaNae

2009-01-01

We postulate that an orchestrated network composed of various vasodilatory systems participates in the systemic and local hemodynamic adaptations in pregnancy. The temporal patterns of increase in the circulating and urinary levels of five vasodilator factors/systems, prostacyclin, nitric oxide, kallikrein, angiotensin-(1–7) and VEGF, in normal pregnant women and animals, as well as the changes observed in preeclamptic pregnancies support their functional role in maintaining normotension by opposing the vasoconstrictor systems. In addition, the expression of these vasodilators in the different trophoblastic subtypes in various species supports their role in the transformation of the uterine arteries. Moreover, their expression in the fetal endothelium and in the syncytiotrophoblast in humans, rats and guinea-pigs, favour their participation in maintaining the uteroplacental circulation. The findings that sustain the functional associations of the various vasodilators, and their participation by endocrine, paracrine and autocrine regulation of the systemic and local vasoactive changes of pregnancy are abundant and compelling. However, further elucidation of the role of the various players is hampered by methodological problems. Among these difficulties is the complexity of the interactions between the different factors, the likelihood that experimental alterations induced in one system may be compensated by the other players of the network, and the possibility that data obtained by manipulating single factors in vitro or in animal studies may be difficult to translate to the human. In addition, the impossibility of sampling the uteroplacental interface along normal pregnancy precludes obtaining longitudinal profiles of the various players. Nevertheless, the possibility of improving maternal blood pressure regulation, trophoblast invasion and uteroplacental flow by enhancing vasodilation (e.g. L-arginine, NO donors, VEGF transfection) deserves unravelling the

Strong expectations cancel locality effects: evidence from Hindi.

PubMed

Husain, Samar; Vasishth, Shravan; Srinivasan, Narayanan

2014-01-01

Expectation-driven facilitation (Hale, 2001; Levy, 2008) and locality-driven retrieval difficulty (Gibson, 1998, 2000; Lewis & Vasishth, 2005) are widely recognized to be two critical factors in incremental sentence processing; there is accumulating evidence that both can influence processing difficulty. However, it is unclear whether and how expectations and memory interact. We first confirm a key prediction of the expectation account: a Hindi self-paced reading study shows that when an expectation for an upcoming part of speech is dashed, building a rarer structure consumes more processing time than building a less rare structure. This is a strong validation of the expectation-based account. In a second study, we show that when expectation is strong, i.e., when a particular verb is predicted, strong facilitation effects are seen when the appearance of the verb is delayed; however, when expectation is weak, i.e., when only the part of speech "verb" is predicted but a particular verb is not predicted, the facilitation disappears and a tendency towards a locality effect is seen. The interaction seen between expectation strength and distance shows that strong expectations cancel locality effects, and that weak expectations allow locality effects to emerge.

Strong Expectations Cancel Locality Effects: Evidence from Hindi

PubMed Central

Husain, Samar; Vasishth, Shravan; Srinivasan, Narayanan

2014-01-01

Expectation-driven facilitation (Hale, 2001; Levy, 2008) and locality-driven retrieval difficulty (Gibson, 1998, 2000; Lewis & Vasishth, 2005) are widely recognized to be two critical factors in incremental sentence processing; there is accumulating evidence that both can influence processing difficulty. However, it is unclear whether and how expectations and memory interact. We first confirm a key prediction of the expectation account: a Hindi self-paced reading study shows that when an expectation for an upcoming part of speech is dashed, building a rarer structure consumes more processing time than building a less rare structure. This is a strong validation of the expectation-based account. In a second study, we show that when expectation is strong, i.e., when a particular verb is predicted, strong facilitation effects are seen when the appearance of the verb is delayed; however, when expectation is weak, i.e., when only the part of speech “verb” is predicted but a particular verb is not predicted, the facilitation disappears and a tendency towards a locality effect is seen. The interaction seen between expectation strength and distance shows that strong expectations cancel locality effects, and that weak expectations allow locality effects to emerge. PMID:25010700

Factors predicting labor induction success: a critical analysis.

PubMed

Crane, Joan M G

2006-09-01

Because of the risk of failed induction of labor, a variety of maternal and fetal factors as well as screening tests have been suggested to predict labor induction success. Certain characteristics of the woman (including parity, age, weight, height and body mass index), and of the fetus (including birth weight and gestational age) are associated with the success of labor induction; with parous, young women who are taller and lower weight having a higher rate of induction success. Fetuses with a lower birth weight or increased gestational age are also associated with increased induction success. The condition of the cervix at the start of induction is an important predictor, with the modified Bishop score being a widely used scoring system. The most important element of the Bishop score is dilatation. Other predictors, including transvaginal ultrasound (TVUS) and biochemical markers [including fetal fibronectin (fFN)] have been suggested. Meta-analyses of studies identified from MEDLINE, PubMed, and EMBASE and published from 1990 to October 2005 were performed evaluating the use of TVUS and fFN in predicting labor induction success in women at term with singleton gestations. Both TVUS and Bishop score predicted successful induction [likelihood ratio (LR)=1.82, 95% confidence interval (CI)=1.51-2.20 and LR=2.10, 95%CI=1.67-2.64, respectively]. As well, fFN and Bishop score predicted successful induction (LR=1.49, 95%CI=1.20-1.85, and LR=2.62, 95%CI=1.88-3.64, respectively). Although TVUS and fFN predicted successful labor induction, neither has been shown to be superior to Bishop score. Further research is needed to evaluate these potential predictors and insulin-like growth factor binding protein-1 (IGFBP-1), another potential biochemical marker.

Carbide factor predicts rolling-element bearing fatigue life

NASA Technical Reports Server (NTRS)

Chevalier, J. L.; Zaretsky, E. V.

1973-01-01

Analysis was made to determine correlation between number and size of carbide particles and rolling-element fatigue. Correlation was established, and carbide factor was derived that can be used to predict fatigue life more effectively than such variables as heat treatment, chemical composition, and hardening mechanism.

R&D Project Plan: Development of a State-Local-Tribal Emission Factors Compendium

EPA Pesticide Factsheets

Development of a compendium of emission factors that will support state, local and tribal authorities (SLTs) and other relevant stakeholders that utilize and are in need of emission factor information.

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

PubMed Central

Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

2016-01-01

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348

Predictive factors of dropout from inpatient treatment for anorexia nervosa.

PubMed

Roux, H; Ali, A; Lambert, S; Radon, L; Huas, C; Curt, F; Berthoz, S; Godart, Nathalie

2016-09-30

Patients with severe Anorexia Nervosa (AN) whose condition is life-threatening or who are not receiving adequate ambulatory care are hospitalized. However, 40 % of these patients leave the hospital prematurely, without reaching the target weight set in the treatment plan, and this can compromise outcome. This study set out to explore factors predictive of dropout from hospital treatment among patients with AN, in the hope of identifying relevant therapeutic targets. From 2009 to 2011, 180 women hospitalized for AN (DSM-IV diagnosis) in 10 centres across France were divided into two groups: those under 18 years (when the decision to discharge belongs to the parents) and those aged 18 years and over (when the patient can legally decide to leave the hospital). Both groups underwent clinical assessment using the Morgan & Russell Global Outcome State questionnaire and the Eating Disorders Examination Questionnaire (EDE-Q) for assessment of eating disorder symptoms and outcome. Psychological aspects were assessed via the evaluation of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS). Socio-demographic data were also collected. A number of factors identified in previous research as predictive of dropout from hospital treatment were tested using stepwise descending Cox regressions. We found that factors predictive of dropout varied according to age groups (being under 18 as opposed to 18 and over). For participants under 18, predictive factors were living in a single-parent family, severe intake restriction as measured on the "dietary restriction" subscale of the Morgan & Russell scale, and a low patient-reported score on the EDE-Q "restraint concerns" subscale. For those over 18, dropout was predicted from a low depression score on the HADS, low level of concern about weight on the EDE-Q subscale, and lower educational status. To prevent dropout from hospitalization for AN, the appropriate therapeutic measures vary according to whether

Predictors and Patterns of Local, Regional, and Distant Failure in Squamous Cell Carcinoma of the Vulva.

PubMed

Bogani, Giorgio; Cromi, Antonella; Serati, Maurizio; Uccella, Stefano; Donato, Violante Di; Casarin, Jvan; Naro, Edoardo Di; Ghezzi, Fabio

2017-06-01

To identify factors predicting for recurrence in vulvar cancer patients undergoing surgical treatment. We retrospectively evaluated data of consecutive patients with squamous cell vulvar cancer treated between January 1, 1990 and December 31, 2013. Basic descriptive statistics and multivariable analysis were used to design predicting models influencing outcomes. Five-year disease-free survival (DFS) and overall survival (OS) were analyzed using the Cox model. The study included 101 patients affected by vulvar cancer: 64 (63%) stage I, 12 (12%) stage II, 20 (20%) stage III, and 5 (5%) stage IV. After a mean (SD) follow-up of 37.6 (22.1) months, 21 (21%) recurrences occurred. Local, regional, and distant failures were recorded in 14 (14%), 6 (6%), and 3 (3%) patients, respectively. Five-year DFS and OS were 77% and 82%, respectively. At multivariate analysis only stromal invasion >2 mm (hazard ratio: 4.9 [95% confidence interval, 1.17-21.1]; P=0.04) and extracapsular lymph node involvement (hazard ratio: 9.0 (95% confidence interval, 1.17-69.5); P=0.03) correlated with worse DFS, although no factor independently correlated with OS. Looking at factors influencing local and regional failure, we observed that stromal invasion >2 mm was the only factor predicting for local recurrence, whereas lymph node extracapsular involvement predicted for regional recurrence. Stromal invasion >2 mm and lymph node extracapsular spread are the most important factors predicting for local and regional failure, respectively. Studies evaluating the effectiveness of adjuvant treatment in high-risk patients are warranted.

Spatial ecological processes and local factors predict the distribution and abundance of spawning by steelhead (Oncorhynchus mykiss) across a complex riverscape

USGS Publications Warehouse

Falke, Jeffrey A.; Dunham, Jason B.; Jordan, Christopher E.; McNyset, Kris M.; Reeves, Gordon H.

2013-01-01

Processes that influence habitat selection in landscapes involve the interaction of habitat composition and configuration and are particularly important for species with complex life cycles. We assessed the relative influence of landscape spatial processes and local habitat characteristics on patterns in the distribution and abundance of spawning steelhead (Oncorhynchus mykiss), a threatened salmonid fish, across ~15,000 stream km in the John Day River basin, Oregon, USA. We used hurdle regression and a multi-model information theoretic approach to identify the relative importance of covariates representing key aspects of the steelhead life cycle (e.g., site access, spawning habitat quality, juvenile survival) at two spatial scales: within 2-km long survey reaches (local sites) and ecological neighborhoods (5 km) surrounding the local sites. Based on Akaike’s Information Criterion, models that included covariates describing ecological neighborhoods provided the best description of the distribution and abundance of steelhead spawning given the data. Among these covariates, our representation of offspring survival (growing-season-degree-days, °C) had the strongest effect size (7x) relative to other predictors. Predictive performances of model-averaged composite and neighborhood-only models were better than a site-only model based on both occurrence (percentage of sites correctly classified = 0.80±0.03 SD, 0.78±0.02 vs. 0.62±0.05, respectively) and counts (root mean square error = 3.37, 3.93 vs. 5.57, respectively). The importance of both temperature and stream flow for steelhead spawning suggest this species may be highly sensitive to impacts of land and water uses, and to projected climate impacts in the region and that landscape context, complementation, and connectivity will drive how this species responds to future environments.

Predictive factors for survival and correlation to toxicity in advanced Stage III non-small cell lung cancer patients with concurrent chemoradiation.

PubMed

Kim, Yong-Hyub; Ahn, Sung-Ja; Kim, Young-Chul; Kim, Kyu-Sik; Oh, In-Jae; Ban, Hee-Jung; Chung, Woong-Ki; Nam, Taek-Keun; Yoon, Mee Sun; Jeong, Jae-Uk; Song, Ju-Young

2016-02-01

Concurrent chemoradiotherapy is the standard treatment for locally advanced Stage III non-small cell lung cancer in patients with a good performance status and minimal weight loss. This study aimed to define subgroups with different survival outcomes and identify correlations with the radiation-related toxicities. We retrospectively reviewed 381 locally advanced Stage III non-small cell lung cancer patients with a good performance status or weight loss of <10% who received concurrent chemoradiotherapy between 2004 and 2011. Three-dimensional conformal radiotherapy was administered once daily, combined with weekly chemotherapy. The Kaplan-Meier method was used for survival comparison and Cox regression for multivariate analysis. Multivariate analysis was performed using all variables with P values <0.1 from the univariate analysis. Median survival of all patients was 24 months. Age > 75 years, the diffusion lung capacity for carbon monoxide ≤80%, gross tumor volume ≥100 cm(3) and subcarinal nodal involvement were the statistically significant predictive factors for poor overall survival both in univariate and multivariate analyses. Patients were classified into four groups according to these four predictive factors. The median survival times were 36, 29, 18 and 14 months in Groups I, II, III and IV, respectively (P < 0.001). Rates of esophageal or lung toxicity ≥Grade 3 were 5.9, 14.1, 12.5 and 22.2%, respectively. The radiotherapy interruption rate differed significantly between the prognostic subgroups; 8.8, 15.4, 22.7 and 30.6%, respectively (P = 0.017). Severe toxicity and interruption of radiotherapy were more frequent in patients with multiple adverse predictive factors. To maintain the survival benefit in patients with concurrent chemoradiotherapy, strategies to reduce treatment-related toxicities need to be deeply considered. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predictive factors for moderate or severe exacerbations in asthma patients receiving outpatient care.

PubMed

Gutiérrez, Francisco Javier Álvarez; Galván, Marta Ferrer; Gallardo, Juan Francisco Medina; Mancera, Marta Barrera; Romero, Beatriz Romero; Falcón, Auxiliadora Romero

2017-05-02

Asthma exacerbations are important events that affect disease control, but predictive factors for severe or moderate exacerbations are not known. The objective was to study the predictive factors for moderate (ME) and severe (SE) exacerbations in asthma patients receiving outpatient care. Patients aged > 12 years with asthma were included in the study and followed-up at 4-monthly intervals over a 12-month period. Clinical (severity, level of control, asthma control test [ACT]), atopic, functional, inflammatory, SE and ME parameters were recorded. Univariate analysis was used to compare data from patients presenting at least 1 SE or ME during the follow-up period vs no exacerbations. Statistically significant (p <0.1) factors were then subjected to multiple analysis by binary logistic regression. A total of 330 patients completed the study, most of whom were atopic (76%), women (nearly 70%), with moderate and mild persistent asthma (>80%). Twenty-seven patients (8%) had a SE and 183 had a ME (58.5%) during follow-up. In the case of SEs, the only predictive factor identified in the multiple analysis was previous SE (baseline visit OR 4.218 95% CI 1.53-11.58, 4-month follow-up OR 6.88 95% CI 2.018-23.51) and inhalation technique (OR 3.572 95% CI 1.324-9.638). In the case of MEs, the only predictive factor found in the multiple analysis were previous ME (baseline visit OR 2.90 95% CI 1.54-5.48, 4-month follow- up OR 1.702 95% CI 1.146-2.529). The primary predictive factor for SE or ME is prior SE or ME, respectively. SEs seem to constitute a specific patient "phenotype", in which the sole predictive factor is prior SEs.

[Predictive factors of the outcomes of prenatal hydronephrosis.

PubMed

Bragagnini, Paolo; Estors, Blanca; Delgado, Reyes; Rihuete, Miguel Ángel; Gracia, Jesús

2016-12-01

To determine prenatal and postnatal independent predictors of poor outcome, spontaneous resolution, or the need for surgery in patients with prenatal hydronephrosis. We performed a retrospective study of patients with prenatal hydronephrosis. The renal pelvis APD was measured in the third prenatal trimester ultrasound, as well as in the first and second postnatal ultrasound. Other variables were taken into account, both prenatal and postnatal. For statistical analysis we used Student t-test, chi-square test, survival analysis, logrank test, and ROC curves. We included 218 patients with 293 renal units (RU). Of these, 147/293 (50.2%) RU were operated. 76/293 (25.9%) RU had spontaneous resolution and other 76/293 (25.9%) RU had poor outcome. As risk factors for surgery we found low birth weight (OR 3.84; 95% CI 1.24-11.84), prematurity (OR 4.17; 95% CI 1.35-12.88), duplication (OR 4.99; 95% CI 2.21-11.23) and the presence of nephrourological underlying pathology (OR 53.54; 95% CI 26.23-109.27). For the non-spontaneous resolution, we found as risk factors the alterations of amniotic fluid volume (RR 1.46; 95% CI 1.33-1.60) as well as the underlying nephrourological pathology and duplication. In the poor outcome, we found as risk factors the alterations of amniotic fluid volume (OR 4.54; 95% CI 1.31-15.62), the presence of nephrourological pathology (OR 4.81 95% CI 2.60-8.89) and RU that was operated (OR 4.23, 95% CI 2.35-7.60). The APD of the renal pelvis in all three ultrasounds were reliable for surgery prediction (area under the curve 0.65; 0.82; 0.71) or spontaneous resolution (area under the curve 0.80; 0.91; 0.80), only the first postnatal ultrasound has predictive value in the poor outcome (area under the curve 0.73). The higher sensitivity and specificity of the APD as predictor value was on the first postnatal ultrasound, 14.60 mm for surgery; 11.35 mm for spontaneous resolution and 15.50 mm for poor outcome. The higher APD in the renal pelvis in any of the

Individual Factors Predicting Mental Health Court Diversion Outcome

ERIC Educational Resources Information Center

Verhaaff, Ashley; Scott, Hannah

2015-01-01

Objective: This study examined which individual factors predict mental health court diversion outcome among a sample of persons with mental illness participating in a postcharge diversion program. Method: The study employed secondary analysis of existing program records for 419 persons with mental illness in a court diversion program. Results:…

Psychosocial Factors Predicting First-Year College Student Success

ERIC Educational Resources Information Center

Krumrei-Mancuso, Elizabeth J.; Newton, Fred B.; Kim, Eunhee; Wilcox, Dan

2013-01-01

This study made use of a model of college success that involves students achieving academic goals and life satisfaction. Hierarchical regressions examined the role of six psychosocial factors for college success among 579 first-year college students. Academic self-efficacy and organization and attention to study were predictive of first semester…

Learning Approaches, Demographic Factors to Predict Academic Outcomes

ERIC Educational Resources Information Center

Nguyen, Tuan Minh

2016-01-01

Purpose: The purpose of this paper is to predict academic outcome in math and math-related subjects using learning approaches and demographic factors. Design/Methodology/Approach: ASSIST was used as the instrumentation to measure learning approaches. The study was conducted in the International University of Vietnam with 616 participants. An…

Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses.

PubMed

Kwasigroch, Jean Marc; Rooman, Marianne

2006-07-15

Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. http://babylone.ulb.ac.be/Prelude_and_Fugue.

The Role of Socioeconomic Factors in the Prediction of Persistence in Puerto Rico

ERIC Educational Resources Information Center

Dika, Sandra L.

2014-01-01

While research literature suggests that socioeconomic factors play a role in predicting educational attainment, very little research has been done to examine these relationships using data from Puerto Rico. A logistic regression approach was adopted to investigate the extent to which family and school socioeconomic factors predict retention from…

Factors Predictive of Healing in Large Rotator Cuff Tears: Is It Possible to Predict Retear Preoperatively?

PubMed

Jeong, Ho Yeon; Kim, Hwan Jin; Jeon, Yoon Sang; Rhee, Yong Girl

2018-03-01

Many studies have identified risk factors that cause retear after rotator cuff repair. However, it is still questionable whether retears can be predicted preoperatively. To determine the risk factors related to retear after arthroscopic rotator cuff repair and to evaluate whether it is possible to predict the occurrence of retear preoperatively. Case-control study; Level of evidence, 3. This study enrolled 112 patients who underwent arthroscopic rotator cuff repair with single-row technique for a large-sized tear, defined as a tear with a mediolateral length of 3 to 5 cm. All patients underwent routine magnetic resonance imaging (MRI) at 9 months postoperatively to assess tendon integrity. The sample included 61 patients (54.5%) in the healed group and 51 (45.5%) in the retear group. In multivariate analysis, the independent predictors of retears were supraspinatus muscle atrophy ( P < .001) and fatty infiltration of the infraspinatus ( P = .027), which could be preoperatively measured by MRI. A significant difference was found between the two groups in sex, the acromiohumeral interval, tendon tension, and preoperative or intraoperative mediolateral tear length and musculotendinous junction position in univariate analysis. However, these variables were not independent predictors in multivariate analysis. The cutoff values of occupation ratio of supraspinatus and fatty infiltration of the infraspinatus were 43% and grade 2, respectively. The occupation ratio of supraspinatus <43% and grade ≥2 fatty infiltration of the infraspinatus were the strongest predictors of retear, with an area under the curve of 0.908, sensitivity of 98.0%, and specificity of 83.6% (accuracy = 90.2%). In patients with large rotator cuff tears, it was possible to predict the retear before rotator cuff repair regardless of intraoperative factors. The retear could be predicted most effectively when the occupation ratio of supraspinatus was <43% or the fatty infiltration of infraspinatus was

Spatial Clustering and Local Risk Factors of Chronic Obstructive Pulmonary Disease (COPD).

PubMed

Chan, Ta-Chien; Wang, Hsuan-Wen; Tseng, Tzu-Jung; Chiang, Po-Huang

2015-12-10

Chronic obstructive pulmonary disease (COPD) mortality has been steadily increasing in Taiwan since 2009. In order to understand where the hotspot areas are and what the local risk factors are, we integrated an ecological and a case-control study. We used a two-stage approach to identify hotspots and explore the possible risk factors for developing COPD. The first stage used the annual township COPD mortality from 2000 to 2012 and applied the retrospective space-time scan statistic to calculate the local relative risks in each township. In the second stage, we conducted a case-control study, recruiting 200 patients from one local hospital within the one identified hotspot area located in southern Taiwan. Logistic regression was applied for analyzing the personal risk factors of COPD. The univariate analyses showed that higher percentages of aborigines, patients with tuberculosis (TB) history, and those with smoking history had COPD (p < 0.05). After controlling for demographic variables, aboriginal status (adjusted odds ratios (AORs): 3.01, 95% CI: 1.52-5.93) and smoking history (AORs: 2.64, 95% CI: 1.46-4.76) were still the two significant risk factors. This two-stage approach might be beneficial to examine and cross-validate the findings from an aggregate to an individual scale, and can be easily extended to other chronic diseases.

Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning.

PubMed

Mei, Suyu

2012-10-07

Recent years have witnessed much progress in computational modeling for protein subcellular localization. However, there are far few computational models for predicting plant protein subcellular multi-localization. In this paper, we propose a multi-label multi-kernel transfer learning model for predicting multiple subcellular locations of plant proteins (MLMK-TLM). The method proposes a multi-label confusion matrix and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which we further extend our published work MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for plant protein subcellular multi-localization. By proper homolog knowledge transfer, MLMK-TLM is applicable to novel plant protein subcellular localization in multi-label learning scenario. The experiments on plant protein benchmark dataset show that MLMK-TLM outperforms the baseline model. Unlike the existing models, MLMK-TLM also reports its misleading tendency, which is important for comprehensive survey of model's multi-labeling performance. Copyright © 2012 Elsevier Ltd. All rights reserved.

Support Vector Machine-Based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

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

Klement, Rainer J., E-mail: rainer_klement@gmx.de; Department of Radiotherapy and Radiation Oncology, Leopoldina Hospital, Schweinfurt; Allgäuer, Michael

2014-03-01

Background: Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control. Methods and Materials: We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performancemore » by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection. Results: Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED{sub ISO}) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED{sub ISO} and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED{sub ISO}, age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively. Conclusions: These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional

Prediction of local concentration statistics in variably saturated soils: Influence of observation scale and comparison with field data

NASA Astrophysics Data System (ADS)

Graham, Wendy; Destouni, Georgia; Demmy, George; Foussereau, Xavier

1998-07-01

The methodology developed in Destouni and Graham [Destouni, G., Graham, W.D., 1997. The influence of observation method on local concentration statistics in the subsurface. Water Resour. Res. 33 (4) 663-676.] for predicting locally measured concentration statistics for solute transport in heterogeneous porous media under saturated flow conditions is applied to the prediction of conservative nonreactive solute transport in the vadose zone where observations are obtained by soil coring. Exact analytical solutions are developed for both the mean and variance of solute concentrations measured in discrete soil cores using a simplified physical model for vadose-zone flow and solute transport. Theoretical results show that while the ensemble mean concentration is relatively insensitive to the length-scale of the measurement, predictions of the concentration variance are significantly impacted by the sampling interval. Results also show that accounting for vertical heterogeneity in the soil profile results in significantly less spreading in the mean and variance of the measured solute breakthrough curves, indicating that it is important to account for vertical heterogeneity even for relatively small travel distances. Model predictions for both the mean and variance of locally measured solute concentration, based on independently estimated model parameters, agree well with data from a field tracer test conducted in Manatee County, Florida.

Using fire-weather forecasts and local weather observations in predicting burning index for individual fire-danger stations.

Treesearch

Owen P. Cramer

1958-01-01

Any agency engaged in forest-fire control needs accurate weather forecasts and systematic procedures for making the best use of predicted and reported weather information. This study explores the practicability of using several tabular and graphical aids for converting area forecasts and local observations of relative humidity and wind speed into predicted values for...

Collaborative Completion of Transcription Factor Binding Profiles via Local Sensitive Unified Embedding.

PubMed

Zhu, Lin; Guo, Wei-Li; Lu, Canyi; Huang, De-Shuang

2016-12-01

Although the newly available ChIP-seq data provides immense opportunities for comparative study of regulatory activities across different biological conditions, due to cost, time or sample material availability, it is not always possible for researchers to obtain binding profiles for every protein in every sample of interest, which considerably limits the power of integrative studies. Recently, by leveraging related information from measured data, Ernst et al. proposed ChromImpute for predicting additional ChIP-seq and other types of datasets, it is demonstrated that the imputed signal tracks accurately approximate the experimentally measured signals, and thereby could potentially enhance the power of integrative analysis. Despite the success of ChromImpute, in this paper, we reexamine its learning process, and show that its performance may degrade substantially and sometimes may even fail to output a prediction when the available data is scarce. This limitation could hurt its applicability to important predictive tasks, such as the imputation of TF binding data. To alleviate this problem, we propose a novel method called Local Sensitive Unified Embedding (LSUE) for imputing new ChIP-seq datasets. In LSUE, the ChIP-seq data compendium are fused together by mapping proteins, samples, and genomic positions simultaneously into the Euclidean space, thereby making their underling associations directly evaluable using simple calculations. In contrast to ChromImpute which mainly makes use of the local correlations between available datasets, LSUE can better estimate the overall data structure by formulating the representation learning of all involved entities as a single unified optimization problem. Meanwhile, a novel form of local sensitive low rank regularization is also proposed to further improve the performance of LSUE. Experimental evaluations on the ENCODE TF ChIP-seq data illustrate the performance of the proposed model. The code of LSUE is available at https://github.com/ekffar/LSUE.

Analysis of uncertainties in turbine metal temperature predictions

NASA Technical Reports Server (NTRS)

Stepka, F. S.

1980-01-01

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

Gluing operation and form factors of local operators in N = 4 Super Yang-Mills theory

NASA Astrophysics Data System (ADS)

Bolshov, A. E.

2018-04-01

The gluing operation is an effective way to get form factors of both local and non-local operators starting from different representations of on-shell scattering amplitudes. In this paper it is shown how it works on the example of form factors of operators from stress-tensor operator supermultiplet in Grassmannian and spinor helicity representations.

Predictive factors for red blood cell transfusion in children undergoing noncomplex cardiac surgery.

PubMed

Mulaj, Muj; Faraoni, David; Willems, Ariane; Sanchez Torres, Cristel; Van der Linden, Philippe

2014-08-01

Red blood cell (RBC) transfusion is frequently required in pediatric cardiac surgery and is associated with altered outcome and increased costs. Determining which factors predict transfusion in this context will enable clinicians to adopt strategies that will reduce the risk of RBC transfusion. This study aimed to assess predictive factors associated with RBC transfusion in children undergoing low-risk cardiac surgery with cardiopulmonary bypass (CPB). Children undergoing surgery to repair ventricular septal defect or atrioventricular septal defect from 2006 to 2011 were included in this retrospective study. Demography, preoperative laboratory testing, intraoperative data, and RBC transfusion were reviewed. Univariate and multivariate logistic regression analysis were used to define factors that were able to predict RBC transfusion. Then, we employed receiver operating characteristic analysis to design a predictive score. Among the 334 children included, 261 (78%) were transfused. Age (<18 months), priming volume of the CPB (>43 mL/kg), type of oxygenator used, minimal temperature reached during CPB (<32°C), and preoperative hematocrit (<34%) were independently associated with RBC transfusion in the studied population. A predictive score 2 or greater was the best predictor of RBC transfusion. The present study identified several factors that were significantly associated with perioperative RBC transfusion. Based on these factors, we designed a predictive score that can be used to develop a patient-based blood management program with the aim of reducing the incidence of RBC transfusion. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed Central

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-01-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 ± 9.2 and 14.1 ± 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 ± 6.9 and 14.2 ± 8.2 mL s−1 in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi. PMID:19966831

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-03-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 +/- 9.2 and 14.1 +/- 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 +/- 6.9 and 14.2 +/- 8.2 mL s(-1) in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi.

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

PubMed Central

Paramasivam, Nagarajan; Linke, Dirk

2011-01-01

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/ PMID:22073040

Predicting Periodontitis at State and Local Levels in the United States.

PubMed

Eke, P I; Zhang, X; Lu, H; Wei, L; Thornton-Evans, G; Greenlund, K J; Holt, J B; Croft, J B

2016-05-01

The objective of the study was to estimate the prevalence of periodontitis at state and local levels across the United States by using a novel, small area estimation (SAE) method. Extended multilevel regression and poststratification analyses were used to estimate the prevalence of periodontitis among adults aged 30 to 79 y at state, county, congressional district, and census tract levels by using periodontal data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012, population counts from the 2010 US census, and smoking status estimates from the Behavioral Risk Factor Surveillance System in 2012. The SAE method used age, race, gender, smoking, and poverty variables to estimate the prevalence of periodontitis as defined by the Centers for Disease Control and Prevention/American Academy of Periodontology case definitions at the census block levels and aggregated to larger administrative and geographic areas of interest. Model-based SAEs were validated against national estimates directly from NHANES 2009-2012. Estimated prevalence of periodontitis ranged from 37.7% in Utah to 52.8% in New Mexico among the states (mean, 45.1%; median, 44.9%) and from 33.7% to 68% among counties (mean, 46.6%; median, 45.9%). Severe periodontitis ranged from 7.27% in New Hampshire to 10.26% in Louisiana among the states (mean, 8.9%; median, 8.8%) and from 5.2% to 17.9% among counties (mean, 9.2%; median, 8.8%). Overall, the predicted prevalence of periodontitis was highest for southeastern and southwestern states and for geographic areas in the Southeast along the Mississippi Delta, as well as along the US and Mexico border. Aggregated model-based SAEs were consistent with national prevalence estimates from NHANES 2009-2012. This study is the first-ever estimation of periodontitis prevalence at state and local levels in the United States, and this modeling approach complements public health surveillance efforts to identify areas with a high burden of

Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

NASA Astrophysics Data System (ADS)

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2017-02-01

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

Remission of Intermediate Uveitis: Incidence and Predictive Factors.

PubMed

Kempen, John H; Gewaily, Dina Y; Newcomb, Craig W; Liesegang, Teresa L; Kaçmaz, R Oktay; Levy-Clarke, Grace A; Nussenblatt, Robert B; Rosenbaum, James T; Sen, H Nida; Suhler, Eric B; Thorne, Jennifer E; Foster, C Stephen; Jabs, Douglas A; Payal, Abhishek; Fitzgerald, Tonetta D

2016-04-01

To evaluate the incidence of remission among patients with intermediate uveitis; to identify factors potentially predictive of remission. Retrospective cohort study. Involved eyes of patients with primary noninfectious intermediate uveitis at 4 academic ocular inflammation subspecialty practices, followed sufficiently long to meet the remission outcome definition, were studied retrospectively by standardized chart review data. Remission of intermediate uveitis was defined as a lack of inflammatory activity at ≥2 visits spanning ≥90 days in the absence of any corticosteroid or immunosuppressant medications. Factors potentially predictive of intermediate uveitis remission were evaluated using survival analysis. Among 849 eyes (of 510 patients) with intermediate uveitis followed over 1934 eye-years, the incidence of intermediate uveitis remission was 8.6/100 eye-years (95% confidence interval [CI], 7.4-10.1). Factors predictive of disease remission included prior pars plana vitrectomy (PPV) (hazard ratio [HR] [vs no PPV] = 2.39; 95% CI, 1.42-4.00), diagnosis of intermediate uveitis within the last year (HR [vs diagnosis >5 years ago] =3.82; 95% CI, 1.91-7.63), age ≥45 years (HR [vs age <45 years] = 1.79; 95% CI, 1.03-3.11), female sex (HR = 1.61; 95% CI, 1.04-2.49), and Hispanic race/ethnicity (HR [vs white race] = 2.81; 95% CI, 1.23-6.41). Presence/absence of a systemic inflammatory disease, laterality of uveitis, and smoking status were not associated with differential incidence. Our results suggest that intermediate uveitis is a chronic disease with an overall low rate of remission. Recently diagnosed patients and older, female, and Hispanic patients were more likely to remit. With regard to management, pars plana vitrectomy was associated with increased probability of remission. Copyright © 2016 Elsevier Inc. All rights reserved.

Remission of Intermediate Uveitis: Incidence and Predictive Factors

PubMed Central

Kempen, John H.; Gewaily, Dina Y.; Newcomb, Craig W.; Liesegang, Teresa L.; Kaçmaz, R. Oktay; Levy-Clarke, Grace A.; Nussenblatt, Robert B.; Rosenbaum, James T.; Sen, H. Nida; Suhler, Eric B.; Thorne, Jennifer E.; Foster, C. Stephen; Jabs, Douglas A.; Payal, Abhishek; Fitzgerald, Tonetta D.

2016-01-01

Purpose To evaluate the incidence of remission among patients with intermediate uveitis; to identify factors potentially predictive of remission. Design Retrospective cohort study. Methods Involved eyes of patients with primary non-infectious intermediate uveitis at 4 academic ocular inflammation subspecialty practices, followed sufficiently long to meet the remission outcome definition, were studied retrospectively by standardized chart review data. Remission of intermediate uveitis was defined as a lack of inflammatory activity at ≥2 visits spanning ≥90 days in the absence of any corticosteroid or immunosuppressant medications. Factors potentially predictive of intermediate uveitis remission were evaluated using survival analysis. Results Among 849 eyes (of 510 patients) with intermediate uveitis followed over 1,934 eye-years, the incidence of intermediate uveitis remission was 8.6/100 eye-years (95% confidence interval (CI), 7.4–10.1). Factors predictive of disease remission included prior pars plana vitrectomy (PPV) (HR (vs. no PPV)=2.39; 95% CI, 1.42–4.00), diagnosis of intermediate uveitis within the last year (vs. diagnosis >5 years ago)=3.82; 95% CI, 1.91–7.63), age ≥45 years (HR (vs. age <45 years)=1.79; 95% CI, 1.03–3.11), female sex (HR=1.61; 95% CI, 1.04–2.49), and Hispanic race/ethnicity (HR (vs. white race)=2.81; 95% CI, 1.23–6.41). Presence/absence of a systemic inflammatory disease, laterality of uveitis, and smoking status were not associated with differential incidence. Conclusions Our results suggest that intermediate uveitis is a chronic disease with an overall low rate of remission. Recently diagnosed cases, and older, female and Hispanic cases were more likely to remit. With regards to management, pars plana vitrectomy was associated with increased probability of remission. PMID:26772874

Predictive factors for pharyngocutaneous fistulization after total laryngectomy: a Dutch Head and Neck Society audit.

PubMed

Lansaat, Liset; van der Noort, Vincent; Bernard, Simone E; Eerenstein, Simone E J; Plaat, Boudewijn E C; Langeveld, Ton A P M; Lacko, Martin; Hilgers, Frans J M; de Bree, Remco; Takes, Robert P; van den Brekel, Michiel W M

2018-03-01

Incidences of pharyngocutaneous fistulization (PCF) after total laryngectomy (TL) reported in the literature vary widely, ranging from 2.6 to 65.5%. Comparison between different centers might identify risk factors, but also might enable improvements in quality of care. To enable this on a national level, an audit in the 8 principle Dutch Head and Neck Centers (DHNC) was initiated. A retrospective chart review of all 324 patients undergoing laryngectomy in a 2-year (2012 and 2013) period was performed. Overall PCF%, PCF% per center and factors predictive for PCF were identified. Furthermore, a prognostic model predicting the PCF% per center was developed. To provide additional data, a survey among the head and neck surgeons of the participating centers was carried out. Overall PCF% was 25.9. The multivariable prediction model revealed that previous treatment with (chemo)radiotherapy in combination with a long interval between primary treatment and TL, previous tracheotomy, near total pharyngectomy, neck dissection, and BMI < 18 were the best predictors for PCF. Early oral intake did not influence PCF rate. PCF% varied quite widely between centers, but for a large extend this could be explained with the prediction model. PCF performance rate (difference between the PCF% and the predicted PCF%) per DHNC, though, shows that not all differences are explained by factors established in the prediction model. However, these factors explain enough of the differences that, compensating for these factors, hospital is no longer independently predictive for PCF. This nationwide audit has provided valid comparative PCF data confirming the known risk factors from the literature which are important for counseling on PCF risks. Data show that variations in PCF% in the DHNCs (in part) are explainable by the variations in these predictive factors. Since elective neck dissection is a major risk factor for PCF, it only should be performed on well funded indication.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

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

He, Yupeng; Gorkin, David U.; Dickel, Diane E.

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

PubMed Central

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; Nery, Joseph R.; Castanon, Rosa G.; Lee, Ah Young; Shen, Yin; Visel, Axel; Pennacchio, Len A.; Ren, Bing; Ecker, Joseph R.

2017-01-01

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulatory element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types. REPTILE is available at https://github.com/yupenghe/REPTILE/. PMID:28193886

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

DOE PAGES

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; ...

2017-02-13

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Localization and prediction of malignant potential in recurrent pheochromocytoma/paraganglioma (PCC/PGL) using 18F-FDG PET/CT.

PubMed

Fikri, Ahmad Saad Fathinul; Kroiss, A; Ahmad, A Z F; Zanariah, H; Lau, W F E; Uprimny, C; Donnemiller, E; Kendler, D; Nordin, A J; Virgolini, I J

2014-06-01

To our knowledge, data are lacking on the role of 18F-FDG PET/CT in the localization and prediction of neuroendocrine tumors, in particular the pheochromocytoma/paraganglioma (PCC/PGL) group. To evaluate the role of 18F-FDG PET/CT in localizing and predicting the malignant potential of PCC/PGL. Twenty-three consecutive patients with a history of PCC/PGL, presenting with symptoms related to catecholamine excess, underwent 18F-FDG PET/CT. Final confirmation of the diagnosis was made using the composite references. PET/CT findings were analyzed on a per-lesion basis and a per-patient basis. Tumor SUVmax was analyzed to predict the dichotomization of patient endpoints for the local disease and metastatic groups. We investigated 23 patients (10 men, 13 women) with a mean age of 46.43 ± 3.70 years. Serum catecholamine levels were elevated in 82.60% of these patients. There were 136 sites (mean SUVmax: 16.39 ± 3.47) of validated disease recurrence. The overall sensitivities for diagnostic CT, FDG PET, and FDG PET/CT were 86.02%, 87.50%, and 98.59%, respectively. Based on the composite references, 39.10% of patients had local disease. There were significant differences in the SUVmax distribution between the local disease and metastatic groups; a significant correlation was noted when a SUVmax cut-off was set at 9.2 (P<0.05). In recurrent PCC/PGL, diagnostic 18F-FDG PET/CT is a superior tool in the localization of recurrent tumors. Tumor SUVmax is a potentially useful predictor of malignant tumor potential. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Temporal and spatial localization of prediction-error signals in the visual brain.

PubMed

Johnston, Patrick; Robinson, Jonathan; Kokkinakis, Athanasios; Ridgeway, Samuel; Simpson, Michael; Johnson, Sam; Kaufman, Jordy; Young, Andrew W

2017-04-01

It has been suggested that the brain pre-empts changes in the environment through generating predictions, although real-time electrophysiological evidence of prediction violations in the domain of visual perception remain elusive. In a series of experiments we showed participants sequences of images that followed a predictable implied sequence or whose final image violated the implied sequence. Through careful design we were able to use the same final image transitions across predictable and unpredictable conditions, ensuring that any differences in neural responses were due only to preceding context and not to the images themselves. EEG and MEG recordings showed that early (N170) and mid-latency (N300) visual evoked potentials were robustly modulated by images that violated the implied sequence across a range of types of image change (expression deformations, rigid-rotations and visual field location). This modulation occurred irrespective of stimulus object category. Although the stimuli were static images, MEG source reconstruction of the early latency signal (N/M170) localized expectancy violation signals to brain areas associated with motion perception. Our findings suggest that the N/M170 can index mismatches between predicted and actual visual inputs in a system that predicts trajectories based on ongoing context. More generally we suggest that the N/M170 may reflect a "family" of brain signals generated across widespread regions of the visual brain indexing the resolution of top-down influences and incoming sensory data. This has important implications for understanding the N/M170 and investigating how the brain represents context to generate perceptual predictions. Copyright © 2017 Elsevier B.V. All rights reserved.

Use the predictive models to explore the key factors affecting phytoplankton succession in Lake Erhai, China.

PubMed

Zhu, Rong; Wang, Huan; Chen, Jun; Shen, Hong; Deng, Xuwei

2018-01-01

Increasing algae in Lake Erhai has resulted in frequent blooms that have not only led to water ecosystem degeneration but also seriously influenced the quality of the water supply and caused extensive damage to the local people, as the lake is a water resource for Dali City. Exploring the key factors affecting phytoplankton succession and developing predictive models with easily detectable parameters for phytoplankton have been proven to be practical ways to improve water quality. To this end, a systematic survey focused on phytoplankton succession was conducted over 2 years in Lake Erhai. The data from the first study year were used to develop predictive models, and the data from the second year were used for model verification. The seasonal succession of phytoplankton in Lake Erhai was obvious. The dominant groups were Cyanobacteria in the summer, Chlorophyta in the autumn and Bacillariophyta in the winter. The developments and verification of predictive models indicated that compared to phytoplankton biomass, phytoplankton density is more effective for estimating phytoplankton variation in Lake Erhai. CCA (canonical correlation analysis) indicated that TN (total nitrogen), TP (total phosphorus), DO (dissolved oxygen), SD (Secchi depth), Cond (conductivity), T (water temperature), and ORP (oxidation reduction potential) had significant influences (p < 0.05) on the phytoplankton community. The CCA of the dominant species found that Microcystis was significantly influenced by T. The dominant Chlorophyta, Psephonema aenigmaticum and Mougeotia, were significantly influenced by TN. All results indicated that TN and T were the two key factors driving phytoplankton succession in Lake Erhai.

Profiling healthy eaters. Determining factors that predict healthy eating practices among Dutch adults.

PubMed

Swan, Emily; Bouwman, Laura; Hiddink, Gerrit Jan; Aarts, Noelle; Koelen, Maria

2015-06-01

Research has identified multiple factors that predict unhealthy eating practices. However what remains poorly understood are factors that promote healthy eating practices. This study aimed to determine a set of factors that represent a profile of healthy eaters. This research applied Antonovsky's salutogenic framework for health development to examine a set of factors that predict healthy eating in a cross-sectional study of Dutch adults. Data were analyzed from participants (n = 703) who completed the study's survey in January 2013. Logistic regression analysis was performed to test the association of survey factors on the outcome variable high dietary score. In the multivariate logistic regression model, five factors contributed significantly (p < .05) to the predictive ability of the overall model: being female; living with a partner; a strong sense of coherence (construct from the salutogenic framework), flexible restraint of eating, and self-efficacy for healthy eating. Findings complement what is already known of the factors that relate to poor eating practices. This can provide nutrition promotion with a more comprehensive picture of the factors that both support and hinder healthy eating practices. Future research should explore these factors to better understand their origins and mechanisms in relation to healthy eating practices. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predictive and Prognostic Factors in Ovarian and Uterine Carcinosarcomas

PubMed Central

Cicin, İrfan; Özatlı, Tahsin; Türkmen, Esma; Özturk, Türkan; Özçelik, Melike; Çabuk, Devrim; Gökdurnalı, Ayşe; Balvan, Özlem; Yıldız, Yaşar; Şeker, Metin; Özdemir, Nuriye; Yapar, Burcu; Tanrıverdi, Özgür; Günaydin, Yusuf; Menekşe, Serkan; Öksüzoğlu, Berna; Aksoy, Asude; Erdogan, Bülent; Bekir Hacıoglu, M.; Arpaci, Erkan; Sevinç, Alper

2016-01-01

Background: Prognostic factors and the standard treatment approach for gynaecological carcinosarcomas have not yet been clearly defined. Although carcinosarcomas are more aggressive than pure epithelial tumours, they are treated similarly. Serous/clear cell and endometrioid components may be predictive factors for the efficacy of adjuvant chemotherapy (CT) or radiotherapy (RT) or RT in patients with uterine and ovarian carcinosarcomas. Heterologous carcinosarcomas may benefit more from adjuvant CT. Aims: We aimed to define the prognostic and predictive factors associated with treatment options in ovarian (OCS) and uterine carcinosarcoma (UCS). Study Design: Retrospective cross-sectional study Methods: We retrospectively reviewed the medical records of patients with ovarian and uterine carcinosarcoma from 2000 to 2013, and 127 women were included in this study (24 ovarian and 103 uterine). Patients admitted to seventeen oncology centres in Turkey between 2000 and December 2013 with a histologically proven diagnosis of uterine carcinosarcoma with FIGO 2009 stage I–III and patients with sufficient data obtained from well-kept medical records were included in this study. Stage IV tumours were excluded. The patient records were retrospectively reviewed. Data from 104 patients were evaluated for this study. Results: Age (≥70 years) was a poor prognostic factor for UCS (p=0.036). Pelvic±para aortic lymph node dissection did not affect overall survival (OS) (p=0.35). Macroscopic residual disease was related with OS (p<0.01). The median OS was significantly longer in stage I–II patients than stage III patients (p=0.03). Adjuvant treatment improved OS (p=0.013). Adjuvant radiotherapy tended to increase the median OS (p=0.075). However, this tendency was observed in UCS (p=0.08) rather than OCS (p=0.6).Adjuvant chemotherapy had no effect on OS (p=0.15).Adjuvant radiotherapy significantly prolonged the median OS in patients with endometrioid component (p=0.034). A

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Local functional descriptors for surface comparison based binding prediction

PubMed Central

2012-01-01

Background Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface. Results We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods. Conclusions Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications. PMID:23176080

Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-12-02

In the postgenomic era, the number of unreviewed protein sequences is remarkably larger and grows tremendously faster than that of reviewed ones. However, existing methods for protein subchloroplast localization often ignore the information from these unlabeled proteins. This paper proposes a multi-label predictor based on ensemble linear neighborhood propagation (LNP), namely, LNP-Chlo, which leverages hybrid sequence-based feature information from both labeled and unlabeled proteins for predicting localization of both single- and multi-label chloroplast proteins. Experimental results on a stringent benchmark dataset and a novel independent dataset suggest that LNP-Chlo performs at least 6% (absolute) better than state-of-the-art predictors. This paper also demonstrates that ensemble LNP significantly outperforms LNP based on individual features. For readers' convenience, the online Web server LNP-Chlo is freely available at http://bioinfo.eie.polyu.edu.hk/LNPChloServer/ .

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

PubMed Central

Liu, Tao; Zhu, Guanghu; Lin, Hualiang; Zhang, Yonghui; He, Jianfeng; Deng, Aiping; Peng, Zhiqiang; Xiao, Jianpeng; Rutherford, Shannon; Xie, Runsheng; Zeng, Weilin; Li, Xing; Ma, Wenjun

2017-01-01

Background Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. Methodology and principal findings A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Conclusions Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. PMID:28263988

Angiogenic factors combined with clinical risk factors to predict preterm pre-eclampsia in nulliparous women: a predictive test accuracy study.

PubMed

Myers, J E; Kenny, L C; McCowan, L M E; Chan, E H Y; Dekker, G A; Poston, L; Simpson, N A B; North, R A

2013-09-01

To assess the performance of clinical risk factors, uterine artery Doppler and angiogenic markers to predict preterm pre-eclampsia in nulliparous women. Predictive test accuracy study. Prospective multicentre cohort study Screening for Pregnancy Endpoints (SCOPE). Low-risk nulliparous women with a singleton pregnancy were recruited. Clinical risk factor data were obtained and plasma placental growth factor (PlGF), soluble endoglin and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured at 14-16 weeks of gestation. Prediction models were developed using multivariable stepwise logistic regression. Preterm pre-eclampsia (delivered before 37(+0)  weeks of gestation). Of the 3529 women recruited, 187 (5.3%) developed pre-eclampsia of whom 47 (1.3%) delivered preterm. Controls (n = 188) were randomly selected from women without preterm pre-eclampsia and included women who developed other pregnancy complications. An area under a receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.67-0.84) was observed using previously reported clinical risk variables. The AUC improved following the addition of PlGF measured at 14-16 weeks (0.84; 95% CI 0.77-0.91), but no further improvement was observed with the addition of uterine artery Doppler or the other angiogenic markers. A sensitivity of 45% (95% CI 0.31-0.59) (5% false-positive rate) and post-test probability of 11% (95% CI 9-13) were observed using clinical risk variables and PlGF measurement. Addition of plasma PlGF at 14-16 weeks of gestation to clinical risk assessment improved the identification of nulliparous women at increased risk of developing preterm pre-eclampsia, but the performance is not sufficient to warrant introduction as a clinical screening test. These findings are marker dependent, not assay dependent; additional markers are needed to achieve clinical utility. © 2013 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2013 RCOG.

Predictive factors of head and neck squamous cell carcinoma patient tolerance to high-dose cisplatin in concurrent chemoradiotherapy

PubMed Central

NAKANO, KENJI; SATO, YASUYOSHI; TOSHIYASU, TAKASHI; SATO, YUKIKO; INAGAKI, LINA; TOMOMATSU, JUNICHI; SASAKI, TORU; SHIMBASHI, WATARU; FUKUSHIMA, HIROFUMI; YONEKAWA, HIROYUKI; MITANI, HIROKI; KAWABATA, KAZUYOSHI; TAKAHASHI, SHUNJI

2016-01-01

Although high-dose cisplatin is the standard regimen of concurrent chemoradiotherapy (CCRT) for locally advanced head and neck squamous cell carcinoma (HNSCC), varying levels of patient tolerance towards cisplatin have been reported, and the predictive factors of cisplatin tolerance remain to be elucidated. The present study retrospectively reviewed newly diagnosed HNSCC patients who received CCRT. Cisplatin (80 mg/m2) was administered every 3 weeks. The proportion of high-dose cisplatin-tolerant patients (cumulative cisplatin dose, ≥200 mg/m2) was determined, and the predictive factors of cisplatin tolerance were analyzed in a logistic regression analysis. Between June 2006 and March 2013, a total of 159 patients were treated with CCRT. The median follow-up time was 36.7 months. A total of 73 patients (46%) tolerated a cumulative cisplatin dose ≥200 mg/m2; male gender [odds ratio (OR), 25.00; P=0.005] and high body surface area (BSA) (>1.80 m2; OR, 2.21; P=0.032) were significantly predictive of high-dose cisplatin tolerance. The high-dose cisplatin-tolerant patients had a significantly higher complete response (CR) rate (82 vs. 67%, P=0.045); however, there were no significant between-group differences in the 3-year OS (79.5 vs. 81.2%, P=0.59) or PFS (70.4 vs. 44.6%, P=0.076) by cisplatin tolerance. In clinical practice, approximately one-half of the patients tolerated high-dose cisplatin in CCRT. Male gender and high BSA could be predictive of cisplatin tolerance. PMID:26893880

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

PubMed

Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

2015-12-01

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion

Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

NASA Astrophysics Data System (ADS)

Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

2014-02-01

Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

Frequency-dependent local field factors in dielectric liquids by a polarizable force field and molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Davari, Nazanin; Haghdani, Shokouh; Åstrand, Per-Olof

2015-12-01

A force field model for calculating local field factors, i.e. the linear response of the local electric field for example at a nucleus in a molecule with respect to an applied electric field, is discussed. It is based on a combined charge-transfer and point-dipole interaction model for the polarizability, and thereby it includes two physically distinct terms for describing electronic polarization: changes in atomic charges arising from transfer of charge between the atoms and atomic induced dipole moments. A time dependence is included both for the atomic charges and the atomic dipole moments and if they are assumed to oscillate with the same frequency as the applied electric field, a model for frequency-dependent properties are obtained. Furthermore, if a life-time of excited states are included, a model for the complex frequency-dependent polariability is obtained including also information about excited states and the absorption spectrum. We thus present a model for the frequency-dependent local field factors through the first molecular excitation energy. It is combined with molecular dynamics simulations of liquids where a large set of configurations are sampled and for which local field factors are calculated. We are normally not interested in the average of the local field factor but rather in configurations where it is as high as possible. In electrical insulation, we would like to avoid high local field factors to reduce the risk for electrical breakdown, whereas for example in surface-enhanced Raman spectroscopy, high local field factors are desired to give dramatically increased intensities.

Robust prediction of protein subcellular localization combining PCA and WSVMs.

PubMed

Tian, Jiang; Gu, Hong; Liu, Wenqi; Gao, Chiyang

2011-08-01

Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting habitat suitability for rare plants at local spatial scales using a species distribution model.

PubMed

Gogol-Prokurat, Melanie

2011-01-01

If species distribution models (SDMs) can rank habitat suitability at a local scale, they may be a valuable conservation planning tool for rare, patchily distributed species. This study assessed the ability of Maxent, an SDM reported to be appropriate for modeling rare species, to rank habitat suitability at a local scale for four edaphic endemic rare plants of gabbroic soils in El Dorado County, California, and examined the effects of grain size, spatial extent, and fine-grain environmental predictors on local-scale model accuracy. Models were developed using species occurrence data mapped on public lands and were evaluated using an independent data set of presence and absence locations on surrounding lands, mimicking a typical conservation-planning scenario that prioritizes potential habitat on unsurveyed lands surrounding known occurrences. Maxent produced models that were successful at discriminating between suitable and unsuitable habitat at the local scale for all four species, and predicted habitat suitability values were proportional to likelihood of occurrence or population abundance for three of four species. Unfortunately, models with the best discrimination (i.e., AUC) were not always the most useful for ranking habitat suitability. The use of independent test data showed metrics that were valuable for evaluating which variables and model choices (e.g., grain, extent) to use in guiding habitat prioritization for conservation of these species. A goodness-of-fit test was used to determine whether habitat suitability values ranked habitat suitability on a continuous scale. If they did not, a minimum acceptable error predicted area criterion was used to determine the threshold for classifying habitat as suitable or unsuitable. I found a trade-off between model extent and the use of fine-grain environmental variables: goodness of fit was improved at larger extents, and fine-grain environmental variables improved local-scale accuracy, but fine-grain variables

Predicting Transition from Laminar to Turbulent Flow over a Surface

NASA Technical Reports Server (NTRS)

Rajnarayan, Dev (Inventor); Sturdza, Peter (Inventor)

2016-01-01

A prediction of whether a point on a computer-generated surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For an instability mode in the plurality of instability modes, a covariance vector is determined. A predicted local instability growth rate at the point is determined using the covariance vector and the vector of regressor weights. Based on the predicted local instability growth rate, an n-factor envelope at the point is determined.

Epileptogenic zone localization using magnetoencephalography predicts seizure freedom in epilepsy surgery

PubMed Central

Englot, Dario J.; Nagarajan, Srikantan S.; Imber, Brandon S.; Raygor, Kunal P.; Honma, Susanne M.; Mizuiri, Danielle; Mantle, Mary; Knowlton, Robert C.; Kirsch, Heidi E.; Chang, Edward F.

2015-01-01

Objective The efficacy of epilepsy surgery depends critically upon successful localization of the epileptogenic zone. Magnetoencephalography (MEG) enables non-invasive detection of interictal spike activity in epilepsy, which can then be localized in three dimensions using magnetic source imaging (MSI) techniques. However, the clinical value of MEG in the pre-surgical epilepsy evaluation is not fully understood, as studies to date are limited by either a lack of long-term seizure outcomes or small sample size. Methods We performed a retrospective cohort study of focal epilepsy patients who received MEG for interictal spike mapping followed by surgical resection at our institution. Results We studied 132 surgical patients, with mean post-operative follow-up of 3.6 years (minimum 1 year). Dipole source modelling was successful in 103 (78%) patients, while no interictal spikes were seen in others. Among patients with successful dipole modelling, MEG findings were concordant with and specific to: i) the region of resection in 66% of patients, ii) invasive electrocorticography (ECoG) findings in 67% of individuals, and iii) the MRI abnormality in 74% of cases. MEG showed discordant lateralization in ~5% of cases. After surgery, 70% of all patients achieved seizure-freedom (Engel class I outcome). Whereas 85% of patients with concordant and specific MEG findings became seizure-free, this outcome was achieved by only 37% of individuals with MEG findings that were non-specific or discordant with the region of resection (χ2 = 26.4, p < 0.001). MEG reliability was comparable in patients with or without localized scalp EEG, and overall, localizing MEG findings predicted seizure freedom with an odds ratio of 5.11 (2.23–11.8, 95% CI). Significance MEG is a valuable tool for non-invasive interictal spike mapping in epilepsy surgery, including patients with non-localized findings on long-term EEG monitoring, and localization of the epileptogenic zone using MEG is associated

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

PubMed

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

Adolescent Alcohol Use: Protective and Predictive Parent, Peer, and Self-Related Factors

PubMed Central

Donaldson, Candice D.; Crano, William D.

2018-01-01

Adolescent alcohol use has been linked with a multitude of problems and a trajectory predictive of problematic use in adulthood. Thus, targeting factors that enhance early prevention efforts is vital. The current study highlights variables that mitigate or predict alcohol use and heavy episodic drinking. Using Monitoring the Future (MTF) data, multiple path analytic models revealed links between parental involvement and alcohol abstinence and initiation. Parental involvement predicted enhanced self-esteem and less self-derogation and was negatively associated with peer alcohol norms for each MTF grade sampled, with stronger associations for 8th and 10th graders than 12th graders. For younger groups, self-esteem predicted increased perceptions of alcohol risk and reduced drinking. Self-derogation was associated with peers’ pro-alcohol norms, which was linked to lower risk perceptions, lower personal disapproval of use, and increased drinking. Peer influence had a stronger association with consumption for 8th and 10th graders, whereas 12th graders’ drinking was related to personal factors of alcohol risk perception and disapproval. In all grades, general alcohol use had a strong connection to heavy episodic drinking within the past 2 weeks. Across-grade variations in association of parent, peer, and personal factors suggest the desirability of tailored interventions focused on specific factors for each grade level, with the overall goal of attenuating adolescent alcohol use. PMID:27562038

Clinical Prediction Making: Examining Influential Factors Related to Clinician Predictions of Recidivism among Juvenile Offenders

ERIC Educational Resources Information Center

Calley, Nancy G.; Richardson, Emily M.

2011-01-01

This study examined factors influencing clinician predictions of recidivism for juvenile offenders, including youth age at initial juvenile justice system involvement, youth age at discharge, program completion status, clinician perception of strength of the therapeutic relationship, and clinician perception of youth commitment to treatment.…

Local factors modify the dose dependence of 56Fe-induced atherosclerosis.

NASA Astrophysics Data System (ADS)

Kucik, Dennis; Gupta, Kiran; Wu, Xing; Yu, Tao; Chang, Polly; Kabarowski, Janusz; Yu, Shaohua

2012-07-01

Radiation exposure from a number of terrestrial sources is associated with an increased risk of cardiovascular disease, but evidence establishing whether high-LET radiation has similar effects has been lacking. We recently demonstrated that 600 MeV/n 56Fe induces atherosclerosis as well. Ten-week old male apolipoprotein-E deficient mice, a well-characterized atherosclerosis animal model, were exposed to 0 (control) 2, or 5Gy 56Fe targeted to the chest and neck. In these mice, 56Fe-induced atherosclerosis was similar in character to that induced by X-rays in the same mouse model and to that resulting from therapeutic radiation in cancer patients. Atherosclerosis was exacerbated by 56Fe only in targeted areas, however, suggesting a direct effect of the radiation on the arteries themselves. This is in contrast to some other risk factors, such as high cholesterol or tobacco use, which have systemic effects. The radiation dose required to accelerate development of atherosclerotic plaques, however, differed depending on the vessel that was irradiated and even the location within the vessel. For example, atherosclerosis in the aortic arch was accelerated only by the highest dose (5 Gy), while the carotid arteries and the aortic root showed effects at 2 Gy (a dose four- to eight-fold lower than the dose of X-rays that produces similar effects in this model). Since shear stress is disrupted in the area of the aortic root, it is likely that at least part of the site-specificity is due to additive or synergistic effects of radiation and local hydrodynamics. Other factors, such as local oxidative stress or gene expression may also have been involved. Since the pro-atherogenic effects of 56Fe depend on additional local factors, this suggests that radiation exposure, when unavoidable, might be mitigated by modification of factors unrelated to the radiation itself.

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

Internal Associations of the Acidic Region of Upstream Binding Factor Control Its Nucleolar Localization.

PubMed

Ueshima, Shuhei; Nagata, Kyosuke; Okuwaki, Mitsuru

2017-11-15

Upstream binding factor (UBF) is a member of the high-mobility group (HMG) box protein family, characterized by multiple HMG boxes and a C-terminal acidic region (AR). UBF is an essential transcription factor for rRNA genes and mediates the formation of transcriptionally active chromatin in the nucleolus. However, it remains unknown how UBF is specifically localized to the nucleolus. Here, we examined the molecular mechanisms that localize UBF to the nucleolus. We found that the first HMG box (HMG box 1), the linker region (LR), and the AR cooperatively regulate the nucleolar localization of UBF1. We demonstrated that the AR intramolecularly associates with and attenuates the DNA binding activity of HMG boxes and confers the structured DNA preference to HMG box 1. In contrast, the LR was found to serve as a nuclear localization signal and compete with HMG boxes to bind the AR, permitting nucleolar localization of UBF1. The LR sequence binds DNA and assists the stable chromatin binding of UBF. We also showed that the phosphorylation status of the AR does not clearly affect the localization of UBF1. Our results strongly suggest that associations of the AR with HMG boxes and the LR regulate UBF nucleolar localization. Copyright © 2017 American Society for Microbiology.

Evaluating Academic Journals Using Impact Factor and Local Citation Score

ERIC Educational Resources Information Center

Chung, Hye-Kyung

2007-01-01

This study presents a method for journal collection evaluation using citation analysis. Cost-per-use (CPU) for each title is used to measure cost-effectiveness with higher CPU scores indicating cost-effective titles. Use data are based on the impact factor and locally collected citation score of each title and is compared to the cost of managing…

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

Factors predictive of locoregional recurrence following neoadjuvant chemotherapy in patients with large operable or locally advanced breast cancer: An analysis of the EORTC 10994/BIG 1-00 study.

PubMed

Gillon, Pauline; Touati, Nathan; Breton-Callu, Christel; Slaets, Leen; Cameron, David; Bonnefoi, Hervé

2017-07-01

Identification of clinicopathological factors predicting for a locoregional recurrence (LRR) after neoadjuvant chemotherapy (NAC) could help to decide on the optimal locoregional radiotherapy. The objective of this trial is to identify those factors in the context of a phase III trial (European Organisation for Research and Treatment of Cancer 10994). Patients received NAC followed by surgery with or without radiotherapy. Radiotherapy was administered according to pre-specified guidelines. Patients with hormone receptor positive tumours received adjuvant hormonal therapy. A proportion of patients with human epidermal growth factor receptor 2 (HER2) positive cancer received adjuvant trastuzumab. The predictive factors for LRR were identified by multivariate analysis with time to LRR as first event as the primary end-point. The median follow-up was 4.4 years. In 1553 eligible patients, there were 76 LRRs with a 5-year cumulative incidence of 4.9% (95% confidence interval, CI [3.76-6.04]). In multivariate analysis, breast cancer subtype was a significant predictor of LRR (p < 0.0001): hazard ratio (HR) 6.44 (95% CI [2.83-14.69]) for triple negative, 6.26 (95% CI [2.81-13.93]) for HER2+ without trastuzumab (T) and 3.37 (95% CI [1.10-10.34]) for HER2+ with T cancers, all compared to luminal A patients. Lack of pathological response was also associated with significantly higher LRR risk in case of ≥4 pathologically positive nodes, HR 2.43 (95% CI [1.34-4.40], p < 0.0001). Breast cancer subtype and lack of pathological response are predictive factors for high LRR after NAC. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature.

PubMed

Mbeutcha, Aurélie; Mathieu, Romain; Rouprêt, Morgan; Gust, Kilian M; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2016-10-01

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, C. C., Jr.

1988-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 to + or - 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was developed to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Strengths of specimens containing crack-like slits were calculated from predicted failing strains using uniaxial stress-strain curves. Predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only + or - 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.

Demarcation of local neighborhoods to study relations between contextual factors and health

PubMed Central

2010-01-01

Background Several studies have highlighted the importance of collective social factors for population health. One of the major challenges is an adequate definition of the spatial units of analysis which present properties potentially related to the target outcomes. Political and administrative divisions of urban areas are the most commonly used definition, although they suffer limitations in their ability to fully express the neighborhoods as social and spatial units. Objective This study presents a proposal for defining the boundaries of local neighborhoods in Rio de Janeiro city. Local neighborhoods are constructed by means of aggregation of contiguous census tracts which are homogeneous regarding socioeconomic indicators. Methodology Local neighborhoods were created using the SKATER method (TerraView software). Criteria used for socioeconomic homogeneity were based on four census tract indicators (income, education, persons per household, and percentage of population in the 0-4-year age bracket) considering a minimum population of 5,000 people living in each local neighborhood. The process took into account the geographic boundaries between administrative neighborhoods (a political-administrative division larger than a local neighborhood, but smaller than a borough) and natural geographic barriers. Results The original 8,145 census tracts were collapsed into 794 local neighborhoods, distributed along 158 administrative neighborhoods. Local neighborhoods contained a mean of 10 census tracts, and there were an average of five local neighborhoods per administrative neighborhood. The local neighborhood units demarcated in this study are less socioeconomically heterogeneous than the administrative neighborhoods and provide a means for decreasing the well-known statistical variability of indicators based on census tracts. The local neighborhoods were able to distinguish between different areas within administrative neighborhoods, particularly in relation to squatter

Demarcation of local neighborhoods to study relations between contextual factors and health.

PubMed

Santos, Simone M; Chor, Dora; Werneck, Guilherme Loureiro

2010-06-29

Several studies have highlighted the importance of collective social factors for population health. One of the major challenges is an adequate definition of the spatial units of analysis which present properties potentially related to the target outcomes. Political and administrative divisions of urban areas are the most commonly used definition, although they suffer limitations in their ability to fully express the neighborhoods as social and spatial units. This study presents a proposal for defining the boundaries of local neighborhoods in Rio de Janeiro city. Local neighborhoods are constructed by means of aggregation of contiguous census tracts which are homogeneous regarding socioeconomic indicators. Local neighborhoods were created using the SKATER method (TerraView software). Criteria used for socioeconomic homogeneity were based on four census tract indicators (income, education, persons per household, and percentage of population in the 0-4-year age bracket) considering a minimum population of 5,000 people living in each local neighborhood. The process took into account the geographic boundaries between administrative neighborhoods (a political-administrative division larger than a local neighborhood, but smaller than a borough) and natural geographic barriers. The original 8,145 census tracts were collapsed into 794 local neighborhoods, distributed along 158 administrative neighborhoods. Local neighborhoods contained a mean of 10 census tracts, and there were an average of five local neighborhoods per administrative neighborhood.The local neighborhood units demarcated in this study are less socioeconomically heterogeneous than the administrative neighborhoods and provide a means for decreasing the well-known statistical variability of indicators based on census tracts. The local neighborhoods were able to distinguish between different areas within administrative neighborhoods, particularly in relation to squatter settlements. Although the literature on

Prediction of beef carcass and meat traits from rearing factors in young bulls and cull cows.

PubMed

Soulat, J; Picard, B; Léger, S; Monteils, V

2016-04-01

The aim of this study was to predict the beef carcass and LM (thoracis part) characteristics and the sensory properties of the LM from rearing factors applied during the fattening period. Individual data from 995 animals (688 young bulls and 307 cull cows) in 15 experiments were used to establish prediction models. The data concerned rearing factors (13 variables), carcass characteristics (5 variables), LM characteristics (2 variables), and LM sensory properties (3 variables). In this study, 8 prediction models were established: dressing percentage and the proportions of fat tissue and muscle in the carcass to characterize the beef carcass; cross-sectional area of fibers (mean fiber area) and isocitrate dehydrogenase activity to characterize the LM; and, finally, overall tenderness, juiciness, and flavor intensity scores to characterize the LM sensory properties. A random effect was considered in each model: the breed for the prediction models for the carcass and LM characteristics and the trained taste panel for the prediction of the meat sensory properties. To evaluate the quality of prediction models, 3 criteria were measured: robustness, accuracy, and precision. The model was robust when the root mean square errors of prediction of calibration and validation sub-data sets were near to one another. Except for the mean fiber area model, the obtained predicted models were robust. The prediction models were considered to have a high accuracy when the mean prediction error (MPE) was ≤0.10 and to have a high precision when the was the closest to 1. The prediction of the characteristics of the carcass from the rearing factors had a high precision ( > 0.70) and a high prediction accuracy (MPE < 0.10), except for the fat percentage model ( = 0.67, MPE = 0.16). However, the predictions of the LM characteristics and LM sensory properties from the rearing factors were not sufficiently precise ( < 0.50) and accurate (MPE > 0.10). Only the flavor intensity of the beef

Changing Pattern of Indian Monsoon Extremes: Global and Local Factors

NASA Astrophysics Data System (ADS)

Ghosh, Subimal; Shastri, Hiteshri; Pathak, Amey; Paul, Supantha

2017-04-01

Indian Summer Monsoon Rainfall (ISMR) extremes have remained a major topic of discussion in the field of global change and hydro-climatology over the last decade. This attributes to multiple conclusions on changing pattern of extremes along with poor understanding of multiple processes at global and local scales associated with monsoon extremes. At a spatially aggregate scale, when number of extremes in the grids are summed over, a statistically significant increasing trend is observed for both Central India (Goswami et al., 2006) and all India (Rajeevan et al., 2008). However, such a result over Central India does not satisfy flied significance test of increase and no decrease (Krishnamurthy et al., 2009). Statistically rigorous extreme value analysis that deals with the tail of the distribution reveals a spatially non-uniform trend of extremes over India (Ghosh et al., 2012). This results into statistically significant increasing trend of spatial variability. Such an increase of spatial variability points to the importance of local factors such as deforestation and urbanization. We hypothesize that increase of spatial average of extremes is associated with the increase of events occurring over large region, while increase in spatial variability attributes to local factors. A Lagrangian approach based dynamic recycling model reveals that the major contributor of moisture to wide spread extremes is Western Indian Ocean, while land surface also contributes around 25-30% of moisture during the extremes in Central India. We further test the impacts of local urbanization on extremes and find the impacts are more visible over West central, Southern and North East India. Regional atmospheric simulations coupled with Urban Canopy Model (UCM) shows that urbanization intensifies extremes in city areas, but not uniformly all over the city. The intensification occurs over specific pockets of the urban region, resulting an increase in spatial variability even within the city

Systematic review of prognostic factors predicting outcome in non-surgically treated patients with sciatica.

PubMed

Verwoerd, A J H; Luijsterburg, P A J; Lin, C W C; Jacobs, W C H; Koes, B W; Verhagen, A P

2013-09-01

Identification of prognostic factors for surgery in patients with sciatica is important to be able to predict surgery in an early stage. Identification of prognostic factors predicting persistent pain, disability and recovery are important for better understanding of the clinical course, to inform patient and physician and support decision making. Consequently, we aimed to systematically review prognostic factors predicting outcome in non-surgically treated patients with sciatica. A search of Medline, Embase, Web of Science and Cinahl, up to March 2012 was performed for prospective cohort studies on prognostic factors for non-surgically treated sciatica. Two reviewers independently selected studies for inclusion and assessed the risk of bias. Outcomes were pain, disability, recovery and surgery. A best evidence synthesis was carried out in order to assess and summarize the data. The initial search yielded 4392 articles of which 23 articles reporting on 14 original cohorts met the inclusion criteria. High clinical, methodological and statistical heterogeneity among studies was found. Reported evidence regarding prognostic factors predicting the outcome in sciatica is limited. The majority of factors that have been evaluated, e.g., age, body mass index, smoking and sensory disturbance, showed no association with outcome. The only positive association with strong evidence was found for leg pain intensity at baseline as prognostic factor for subsequent surgery. © 2013 European Federation of International Association for the Study of Pain Chapters.

Intrinsic Predictive Factors of Noncontact Lateral Ankle Sprain in Collegiate Athletes

PubMed Central

Kobayashi, Takumi; Yoshida, Masahiro; Yoshida, Makoto; Gamada, Kazuyoshi

2013-01-01

Background: Lateral ankle sprain (LAS) is one of the most common injuries in sports. Despite extensive research, intrinsic factors that predict initial and recurrent noncontact LAS remain undefined. Purpose: To identify the predictive factors of initial and recurrent noncontact LAS, focusing on ankle flexibility and/or alignment in collegiate athletes. Study Design: Case-control study; Level of evidence, 3. Methods: A total of 191 athletes were assessed during the preseason for factors predictive of noncontact LAS. The baseline measurements included weightbearing dorsiflexion range of motion (ROM), leg-heel angle, foot internal rotation angle in plantar flexion, classification according to the mortise test, and navicular–medial malleolus (NMM) distance. Occurrence of noncontact LAS and participation in practice and games were prospectively recorded for 11 months. Results: Of the 191 athletes assessed, 169 (145 males, 24 females) completed the study; 125 athletes had a history of ankle sprain. During the observational period, 16 athletes suffered noncontact LAS (0.58 per 1000 athlete-exposures) consisting of 4 initial sprains and 12 recurrences. The hazard ratio estimated by a Cox regression analysis showed that athletes with an NMM distance ≥4.65 cm were 4.14 times more likely to suffer an initial noncontact LAS than were athletes with a shorter NMM distance (95% confidence interval, 1.12-14.30) and that athletes with a weightbearing dorsiflexion ROM >49.5° were 1.12 times as likely to suffer a recurrent noncontact LAS compared with athletes with a lower ROM (95% confidence interval, 1.05-1.20). Conclusion: NMM distance predicts initial noncontact LAS, and weightbearing dorsiflexion ROM predicts recurrent noncontact LAS. PMID:26535263

Study on predictive role of AR and EGFR family genes with response to neoadjuvant chemotherapy in locally advanced breast cancer in Indian women.

PubMed

Singh, L C; Chakraborty, Anurupa; Mishra, Ashwani K; Devi, Thoudam Regina; Sugandhi, Nidhi; Chintamani, Chintamani; Bhatnagar, Dinesh; Kapur, Sujala; Saxena, Sunita

2012-06-01

Locally advanced breast cancer (LABC) remains a clinical challenge as the majority of patients with this diagnosis develop distant metastases despite appropriate therapy. We analyzed expression of steroid and growth hormone receptor genes as well as gene associated with metabolism of chemotherapeutic drugs in locally advanced breast cancer before and after neoadjuvant chemotherapy (NACT) to study whether there is a change in gene expression induced by chemotherapy and whether such changes are associated with tumor response or non-response. Fifty patients were included with locally advanced breast cancer treated with cyclophosphamide, adriamycin, 5-fluorouracil (CAF)-based neoadjuvant chemotherapy before surgery. Total RNA was extracted from 50 match samples of pre- and post-NACT tumor tissues. RNA expression levels of epidermal growth factor receptor family genes including EGFR, ERBB2, ERBB3, androgen receptor (AR), and multidrug-resistance gene 1 (MDR1) were determined by quantitative real-time reverse transcriptase-polymerase chain reaction. Responders show significantly high levels of pre-NACT AR gene expression (P = 0.016), which reduces following NACT (P = 0.008), and hence can serve as a useful tool for the prediction of the success of neoadjuvant chemotherapy in individual cancer patients with locally advanced breast carcinoma. Moreover, a significant post-therapeutic increase in the expression levels of EGFR and MDR1 gene in responders (P = 0.026 and P < 0.001) as well as in non-responders (P = 0.055, P = 0.001) suggests that expression of these genes changes during therapy but they do not have any impact on tumor response, whereas a post-therapeutic reduction was observed in AR in responders. This indicates an independent predictive role of AR with response to NACT.

Treatment decision-making by men with localized prostate cancer: the influence of personal factors.

PubMed

Berry, Donna L; Ellis, William J; Woods, Nancy Fugate; Schwien, Christina; Mullen, Kristin H; Yang, Claire

2003-01-01

For many men with localized prostate cancer, there is no definite answer or unequivocal choice regarding treatment modality. This high-stakes treatment decision is made in the context of great uncertainty. The purpose of this study is to systematically document meaningful and relevant aspects of treatment decision-making reported by men with localized prostate cancer. Focus groups and individual interviews were conducted with 44 men who were within 6 months of a diagnosis of localized prostate cancer. Using content analysis and grounded theory analytic techniques, major aspects and processes of men's treatment decision making are identified and described. The participants reported their experiences beginning with influential personal history factors, followed by detailed descriptions of information gathering and the important influence of expected treatment outcomes and other individuals' cancer histories and/or shared opinions. Twenty of the 44 (45%) participants relied heavily on the influence of another's opinion or history to finalize a decision, yet only 10 of the 44 (22.7%) reported this individual to be their physician. A common process, "making the best choice for me" was explicated. Clinicians assume that men are making rational treatment decisions based on reliable information, yet this study documents a different reality. Patient education about medical therapies and the patients' own medical factors is not enough. A clinic visit dialogue that brings personal factors to the conversation along with medical factors can guide a man to making his "best choice" for localized prostate cancer.

Local and Systemic Factors and Implantation: what is the Evidence?

PubMed Central

Fox, Chelsea; Morin, Scott; Jeong, Jae-Wook; Scott, Richard T.; Lessey, Bruce A

2016-01-01

Significant progress has been made in the understanding of embryonic competence and endometrial receptivity since the inception of Assisted Reproductive Technologies (ART). The endometrium is a highly dynamic tissue that plays a crucial role in the establishment and maintenance of normal pregnancy. In response to steroid sex hormones, the endometrium undergoes marked changes during the menstrual cycle that are critical for acceptance of the nascent embryo. There is also a wide body of literature on systemic factors that impact ART outcomes. Patient prognosis is impacted by an array of factors that tip the scales in her favor or against success. Recognizing the local and systemic factors will allow clinicians to better understand and optimize the maternal environment at the time of implantation. This review will address the current literature on endometrial and systemic factors related to impaired implantation and highlight recent advances in this area of reproductive medicine. PMID:26945096

Development and Preliminary Performance of a Risk Factor Screen to Predict Posttraumatic Psychological Disorder After Trauma Exposure

PubMed Central

Carlson, Eve B.; Palmieri, Patrick A.; Spain, David A.

2017-01-01

Objective We examined data from a prospective study of risk factors that increase vulnerability or resilience, exacerbate distress, or foster recovery to determine whether risk factors accurately predict which individuals will later have high posttraumatic (PT) symptom levels and whether brief measures of risk factors also accurately predict later symptom elevations. Method Using data from 129 adults exposed to traumatic injury of self or a loved one, we conducted receiver operating characteristic (ROC) analyses of 14 risk factors assessed by full-length measures, determined optimal cutoff scores and calculated predictive performance for the nine that were most predictive. For five risk factors, we identified sets of items that accounted for 90% of variance in total scores and calculated predictive performance for sets of brief risk measures. Results A set of nine risk factors assessed by full measures identified 89% of those who later had elevated PT symptoms (sensitivity) and 78% of those who did not (specificity). A set of four brief risk factor measures assessed soon after injury identified 86% of those who later had elevated PT symptoms and 72% of those who did not. Conclusions Use of sets of brief risk factor measures shows promise of accurate prediction of PT psychological disorder and probable PTSD or depression. Replication of predictive accuracy is needed in a new and larger sample. PMID:28622811

Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal

PubMed Central

Lewis, Jesse S.; Farnsworth, Matthew L.; Burdett, Chris L.; Theobald, David M.; Gray, Miranda; Miller, Ryan S.

2017-01-01

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. PMID:28276519

Vascular endothelial growth factor (VEGF) expression in locally advanced prostate cancer: secondary analysis of radiation therapy oncology group (RTOG) 8610.

PubMed

Pan, Larry; Baek, Seunghee; Edmonds, Pamela R; Roach, Mack; Wolkov, Harvey; Shah, Satish; Pollack, Alan; Hammond, M Elizabeth; Dicker, Adam P

2013-04-25

Angiogenesis is a key element in solid-tumor growth, invasion, and metastasis. VEGF is among the most potent angiogenic factor thus far detected. The aim of the present study is to explore the potential of VEGF (also known as VEGF-A) as a prognostic and predictive biomarker among men with locally advanced prostate cancer. The analysis was performed using patients enrolled on RTOG 8610, a phase III randomized control trial of radiation therapy alone (Arm 1) versus short-term neoadjuvant and concurrent androgen deprivation and radiation therapy (Arm 2) in men with locally advanced prostate carcinoma. Tissue samples were obtained from the RTOG tissue repository. Hematoxylin and eosin slides were reviewed, and paraffin blocks were immunohistochemically stained for VEGF expression and graded by Intensity score (0-3). Cox or Fine and Gray's proportional hazards models were used. Sufficient pathologic material was available from 103 (23%) of the 456 analyzable patients enrolled in the RTOG 8610 study. There were no statistically significant differences in the pre-treatment characteristics between the patient groups with and without VEGF intensity data. Median follow-up for all surviving patients with VEGF intensity data is 12.2 years. Univariate and multivariate analyses demonstrated no statistically significant correlation between the intensity of VEGF expression and overall survival, distant metastasis, local progression, disease-free survival, or biochemical failure. VEGF expression was also not statistically significantly associated with any of the endpoints when analyzed by treatment arm. This study revealed no statistically significant prognostic or predictive value of VEGF expression for locally advanced prostate cancer. This analysis is among one of the largest sample bases with long-term follow-up in a well-characterized patient population. There is an urgent need to establish multidisciplinary initiatives for coordinating further research in the area of human

Factors affecting species distribution predictions: A simulation modeling experiment

Treesearch

Gordon C. Reese; Kenneth R. Wilson; Jennifer A. Hoeting; Curtis H. Flather

2005-01-01

Geospatial species sample data (e.g., records with location information from natural history museums or annual surveys) are rarely collected optimally, yet are increasingly used for decisions concerning our biological heritage. Using computer simulations, we examined factors that could affect the performance of autologistic regression (ALR) models that predict species...

Risk analysis factors for local recurrence in Ewing's sarcoma: when should adjuvant radiotherapy be administered?

PubMed

Albergo, J I; Gaston, C L L; Parry, M C; Laitinen, M K; Jeys, L M; Tillman, R M; Abudu, A T; Grimer, R J

2018-02-01

The aim of this study was to analyse a group of patients with non-metastatic Ewing's sarcoma at presentation and identify prognostic factors affecting the development of local recurrence, in order to assess the role of radiotherapy. A retrospective review of all patients with a Ewing's sarcoma treated between 1980 and 2012 was carried out. Only those treated with chemotherapy followed by surgery and/or radiotherapy were included. Patients were grouped according to site (central or limb) for further analysis of the prognostic factors. A total of 388 patients were included in the study. Of these, 60 (15%) developed local recurrence at a mean median of 27 months (sd 24, range 7 to 150) and the five-year local recurrence-free survival (5yrLRFS) was 83%. For central tumours, the size of the tumour and histological response to chemotherapy were found to be significant factors for local recurrence. For limb tumours, local recurrence was affected by intralesional and marginal resections, but not by the histological response to chemotherapy. Radiotherapy in those with a marginal resection reduced the risk of local recurrence (5yrLRFS: 96% versus 81%, p = 0.044). Local recurrence significantly affects the overall survival in patients with a Ewing's sarcoma. For those with a tumour in a limb, radiotherapy reduced the risk of local recurrence, especially in those with a marginal margin of excision, but the effect in central tumours was less clear. Radiotherapy for those who have had a wide margin of resection does not reduce the risk of local recurrence, regardless of the histological response to chemotherapy. Cite this article: Bone Joint J 2018;100-B: 247-55. ©2018 The British Editorial Society of Bone & Joint Surgery.

Predicting Transition from Laminar to Turbulent Flow over a Surface

NASA Technical Reports Server (NTRS)

Sturdza, Peter (Inventor); Rajnarayan, Dev (Inventor)

2013-01-01

A prediction of whether a point on a computer-generated surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of boundary-layer properties at the point are obtained from a steady-state solution of a fluid flow in a region adjacent to the point. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For each instability mode in the plurality of instability modes, a covariance vector is determined, which is the covariance of a predicted local growth rate with the known instability growth rates. Each covariance vector is used with the vector of regressor weights to determine a predicted local growth rate at the point. Based on the predicted local growth rates, an n-factor envelope at the point is determined.

Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing

PubMed Central

Ilovitsh, Tali; Meiri, Amihai; Ebeling, Carl G.; Menon, Rajesh; Gerton, Jordan M.; Jorgensen, Erik M.; Zalevsky, Zeev

2013-01-01

Localization of a single fluorescent particle with sub-diffraction-limit accuracy is a key merit in localization microscopy. Existing methods such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) achieve localization accuracies of single emitters that can reach an order of magnitude lower than the conventional resolving capabilities of optical microscopy. However, these techniques require a sparse distribution of simultaneously activated fluorophores in the field of view, resulting in larger time needed for the construction of the full image. In this paper we present the use of a nonlinear image decomposition algorithm termed K-factor, which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique, when implemented on raw data prior to localization, can improve the localization accuracy of standard existing methods, and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores, demonstrate an improvement of up to 85% in the localization precision compared to single fitting techniques. Implementing the proposed concept on experimental data of cellular structures yielded a 37% improvement in resolution for the same super-resolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution. PMID:24466491

Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, Clarence C., Jr.

1989-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 deg and +/- 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was develolped to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Far-field strains at failure were calculated from the strain intensity factor, and then strengths were calculated from the far-field strains using uniaxial stress-strain curves. The predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only +/- 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.

Stress factors predicting injuries of hospital personnel.

PubMed

Salminen, Simo; Kivimäki, Mika; Elovainio, Marko; Vahtera, Jussi

2003-07-01

Stress at work has long been recognized as a factor in increasing risk for mental and physical health problems. The extent to which work stressors and stress predicted injuries occur in a large population of Finnish hospital workers was studied. A total of 5,111 employees (624 men, 4,487 women) from 10 hospitals participated in this study. Their psychological distress was measured by the General Health Questionnaire, and overload and job control by the Harris scale and the Job Content Questionnaire, respectively. Injuries certified by a physician were followed up for 3 years: injuries in 1997 (n = 213) were used as a measure of baseline and injuries in 1998-1999 (n = 443) were the dependent variables. Psychological distress was not significantly related to injuries. However, low decision latitude (risk ratio = 1.27 (1.04 to 1.54)), low skill discretion only for men (risk ratio = 2.76 (1.78 to 4.30)), and highly monotonous work (risk ratio = 1.26 (1.02 to 1.55)) were stressors predicting injuries. In addition, workers with numerous problems in interpersonal relationships (risk ratio = 1.43 (1.18 to 1.73)) or many conflicts in collaboration at work (risk ratio = 1.40 (1.15 to 1.71)) were more often involved in injuries. This study showed that stressors related to autonomy of work and interpersonal relationship at workplace are predictors of injuries in hospital settings. These factors are potentially amenable to organizational interventions. Copyright 2003 Wiley-Liss, Inc.

Horticultural activity predicts later localized limb status in a contemporary pre-industrial population.

PubMed

Stieglitz, Jonathan; Trumble, Benjamin C; Kaplan, Hillard; Gurven, Michael

2017-07-01

Modern humans may have gracile skeletons due to low physical activity levels and mechanical loading. Tests using pre-historic skeletons are limited by the inability to assess behavior directly, while modern industrialized societies possess few socio-ecological features typical of human evolutionary history. Among Tsimane forager-horticulturalists, we test whether greater activity levels and, thus, increased loading earlier in life are associated with greater later-life bone status and diminished age-related bone loss. We used quantitative ultrasonography to assess radial and tibial status among adults aged 20+ years (mean ± SD age = 49 ± 15; 52% female). We conducted systematic behavioral observations to assess earlier-life activity patterns (mean time lag between behavioural observation and ultrasound = 12 years). For a subset of participants, physical activity was again measured later in life, via accelerometry, to determine whether earlier-life time use is associated with later-life activity levels. Anthropometric and demographic data were collected during medical exams. Structural decline with age is reduced for the tibia (female: -0.25 SDs/decade; male: 0.05 SDs/decade) versus radius (female: -0.56 SDs/decade; male: -0.20 SDs/decade), which is expected if greater loading mitigates bone loss. Time allocation to horticulture, but not hunting, positively predicts later-life radial status (β Horticulture  = 0.48, p = 0.01), whereas tibial status is not significantly predicted by subsistence or sedentary leisure participation. Patterns of activity- and age-related change in bone status indicate localized osteogenic responses to loading, and are generally consistent with the logic of bone functional adaptation. Nonmechanical factors related to subsistence lifestyle moderate the association between activity patterns and bone structure. © 2017 Wiley Periodicals, Inc.

Towards a Predictive Capability for Local Helicity Injection Startup

NASA Astrophysics Data System (ADS)

Barr, J. L.; Bongard, M. W.; Burke, M. G.; Fonck, R. J.; Hinson, E. T.; Lewicki, B. T.; Perry, J. M.; Redd, A. J.; Schlossberg, D. J.

2014-10-01

Local helicity injection (LHI) is a non-solenoidal tokamak startup technique under development on the Pegasus ST. New designs of the injector cathode geometry and plasma-facing shield rings support high-voltage operation up to 1.5 kV. This leads to reduced requirements in injector area for a given helicity input rate. Near-term experiments in Pegasus are testing the gain in Ip obtained with a 1 . 5 × increase in the helicity input rate and the efficacy of helicity injection in the lower divertor region. A predictive model for LHI is needed to project scalable scenarios for larger devices. A lumped-parameter circuit model using power and helicity balance is being developed for LHI on Pegasus-U and NSTX-U. The model indicates that MA-class startup on NSTX-U will require operating in a regime where the drive from LHI dominates the inductive effects arising from dynamically evolving plasma geometry. The physics of this new regime can be tested in Pegasus-U at Ip ~ 0 . 3 MA. The LHI systems on the proposed Pegasus-U will be expanded to provide 3 - 4 × helicity injection rate and the toroidal field doubled to reach this regime. Predictive models to be validated on Pegasus-U include the 0-D power balance model, NIMROD, and TSC. Work supported by US DOE Grants DE-FG02-96ER54375 and DE-SC0006928.

A Bayesian network approach for modeling local failure in lung cancer

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Craft, Jeffrey; Lozi, Rawan Al; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O.; Bradley, Jeffrey D.; El Naqa, Issam

2011-03-01

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

Factors influencing U.S. canine heartworm (Dirofilaria immitis) prevalence.

PubMed

Wang, Dongmei; Bowman, Dwight D; Brown, Heidi E; Harrington, Laura C; Kaufman, Phillip E; McKay, Tanja; Nelson, Charles Thomas; Sharp, Julia L; Lund, Robert

2014-06-06

This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates. The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence. All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist. The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence.

Predicting the cosmological constant with the scale-factor cutoff measure

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

De Simone, Andrea; Guth, Alan H.; Salem, Michael P.

2008-09-15

It is well known that anthropic selection from a landscape with a flat prior distribution of cosmological constant {lambda} gives a reasonable fit to observation. However, a realistic model of the multiverse has a physical volume that diverges with time, and the predicted distribution of {lambda} depends on how the spacetime volume is regulated. A very promising method of regulation uses a scale-factor cutoff, which avoids a number of serious problems that arise in other approaches. In particular, the scale-factor cutoff avoids the 'youngness problem' (high probability of living in a much younger universe) and the 'Q and G catastrophes'more » (high probability for the primordial density contrast Q and gravitational constant G to have extremely large or small values). We apply the scale-factor cutoff measure to the probability distribution of {lambda}, considering both positive and negative values. The results are in good agreement with observation. In particular, the scale-factor cutoff strongly suppresses the probability for values of {lambda} that are more than about 10 times the observed value. We also discuss qualitatively the prediction for the density parameter {omega}, indicating that with this measure there is a possibility of detectable negative curvature.« less

Local thermal sensation modeling-a review on the necessity and availability of local clothing properties and local metabolic heat production.

PubMed

Veselá, S; Kingma, B R M; Frijns, A J H

2017-03-01

Local thermal sensation modeling gained importance due to developments in personalized and locally applied heating and cooling systems in office environments. The accuracy of these models depends on skin temperature prediction by thermophysiological models, which in turn rely on accurate environmental and personal input data. Environmental parameters are measured or prescribed, but personal factors such as clothing properties and metabolic rates have to be estimated. Data for estimating the overall values of clothing properties and metabolic rates are available in several papers and standards. However, local values are more difficult to retrieve. For local clothing, this study revealed that full and consistent data sets are not available in the published literature for typical office clothing sets. Furthermore, the values for local heat production were not verified for characteristic office activities, but were adapted empirically. Further analyses showed that variations in input parameters can lead to local skin temperature differences (∆T skin,loc  = 0.4-4.4°C). These differences can affect the local sensation output, where ∆T skin,loc  = 1°C is approximately one step on a 9-point thermal sensation scale. In conclusion, future research should include a systematic study of local clothing properties and the development of feasible methods for measuring and validating local heat production. © 2016 The Authors. Indoor Air published by John Wiley & Sons Ltd.

Validation of a nomogram in the prediction of local recurrence risks after conserving surgery for Asian women with ductal carcinoma in situ of the breast.

PubMed

Wang, F; Li, H; Tan, P H; Chua, E T; Yeo, R M C; Lim, F L W T; Kim, S W; Tan, D Y H; Wong, F Y

2014-11-01

At our centre, ductal carcinoma in situ (DCIS) was commonly treated with breast-conservation therapy (BCT). Local recurrence after BCT is a major concern. The aims of our study were to review the outcomes of DCIS treatment in our patients and to evaluate a nomogram from Memorial Sloan Kettering Cancer Centre (MSKCC) for predicting ipsilateral breast tumour recurrence (IBTR) in our Asian population. Chart reviews of 716 patients with pure DCIS treated from 1992 to 2011 were carried out. Univariable Cox regression analyses were used to evaluate the effects of the 10 prognostic factors of the MSKCC nomogram on IBTR. We constructed a separate National Cancer Centre Singapore (NCCS) nomogram based on multivariable Cox regression via reduced model selection by applying the stopping rule of Akaike's information criterion to predict IBTR-free survival. The abilities of the NCCS nomogram and the MSKCC nomogram to predict IBTR of individual patients were evaluated with bootstrapping of 200 sets of resamples and the NCCS dataset, respectively. Harrell's c-index was calculated for each nomogram to evaluate the concordance between predicted and observed responses of individual subjects. Study patients were followed up for a median of 70 months. Over 95% of patients received adjuvant radiotherapy. The 5 and 10 year actuarial IBTR-free survival rates for the cohort were 95.5 and 92.6%, respectively. In the multivariate analysis, independent prognostic factors for IBTR included use of adjuvant endocrine therapy, presence of comedonecrosis and younger age at diagnosis. These factors formed the basis of the NCCS nomogram, which had a similar c-index (NCCS: 0.696; MSKCC: 0.673) compared with the MSKCC nomogram. The MSKCC nomogram was validated in an Asian population. A simpler NCCS nomogram using a different combination of fewer prognostic factors may be sufficient for the prediction of IBTR in Asians, but requires external validation to compare for relative performance. Copyright �

CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data

PubMed Central

O'Connor, Timothy; Bodén, Mikael

2017-01-01

Abstract Identifying the genomic regions and regulatory factors that control the transcription of genes is an important, unsolved problem. The current method of choice predicts transcription factor (TF) binding sites using chromatin immunoprecipitation followed by sequencing (ChIP-seq), and then links the binding sites to putative target genes solely on the basis of the genomic distance between them. Evidence from chromatin conformation capture experiments shows that this approach is inadequate due to long-distance regulation via chromatin looping. We present CisMapper, which predicts the regulatory targets of a TF using the correlation between a histone mark at the TF's bound sites and the expression of each gene across a panel of tissues. Using both chromatin conformation capture and differential expression data, we show that CisMapper is more accurate at predicting the target genes of a TF than the distance-based approaches currently used, and is particularly advantageous for predicting the long-range regulatory interactions typical of tissue-specific gene expression. CisMapper also predicts which TF binding sites regulate a given gene more accurately than using genomic distance. Unlike distance-based methods, CisMapper can predict which transcription start site of a gene is regulated by a particular binding site of the TF. PMID:28204599

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

PubMed

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

PubMed Central

Gerstner, Wulfram

2017-01-01

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically. PMID:29173280

Factors predictive of risk for complications in patients with oesophageal foreign bodies.

PubMed

Sung, Sang Hun; Jeon, Seong Woo; Son, Hyuk Su; Kim, Sung Kook; Jung, Min Kyu; Cho, Chang Min; Tak, Won Young; Kweon, Young Oh

2011-08-01

Reports on predictive risk factors associated with complications of ingested oesophageal foreign bodies are rare. The aim of this study was to determine the predictive risk factors associated with the complications of oesophageal foreign bodies. Three hundred sixteen cases with foreign bodies in the oesophagus were retrospectively investigated. The predictive risk factors for complications after foreign body ingestion were analysed by multivariate logistic regression, and included age, size and type of foreign body ingested, duration of impaction, and the level of foreign body impaction. The types of oesophageal foreign bodies included fish bones (37.0%), food (19.0%), and metals (18.4%). The complications associated with foreign bodies were ulcers (21.2%), lacerations (14.9%), erosions (12.0%), and perforation (1.9%). Multivariate analysis showed that the duration of impaction (p<0.001), and the type (p<0.001) and size of the foreign bodies (p<0.001) were significant independent risk factors associated with the development of complications in patients with oesophageal foreign bodies. In patients with oesophageal foreign bodies, the risk of complications was increased with a longer duration of impaction, bone type, and larger size. Copyright © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Predictive factors of telemedicine service acceptance and behavioral intention of physicians.

PubMed

Rho, Mi Jung; Choi, In Young; Lee, Jaebeom

2014-08-01

Despite the proliferation of telemedicine technology, telemedicine service acceptance has been slow in actual healthcare settings. The purpose of this research is to develop a theoretical model for explaining the predictive factors influencing physicians' willingness to use telemedicine technology to provide healthcare services. We developed the Telemedicine Service Acceptance model based on the technology acceptance model (TAM) with the inclusion of three predictive constructs from the previously published telemedicine literature: (1) accessibility of medical records and of patients as clinical factors, (2) self-efficacy as an individual factor and (3) perceived incentives as regulatory factors. A survey was conducted, and structural equation modeling was applied to evaluate the empirical validity of the model and causal relationships within the model using the data collected from 183 physicians. Our results confirmed the validity of the original TAM constructs: the perceived usefulness of telemedicine directly impacted the behavioral intention to use it, and the perceived ease of use directly impacted both the perceived usefulness and the behavioral intention to use it. In addition, new predictive constructs were found to have ramifications on TAM variables: the accessibility of medical records and of patients directly impacted the perceived usefulness of telemedicine, self-efficacy had a significant positive effect on both the perceived ease of use and the perceived usefulness of telemedicine, and perceived incentives were found to be important with respect to the intention to use telemedicine technology. This study demonstrated that the Telemedicine Service Acceptance model was feasible and could explain the acceptance of telemedicine services by physicians. These results identified important factors for increasing the involvement of physicians in telemedicine practice. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Mutation of the rice XA21 predicted nuclear localization sequence does not affect resistance to Xanthomonas oryzae pv. oryzae

DOE PAGES

Wei, Tong; Chen, Tsung-Chi; Ho, Yuen Ting; ...

2016-10-05

Background: The rice receptor kinase XA21 confers robust resistance to the bacterial pathogen Xanthomonas oryzae pv. oryzae( Xoo). We previously reported that XA21 is cleaved in transgenic plants overexpressing XA21 with a GFP tag ( Ubi-XA21-GFP) and that the released C-terminal domain is localized to the nucleus. XA21 carries a predicted nuclear localization sequence (NLS) that directs the C-terminal domain to the nucleus in transient assays, whereas alanine substitutions in the NLS disrupt the nuclear localization. Methods: To determine if the predicted NLS is required for XA21-mediated immunity in planta, we generated transgenic plants overexpressing an XA21 variant carrying themore » NLS with the same alanine substitutions ( Ubi-XA21nls-GFP). Results: Ubi- XA21nls-GFP plants displayed slightly longer lesion lengths, higher Xoo bacterial populations after inoculation and lower levels of reactive oxygen species production compared with the Ubi- XA21-GFP control plants. However, the Ubi- XA21nls-GFP plants express lower levels of protein than that observed in Ubi- XA21-GFP. Discussion: These results demonstrate that the predicted NLS is not required for XA21-mediated immunity.« less

Mutation of the rice XA21 predicted nuclear localization sequence does not affect resistance to Xanthomonas oryzae pv. oryzae.

PubMed

Wei, Tong; Chen, Tsung-Chi; Ho, Yuen Ting; Ronald, Pamela C

2016-01-01

The rice receptor kinase XA21 confers robust resistance to the bacterial pathogen Xanthomonas oryzae pv. oryzae ( Xoo ). We previously reported that XA21 is cleaved in transgenic plants overexpressing XA21 with a GFP tag ( Ubi -XA21-GFP) and that the released C-terminal domain is localized to the nucleus. XA21 carries a predicted nuclear localization sequence (NLS) that directs the C-terminal domain to the nucleus in transient assays, whereas alanine substitutions in the NLS disrupt the nuclear localization. To determine if the predicted NLS is required for XA21-mediated immunity in planta , we generated transgenic plants overexpressing an XA21 variant carrying the NLS with the same alanine substitutions ( Ubi -XA21nls-GFP). Ubi- XA21nls-GFP plants displayed slightly longer lesion lengths, higher Xoo bacterial populations after inoculation and lower levels of reactive oxygen species production compared with the Ubi- XA21-GFP control plants. However, the Ubi- XA21nls-GFP plants express lower levels of protein than that observed in Ubi- XA21-GFP. These results demonstrate that the predicted NLS is not required for XA21-mediated immunity.

Mutation of the rice XA21 predicted nuclear localization sequence does not affect resistance to Xanthomonas oryzae pv. oryzae

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

Wei, Tong; Chen, Tsung-Chi; Ho, Yuen Ting

Background: The rice receptor kinase XA21 confers robust resistance to the bacterial pathogen Xanthomonas oryzae pv. oryzae( Xoo). We previously reported that XA21 is cleaved in transgenic plants overexpressing XA21 with a GFP tag ( Ubi-XA21-GFP) and that the released C-terminal domain is localized to the nucleus. XA21 carries a predicted nuclear localization sequence (NLS) that directs the C-terminal domain to the nucleus in transient assays, whereas alanine substitutions in the NLS disrupt the nuclear localization. Methods: To determine if the predicted NLS is required for XA21-mediated immunity in planta, we generated transgenic plants overexpressing an XA21 variant carrying themore » NLS with the same alanine substitutions ( Ubi-XA21nls-GFP). Results: Ubi- XA21nls-GFP plants displayed slightly longer lesion lengths, higher Xoo bacterial populations after inoculation and lower levels of reactive oxygen species production compared with the Ubi- XA21-GFP control plants. However, the Ubi- XA21nls-GFP plants express lower levels of protein than that observed in Ubi- XA21-GFP. Discussion: These results demonstrate that the predicted NLS is not required for XA21-mediated immunity.« less

Finite Element Creep Damage Analyses and Life Prediction of P91 Pipe Containing Local Wall Thinning Defect

NASA Astrophysics Data System (ADS)

Xue, Jilin; Zhou, Changyu

2016-03-01

Creep continuum damage finite element (FE) analyses were performed for P91 steel pipe containing local wall thinning (LWT) defect subjected to monotonic internal pressure, monotonic bending moment and combined internal pressure and bending moment by orthogonal experimental design method. The creep damage lives of pipe containing LWT defect under different load conditions were obtained. Then, the creep damage life formulas were regressed based on the creep damage life results from FE method. At the same time a skeletal point rupture stress was found and used for life prediction which was compared with creep damage lives obtained by continuum damage analyses. From the results, the failure lives of pipe containing LWT defect can be obtained accurately by using skeletal point rupture stress method. Finally, the influence of LWT defect geometry was analysed, which indicated that relative defect depth was the most significant factor for creep damage lives of pipe containing LWT defect.

Predictive factors for the occurrence of idiopathic menorrhagia: evidence for a hereditary trait.

PubMed

Kuzmina, Natalia; Palmblad, Jan; Mints, Miriam

2011-01-01

The aim of the present study was to assess predictive factors for occurrence of idiopathic menorrhagia (IM), a disease characterized by abnormal endometrial blood vessel morphology. It was hypothesized that IM exhibits familial clustering (suggesting inheritance) and is associated with other vascular abnormalities, primarily cutaneous hemangiomas. Women with IM (n=152) and healthy, regularly menstruating (n=56) women answered a questionnaire concerning menstrual pattern, susceptibility to bleeding and family history of abnormal gynecological bleeding. Factor analysis with principal component extraction was used to separate predictive factors that may be associated with IM. A total of 35 different items were analyzed. A strong association was found between IM and a family history of heavy menstrual bleeding (r=0.68), but not with cutaneous vascular abnormalities. Our results revealed that a family history of heavy menstrual bleeding may have the highest predictive value for the diagnosis of IM, indicating a hereditary trait.

Predictive factors associated with neck pain in patients with cervical disc degeneration

PubMed Central

Kong, Lingde; Tian, Weifeng; Cao, Peng; Wang, Haonan; Zhang, Bing; Shen, Yong

2017-01-01

Abstract The predictive factors associated with neck pain remain unclear. We conducted a cross-sectional study to assess predictive factors, especially Modic changes (MCs), associated with the intensity and duration of neck pain in patients with cervical disc degenerative disease. We retrospectively reviewed patients in our hospital from January 2013 to December 2016. Severe neck pain (SNP) and persistent neck pain (PNP) were the 2 main outcomes, and were assessed based on the numerical rating scale (NRS). Basic data, and also imaging data, were collected and analyzed as potential predictive factors. Univariate analysis and multiple logistic regression analysis were performed to assess the predictive factors for neck pain. In all, 381 patients (193 males and 188 females) with cervical degenerative disease were included in our study. The number of patients with SNP and PNP were 94 (24.67%) and 109 (28.61%), respectively. The NRS of neck pain in patients with type 1 MCs was significantly higher than type 2 MCs (4.8 ± 0.9 vs 3.9 ± 1.1; P = .004). The multivariate logistic analysis showed that kyphosis curvature (odds ratio [OR] 1.082, 95% confidence interval [CI] 1.044–1.112), spondylolisthesis (OR 1.339, 95% CI 1.226–1.462), and annular tear (OR 1.188, 95% CI 1.021–1.382) were factors associated with SNP, whereas kyphosis curvature (OR 1.568, 95% CI 1.022–2.394), spondylolisthesis (OR 1.486, 95% CI 1.082–2.041), and MCs (OR 1.152, 95% CI 1.074–1.234) were associated with PNP. We concluded that kyphosis curvature, spondylolisthesis, and annular tear are associated with SNP, whereas kyphosis curvature, spondylolisthesis, and MCs are associated with PNP. This study supports the view that MCs can lead to a long duration of neck pain. PMID:29069048

PRISM offers a comprehensive genomic approach to transcription factor function prediction

PubMed Central

Wenger, Aaron M.; Clarke, Shoa L.; Guturu, Harendra; Chen, Jenny; Schaar, Bruce T.; McLean, Cory Y.; Bejerano, Gill

2013-01-01

The human genome encodes 1500–2000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context-relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high-quality nonredundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (predicting regulatory information from single motifs) approach obtains 2543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells. PMID:23382538

Global analysis of bacterial transcription factors to predict cellular target processes.

PubMed

Doerks, Tobias; Andrade, Miguel A; Lathe, Warren; von Mering, Christian; Bork, Peer

2004-03-01

Whole-genome sequences are now available for >100 bacterial species, giving unprecedented power to comparative genomics approaches. We have applied genome-context methods to predict target processes that are regulated by transcription factors (TFs). Of 128 orthologous groups of proteins annotated as TFs, to date, 36 are functionally uncharacterized; in our analysis we predict a probable cellular target process or biochemical pathway for half of these functionally uncharacterized TFs.

Predictive model for local scour downstream of hydrokinetic turbines in erodible channels

NASA Astrophysics Data System (ADS)

Musa, Mirko; Heisel, Michael; Guala, Michele

2018-02-01

A modeling framework is derived to predict the scour induced by marine hydrokinetic turbines installed on fluvial or tidal erodible bed surfaces. Following recent advances in bridge scour formulation, the phenomenological theory of turbulence is applied to describe the flow structures that dictate the equilibrium scour depth condition at the turbine base. Using scaling arguments, we link the turbine operating conditions to the flow structures and scour depth through the drag force exerted by the device on the flow. The resulting theoretical model predicts scour depth using dimensionless parameters and considers two potential scenarios depending on the proximity of the turbine rotor to the erodible bed. The model is validated at the laboratory scale with experimental data comprising the two sediment mobility regimes (clear water and live bed), different turbine configurations, hydraulic settings, bed material compositions, and migrating bedform types. The present work provides future developers of flow energy conversion technologies with a physics-based predictive formula for local scour depth beneficial to feasibility studies and anchoring system design. A potential prototype-scale deployment in a large sandy river is also considered with our model to quantify how the expected scour depth varies as a function of the flow discharge and rotor diameter.

Predictiveness of Disease Risk in a Global Outreach Tourist Setting in Thailand Using Meteorological Data and Vector-Borne Disease Incidences

PubMed Central

Ninphanomchai, Suwannapa; Chansang, Chitti; Hii, Yien Ling; Rocklöv, Joacim; Kittayapong, Pattamaporn

2014-01-01

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate. PMID:25325356

Predictiveness of disease risk in a global outreach tourist setting in Thailand using meteorological data and vector-borne disease incidences.

PubMed

Ninphanomchai, Suwannapa; Chansang, Chitti; Hii, Yien Ling; Rocklöv, Joacim; Kittayapong, Pattamaporn

2014-10-16

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate.

Clinical and histopathological factors affecting failed sentinel node localization in axillary staging for breast cancer.

PubMed

Dordea, Matei; Colvin, Hugh; Cox, Phil; Pujol Nicolas, Andrea; Kanakala, Venkat; Iwuchukwu, Obi

2013-04-01

Sentinel lymph node biopsy (SLNB) has become the standard of care in axillary staging of clinically node-negative breast cancer patients. To analyze reasons for failure of SLN localization by means of a multivariate analysis of clinical and histopathological factors. We performed a review of 164 consecutive breast cancer patients who underwent SLNB. A superficial injection technique was used. 9/164 patients failed to show nodes. In 7/9 patients no evidence of radioactivity or blue dye was observed. Age and nodal status were the only statistically significant factors (p < 0.05). For every unit increase in age there was a 9% reduced chance of failed SLN localization. Patients with negative nodal status have 90% reduced risk of failed sentinel node localization than patients with macro or extra capsular nodal invasion. The results suggest that altered lymphatic dynamics secondary to tumour burden may play a role in failed sentinel node localization. We showed that in all failed localizations the radiocolloid persisted around the injection site, showing limited local diffusion only. While clinical and histopathological data may provide some clues as to why sentinel node localization fails, we further hypothesize that integrity of peri-areolar lymphatics is important for successful localization. Copyright © 2012 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

Prediction of subcellular localization of eukaryotic proteins using position-specific profiles and neural network with weighted inputs.

PubMed

Zou, Lingyun; Wang, Zhengzhi; Huang, Jiaomin

2007-12-01

Subcellular location is one of the key biological characteristics of proteins. Position-specific profiles (PSP) have been introduced as important characteristics of proteins in this article. In this study, to obtain position-specific profiles, the Position Specific Iterative-Basic Local Alignment Search Tool (PSI-BLAST) has been used to search for protein sequences in a database. Position-specific scoring matrices are extracted from the profiles as one class of characteristics. Four-part amino acid compositions and 1st-7th order dipeptide compositions have also been calculated as the other two classes of characteristics. Therefore, twelve characteristic vectors are extracted from each of the protein sequences. Next, the characteristic vectors are weighed by a simple weighing function and inputted into a BP neural network predictor named PSP-Weighted Neural Network (PSP-WNN). The Levenberg-Marquardt algorithm is employed to adjust the weight matrices and thresholds during the network training instead of the error back propagation algorithm. With a jackknife test on the RH2427 dataset, PSP-WNN has achieved a higher overall prediction accuracy of 88.4% rather than the prediction results by the general BP neural network, Markov model, and fuzzy k-nearest neighbors algorithm on this dataset. In addition, the prediction performance of PSP-WNN has been evaluated with a five-fold cross validation test on the PK7579 dataset and the prediction results have been consistently better than those of the previous method on the basis of several support vector machines, using compositions of both amino acids and amino acid pairs. These results indicate that PSP-WNN is a powerful tool for subcellular localization prediction. At the end of the article, influences on prediction accuracy using different weighting proportions among three characteristic vector categories have been discussed. An appropriate proportion is considered by increasing the prediction accuracy.

Comorbidity, Use of Common Medications, and Risk of Early Death in Patients with Localized or Locally Advanced Prostate Cancer

PubMed Central

Nieder, Carsten; Dalhaug, Astrid; Pawinski, Adam; Aandahl, Gro; Norum, Jan

2011-01-01

In this paper, we analyze predictive factors for early death from comorbidity (defined as death within 3 years from diagnosis and unrelated to prostate cancer) in patients with localized or locally advanced prostate cancer. Such information may guide individually tailored treatment or observation strategies, and help to avoid overtreatment. We retrospectively analyzed baseline parameters including information on comorbidity and medication use among 177 patients (median age at diagnosis 70 years). Actuarial survival analyses were performed. During the first 3 years, two patients (1.1%) died from progressive prostate cancer after they had developed distant metastases. The risk of dying from other causes (3.4%) was numerically higher, although not to a statistically significant degree. Six patients who died from other causes had age-adjusted Charlson comorbidity index (CCI) scores ≥5 (CCI is a sum score where each comorbid condition is assigned with a score depending on the risk of dying associated with this condition). The main comorbidity was cardiovascular disease. The two statistically significant predictive factors were medication use and age-adjusted CCI score ≥5 (univariate analysis). However, medication use was not an independent factor as all patients with age-adjusted CCI score ≥5 also used at least one class of medication. Median survival was 30 months in patients with age-adjusted CCI score ≥5. Prediction of non-prostate cancer death may be important to prevent overtreatment in patients who are more threatened by comorbidity. Our data suggest that simple parameters such as use of medications vs. none, or presence of serious cardiac disease vs. none, are not sufficient, and that age-adjusted CCI scores outperform the other factors included in our analysis. PMID:21666987

Radiofrequency ablation for hepatocellular carcinoma measuring 2 cm or smaller: results and risk factors for local recurrence.

PubMed

Kono, Masashi; Inoue, Tatsuo; Kudo, Masatoshi; Chishina, Hirokazu; Arizumi, Tadaaki; Takita, Masahiro; Kitai, Satoshi; Yada, Norihisa; Hagiwara, Satoru; Minami, Yasunori; Ueshima, Kazuomi; Nishida, Naoshi; Murakami, Takamichi

2014-01-01

The purpose of this study was to evaluate the risk factors for local recurrence with radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) measuring ≤2 cm. This study involved 234 patients with 274 HCCs measuring ≤2 cm who had undergone RFA as the initial treatment. The mean tumor diameter was 1.478 cm. The median follow-up period was 829 days. We evaluated the post-RFA cumulative local recurrence rate and analyzed the risk factors contributing to clinical outcomes. Cumulative local recurrence rates were 9, 19 and 19% at 1, 2 and 3 years, respectively. Among the 145 cases with a complete safety margin (SM) after RFA, only 4 developed local tumor recurrence and the cumulative rates of local tumor recurrence at 1, 2 and 3 years were 2, 3 and 3%, respectively. Among the 129 cases with incomplete SM, local tumor recurrence developed in 34 and the cumulative rates of local tumor progression at 1, 2 and 3 years were 14, 36 and 36%, respectively. In multivariate analysis, significant risk factors were tumor location (liver surface), irregular gross type and SM <5 mm. Even with HCC measuring ≤2 cm, location and gross type of tumor should be carefully evaluated before RFA is performed.

Factors predicting mortality in severe acute pancreatitis.

PubMed

Compañy, L; Sáez, J; Martínez, J; Aparicio, J R; Laveda, R; Griñó, P; Pérez-Mateo, M

2003-01-01

Acute pancreatitis (AP) is a common disorder in which ensuing serious complications may lead to a fatal outcome in patients. To describe a large series of patients with severe AP (SAP) who were admitted to our hospital and to identify factors predicting mortality. In a retrospective study, all patients with SAP diagnosed between February 1996 and October 2000 according to the Atlanta criteria were studied. Out of a total of 363 AP patients, 67 developed SAP. The mean age of the patients was 69; the commonest etiology was biliary; 55.2% developed necrosis; the commonest systemic complication was respiratory failure (44.7%), followed by acute renal failure (35.8%) and shock (20.9%). A total of 31.3% of the patients died. Factors significantly related to mortality were age, upper digestive tract bleeding, acute renal failure, respiratory failure and shock by univariate analysis. However, pseudocysts seemed to have a protective effect. By multivariate analysis, independent prognostic factors were age, acute renal failure and respiratory failure. Patients with SAP mainly died due to systemic complications, especially acute renal failure and respiratory failure. Necrosis (in the absence or presence of infection) was not correlated with increased mortality. A pseudocyst was found to be a protective factor, probably because the definition itself led to the selection of patients who had survived multiorgan failure. Copyright 2003 S. Karger AG, Basel and IAP

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

Risk factors predict post-traumatic stress disorder differently in men and women

PubMed Central

Christiansen, Dorte M; Elklit, Ask

2008-01-01

Background About twice as many women as men develop post-traumatic stress disorder (PTSD), even though men as a group are exposed to more traumatic events. Exposure to different trauma types does not sufficiently explain why women are more vulnerable. Methods The present work examines the effect of age, previous trauma, negative affectivity (NA), anxiety, depression, persistent dissociation, and social support on PTSD separately in men and women. Subjects were exposed to either a series of explosions in a firework factory near a residential area or to a high school stabbing incident. Results Some gender differences were found in the predictive power of well known risk factors for PTSD. Anxiety predicted PTSD in men, but not in women, whereas the opposite was found for depression. Dissociation was a better predictor for PTSD in women than in men in the explosion sample but not in the stabbing sample. Initially, NA predicted PTSD better in women than men in the explosion sample, but when compared only to other significant risk factors, it significantly predicted PTSD for both men and women in both studies. Previous traumatic events and age did not significantly predict PTSD in either gender. Conclusion Gender differences in the predictive value of social support on PTSD appear to be very complex, and no clear conclusions can be made based on the two studies included in this article. PMID:19017412

Perceived threat and corroboration: key factors that improve a predictive model of trust in internet-based health information and advice.

PubMed

Harris, Peter R; Sillence, Elizabeth; Briggs, Pam

2011-07-27

How do people decide which sites to use when seeking health advice online? We can assume, from related work in e-commerce, that general design factors known to affect trust in the site are important, but in this paper we also address the impact of factors specific to the health domain. The current study aimed to (1) assess the factorial structure of a general measure of Web trust, (2) model how the resultant factors predicted trust in, and readiness to act on, the advice found on health-related websites, and (3) test whether adding variables from social cognition models to capture elements of the response to threatening, online health-risk information enhanced the prediction of these outcomes. Participants were asked to recall a site they had used to search for health-related information and to think of that site when answering an online questionnaire. The questionnaire consisted of a general Web trust questionnaire plus items assessing appraisals of the site, including threat appraisals, information checking, and corroboration. It was promoted on the hungersite.com website. The URL was distributed via Yahoo and local print media. We assessed the factorial structure of the measures using principal components analysis and modeled how well they predicted the outcome measures using structural equation modeling (SEM) with EQS software. We report an analysis of the responses of participants who searched for health advice for themselves (N = 561). Analysis of the general Web trust questionnaire revealed 4 factors: information quality, personalization, impartiality, and credible design. In the final SEM model, information quality and impartiality were direct predictors of trust. However, variables specific to eHealth (perceived threat, coping, and corroboration) added substantially to the ability of the model to predict variance in trust and readiness to act on advice on the site. The final model achieved a satisfactory fit: χ(2) (5) = 10.8 (P = .21), comparative fit

Perceived Threat and Corroboration: Key Factors That Improve a Predictive Model of Trust in Internet-based Health Information and Advice

PubMed Central

Harris, Peter R; Briggs, Pam

2011-01-01

Background How do people decide which sites to use when seeking health advice online? We can assume, from related work in e-commerce, that general design factors known to affect trust in the site are important, but in this paper we also address the impact of factors specific to the health domain. Objective The current study aimed to (1) assess the factorial structure of a general measure of Web trust, (2) model how the resultant factors predicted trust in, and readiness to act on, the advice found on health-related websites, and (3) test whether adding variables from social cognition models to capture elements of the response to threatening, online health-risk information enhanced the prediction of these outcomes. Methods Participants were asked to recall a site they had used to search for health-related information and to think of that site when answering an online questionnaire. The questionnaire consisted of a general Web trust questionnaire plus items assessing appraisals of the site, including threat appraisals, information checking, and corroboration. It was promoted on the hungersite.com website. The URL was distributed via Yahoo and local print media. We assessed the factorial structure of the measures using principal components analysis and modeled how well they predicted the outcome measures using structural equation modeling (SEM) with EQS software. Results We report an analysis of the responses of participants who searched for health advice for themselves (N = 561). Analysis of the general Web trust questionnaire revealed 4 factors: information quality, personalization, impartiality, and credible design. In the final SEM model, information quality and impartiality were direct predictors of trust. However, variables specific to eHealth (perceived threat, coping, and corroboration) added substantially to the ability of the model to predict variance in trust and readiness to act on advice on the site. The final model achieved a satisfactory fit: χ2 5 = 10

Differentiated thyroid cancer in children: Heterogeneity of predictive risk factors.

PubMed

Russo, Marco; Malandrino, Pasqualino; Moleti, Mariacarla; Vermiglio, Francesco; D'Angelo, Antonio; La Rosa, Giuliana; Sapuppo, Giulia; Calaciura, Francesca; Regalbuto, Concetto; Belfiore, Antonino; Vigneri, Riccardo; Pellegriti, Gabriella

2018-05-16

To correlate clinical and pathological characteristics at diagnosis with patient long-term outcomes and to evaluate ongoing risk stratifications in a large series of paediatric differentiated thyroid cancers (DTC). Retrospective analysis of clinical and pathological prognostic factors of 124 paediatric patients with DTC (age at diagnosis <19 years) followed up for 10.4 ± 8.4 years. Patients with a follow-up >3 years (n = 104) were re-classified 18 months after surgery on the basis of their response to therapy (ongoing risk stratification). Most patients had a papillary histotype (96.0%), were older than 15 years (75.0%) and were diagnosed because of clinical local symptoms (63.7%). Persistent/recurrent disease was present in 31.5% of cases during follow-up, but at the last evaluation, only 12.9% had biochemical or structural disease. The presence of metastases in the lymph nodes of the lateral compartment (OR 3.2, 95% CI, 1.28-7.16, P = 0.01) was the only independent factor associated with recurrent/persistent disease during follow-up. At the last evaluation, biochemical/structural disease was associated with node metastases (N1a, N1b) by univariate but not multivariate analysis. Ongoing risk stratification compared to the initial risk classification method better identified patients with a lower probability of persistent/recurrent disease (NPV = 100%). In spite of the aggressive presentations at diagnosis, paediatric patients with DTC show an excellent response to treatment and often a favourable outcome. N1b status should be considered a strong predictor of persistent/recurrent disease which, as in adults, is better predicted by ongoing risk stratification. © 2018 Wiley Periodicals, Inc.

Geothermal modelling and geoneutrino flux prediction at JUNO with local heat production data

NASA Astrophysics Data System (ADS)

Xi, Y.; Wipperfurth, S. A.; McDonough, W. F.; Sramek, O.; Roskovec, B.; He, J.

2017-12-01

Geoneutrinos are mostly electron antineutrinos created from natural radioactive decays in the Earth's interior. Measurement of a geoneutrino flux at near surface detector can lead to a better understanding of the composition of the Earth, inform about chemical layering in the mantle, define the power driving mantle convection and plate tectonics, and reveal the energy supplying the geodynamo. JUNO (Jiangmen Underground Neutrino Observatory) is a 20 kton liquid scintillator detector currently under construction with an expected start date in 2020. Due to its enormous mass, JUNO will detect about 400 geoneutrinos per year, making it an ideal tool to study the Earth. JUNO is located on the passive continental margin of South China, where there is an extensive continental shelf. The continental crust surrounding the JUNO detector is between 26 and 32 km thick and represents the transition between the southern Eurasian continental plate and oceanic plate of the South China Sea.We seek to predict the geoneutrino flux at JUNO prior to data taking and announcement of the particle physics measurement. To do so requires a detail survey of the local lithosphere, as it contributes about 50% of the signal. Previous estimates of the geoneutrino signal at JUNO utilized global crustal models, with no local constraints. Regionally, the area is characterized by extensive lateral and vertical variations in lithology and dominated by Mesozoic granite intrusions, with an average heat production of 6.29 μW/m3. Consequently, at 3 times greater heat production than the globally average upper crust, these granites will generate a higher than average geoneutrino flux at JUNO. To better define the U and Th concentrations in the upper crust, we collected some 300 samples within 50 km of JUNO. By combining chemical data obtained from these samples with data for crustal structures defined by local geophysical studies, we will construct a detailed 3D geothermal model of the region. Our

Prevalence and predictive factors of post-traumatic hypopituitarism.

PubMed

Klose, M; Juul, A; Poulsgaard, L; Kosteljanetz, M; Brennum, J; Feldt-Rasmussen, U

2007-08-01

To estimate the prevalence and predictive factors of hypopituitarism following traumatic brain injury (TBI). A cross-sectional cohort study. One hundred and four hospitalized TBI patients (26F/78M), median age 41 (range 18-64) years, body mass index (BMI) 25 (17-39) kg/m(2); severity: mild [Glasgow Coma Scale (GCS) score 13-15) n = 44, moderate (GCS 9-12) n = 20, severe (GCS < 9) n = 40]. Patients were evaluated 13 (10-27) months post-injury, with measurement of baseline (0800-1000 h) and post-stimulatory hormonal levels during an insulin tolerance test (ITT) (86%) or, if contraindicated, an arginine(arg)-GHRH test + Synacthen test (14%). Insufficiencies were confirmed by retesting. Hypopituitarism was found in 16 (15%) patients, affecting one axis in 10, two axes in four and more than two axes in two patients. The GH axis was most frequently affected (15%), followed by secondary hypoadrenalism (5%), hypogonadism (2%), hypothyroidism (2%) and diabetes insipidus (2%). The risk of pituitary insufficiency was increased in patients with severe TBI as opposed to mild TBI [odds ratio (OR) 10.1, 95% confidence interval (CI) 2.1-48.4, P = 0.004], and in those patients with increased intracerebral pressure [OR 6.5, 95% CI 1.0-42.2, P = 0.03]. Patients with only one affected axis were all GH deficient; 60% (n = 6) of these were overweight or obese. The prevalence of hypopituitarism was estimated at 16%. Although high, this value was lower than previously reported, and may still be overestimated because of well-known confounding factors, such as obesity. Indicators of increased TBI severity were predictive of hypopituitarism, with a high negative predictive value. Neuroendocrine evaluation should therefore be considered in patients with severe TBI, and in particular in those with increased intracerebral pressure (ICP).

Specific headache factors predict sleep disturbances among youth with migraine.

PubMed

Heyer, Geoffrey L; Rose, Sean C; Merison, Kelsey; Perkins, Sara Q; Lee, Jo Ellen M

2014-10-01

There is a paucity of pediatric data addressing the complex relationship between primary headaches and sleep disturbances. Our study objective was to explore headache-related factors that predict sleep disturbance and to compare sleep complaints with other forms of headache-related disability among youth with migraines. A prospective cohort study was conducted in patients 10-18 years old with migraine or probable migraine and without daily sleep complaints. The patients completed a 90-day internet-based headache diary. On headache days, patients rated headache intensity, answered Pediatric Migraine Disability Assessment-based questions modified for daily scoring, and reported sleep disturbances that resulted as a direct effect of proximate headaches. Fifty-two patients generated 4680 diary entries, 984 patients (21%) involved headaches. Headache intensity (P = 0.009) and timing of headache onset (P < 0.001) were predictive of sleep disturbances. Three Pediatric Migraine Disability Assessment-based items were also associated with sleep disturbances: partial school-day absence (P = 0.04), recreational activities prevented (P < 0.001), and decreased functioning during recreational activities (P < 0.001). Sleep disturbances correlated positively and significantly with daily headache disability scores (rpb = 0.35; P < 0.01). We conclude that specific headache factors predict sleep disturbances among youth with primary headaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Factors influencing U.S. canine heartworm (Dirofilaria immitis) prevalence

PubMed Central

2014-01-01

Background This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates. Methods The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence. Results All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist. Conclusions The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence. PMID:24906567

Identifying the necessary and sufficient number of risk factors for predicting academic failure.

PubMed

Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

2012-03-01

Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] < 2.0) involves an understanding of which and how many factors contribute to poor outcomes. School-related factors appear to be among the many factors that significantly impact academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA < 2.0). Results yielded a cutoff point of 2 risk factors for predicting academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

[Lightning-caused fire, its affecting factors and prediction: a review].

PubMed

Zhang, Ji-Li; Bi, Wu; Wang, Xiao-Hong; Wang, Zi-Bo; Li, Di-Fei

2013-09-01

Lightning-caused fire is the most important natural fire source. Its induced forest fire brings enormous losses to human beings and ecological environment. Many countries have paid great attention to the prediction of lightning-caused fire. From the viewpoint of the main factors affecting the formation of lightning-caused fire, this paper emphatically analyzed the effects and action mechanisms of cloud-to-ground lightning, fuel, meteorology, and terrain on the formation and development process of lightning-caused fire, and, on the basis of this, summarized and reviewed the logistic model, K-function, and other mathematical methods widely used in prediction research of lightning-caused fire. The prediction methods and processes of lightning-caused fire in America and Canada were also introduced. The insufficiencies and their possible solutions for the present researches as well as the directions of further studies were proposed, aimed to provide necessary theoretical basis and literature reference for the prediction of lightning-caused fire in China.

Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography

NASA Astrophysics Data System (ADS)

Li, Yane; Fan, Ming; Li, Lihua; Zheng, Bin

2017-03-01

This study proposed a near-term breast cancer risk assessment model based on local region bilateral asymmetry features in Mammography. The database includes 566 cases who underwent at least two sequential FFDM examinations. The `prior' examination in the two series all interpreted as negative (not recalled). In the "current" examination, 283 women were diagnosed cancers and 283 remained negative. Age of cancers and negative cases completely matched. These cases were divided into three subgroups according to age: 152 cases among the 37-49 age-bracket, 220 cases in the age-bracket 50- 60, and 194 cases with the 61-86 age-bracket. For each image, two local regions including strip-based regions and difference-of-Gaussian basic element regions were segmented. After that, structural variation features among pixel values and structural similarity features were computed for strip regions. Meanwhile, positional features were extracted for basic element regions. The absolute subtraction value was computed between each feature of the left and right local-regions. Next, a multi-layer perception classifier was implemented to assess performance of features for prediction. Features were then selected according stepwise regression analysis. The AUC achieved 0.72, 0.75 and 0.71 for these 3 age-based subgroups, respectively. The maximum adjustable odds ratios were 12.4, 20.56 and 4.91 for these three groups, respectively. This study demonstrate that the local region-based bilateral asymmetry features extracted from CC-view mammography could provide useful information to predict near-term breast cancer risk.

Predictive factors for overall quality of life in patients with advanced cancer.

PubMed

Cramarossa, Gemma; Chow, Edward; Zhang, Liying; Bedard, Gillian; Zeng, Liang; Sahgal, Arjun; Vassiliou, Vassilios; Satoh, Takefumi; Foro, Palmira; Ma, Brigette B Y; Chie, Wei-Chu; Chen, Emily; Lam, Henry; Bottomley, Andrew

2013-06-01

This study examined which domains/symptoms from the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 15 Palliative (QLQ-C15-PAL), an abbreviated version of the health-related EORTC QLQ-C30 questionnaire designed for palliative cancer patients, were predictive of overall quality of life (QOL) in advanced cancer patients. Patients with advanced cancer from six countries completed the QLQ-C15-PAL at consultation and at one follow-up point. Univariate and multivariate regression analyses were conducted to determine the predictive value of the EORTC QLQ-C15-PAL functional/symptom scores for global QOL (question 15). Three hundred forty-nine patients completed the EORTC QLQ-C15-PAL at baseline. In the total patient sample, worse emotional functioning, pain, and appetite loss were the most significant predictive factors for worse QOL. In the subgroup of patients with bone metastases (n = 240), the domains mentioned above were also the most significant predictors, whereas in patients with brain metastases (n = 109), worse physical and emotional functioning most significantly predicted worse QOL. One-month follow-up in 267 patients revealed that the significant predictors changed somewhat over time. For example, in the total patient sample, physical functioning, fatigue, and appetite loss were significant predictors at the follow-up point. A sub-analysis of predictive factors affecting QOL by primary cancer (lung, breast, and prostate) was also conducted for the total patient sample. Deterioration of certain EORTC QLQ-C15-PAL functional/symptom scores significantly contributes to worse overall QOL. Special attention should be directed to managing factors most influential on overall QOL to ensure optimal management of advanced cancer patients.

Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

PubMed

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

2014-04-15

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

PubMed

Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

2017-01-01

Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p < 0.05). The patient factors model was compared to the traditional surgical scheduling system estimates, which uses historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model

Predictive factors for postoperative visual function of primary chronic rhegmatogenous retinal detachment after scleral buckling.

PubMed

Fang, Wei; Li, Jiu-Ke; Jin, Xiao-Hong; Dai, Yuan-Min; Li, Yu-Min

2016-01-01

To evaluate predictive factors for postoperative visual function of primary chronic rhegmatgenous retinal detachment (RRD) after sclera buckling (SB). Totally 48 patients (51 eyes) with primary chronic RRD were included in this prospective interventional clinical cases study, which underwent SB alone from June 2008 to December 2014. Age, sex, symptoms duration, detached extension, retinal hole position, size, type, fovea on/off, proliferative vitreoretinopathy (PVR), posterior vitreous detachment (PVD), baseline best corrected visual acuity (BCVA), operative duration, follow up duration, final BCVA were measured. Pearson correlation analysis, Spearman correlation analysis and multivariate linear stepwise regression were used to confirm predictive factors for better final visual acuity. Student's t-test, Wilcoxon two-sample test, Chi-square test and logistic stepwise regression were used to confirm predictive factors for better vision improvement. Baseline BCVA was 0.8313±0.6911 logMAR and final BCVA was 0.4761±0.4956 logMAR. Primary surgical success rate was 92.16% (47/51). Correlation analyses revealed shorter symptoms duration (r=0.3850, P=0.0053), less detached area (r=0.5489, P<0.0001), fovea (r=0.4605, P=0.0007), no PVR (r=0.3138, P=0.0250), better baseline BCVA (r=0.7291, P<0.0001), shorter operative duration (r=0.3233, P=0.0207) and longer follow up (r=-0.3358, P=0.0160) were related with better final BCVA, while independent predictive factors were better baseline BCVA [partial R-square (PR(2))=0.5316, P<0.0001], shorter symptoms duration (PR(2)=0.0609, P=0.0101), longer follow up duration (PR(2)=0.0278, P=0.0477) and shorter operative duration (PR(2)=0.0338, P=0.0350). Patients with vision improvement took up 49.02% (25/51). Univariate and multivariate analyses both revealed predictive factors for better vision improvement were better baseline vision [odds ratio (OR) =50.369, P=0.0041] and longer follow up duration (OR=1.144, P=0.0067). Independent

Histopathology of the tissue adhering to the multiple tine expandable electrodes used for radiofrequency ablation of hepatocellular carcinoma predicts local recurrence.

PubMed

Ishikawa, Toru; Kubota, Tomoyuki; Abe, Hiroyuki; Nagashima, Aiko; Hirose, Kanae; Togashi, Tadayuki; Seki, Keiichi; Honma, Terasu; Yoshida, Toshiaki; Kamimura, Tomoteru; Nemoto, Takeo; Takeda, Keiko; Ishihara, Noriko

2012-01-01

To assess the ability to predict the local recurrence of hepatocellular carcinoma by analyzing tissues adhering to the radiofrequency ablation probe after complete ablation. From May 2002 to March 2011, tissue specimens adhering to the radiofrequency ablation probe from 284 radiofrequency ablation sessions performed for hepatocellular carcinomas ≤3 cm in size were analyzed. The specimens were classified as either viable tumor tissue or complete necrosis, and the local recurrence rates were calculated using the Kaplan-Meier method. From the tumors ≤3 cm in size, viable tissue was present in 6 (2.1%) of 284 specimens, and the local recurrence rates after 1 and 2 years of follow-up were 6.7% and 11.2%, respectively. Local recurrence developed significantly earlier in the viable tissue group. The recurrence rate was not significantly different based on whether transcatheter arterial chemoembolization was performed. The histopathology of the tissue adhering to the radiofrequency ablation probes used for hepatocellular carcinoma treatment can predict local recurrence. Additional aggressive treatment for patients with viable tissue can therefore improve the overall survival.

The factors impacted to local contractor from Foreign Direct Investment in advancing economic hub development in Iskandar Malaysia

NASA Astrophysics Data System (ADS)

Syafiq Salim, Muhamad; Zakaria, Rozana; Aminuddin, Eeydzah; Hamid, Abdul Rahim Abdul; Abdullah, Redzuan; Shahzaib Khan, Jam

2018-04-01

Iskandar Malaysia is an advanced economic hub which is rapidly growing in the State of Johor. It has been an attractive place for Foreign Direct Investment (FDI) to invest. Many sectors are affected by the presence of FDI including the construction sector. This paper highlights the investigation on the effects of FDI to the local contractor in the Iskandar Malaysia Development. In this study, a questionnaire survey was carried out to gain the information on problems from internal factors and external factors that caused the limitation on involvement in FDI project by local contractors. 73 numbers of local contractor registered under CIDB in class G5, G6 and G7 are the respondents. Frequency analysis and Average Index Analysis are used for the results. This study provides the factors that impacted local construction players in Iskandar Malaysia Development. This study has portrayed that FDI plays a vital and significant role in spearheading the active involvement of local contractors in an urban sustainable development.

Local bladder cancer clusters in southeastern Michigan accounting for risk factors, covariates and residential mobility.

PubMed

Jacquez, Geoffrey M; Shi, Chen; Meliker, Jaymie R

2015-01-01

In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation. Global, local and focused Q-statistics are applied to data from a population-based case-control study of 11 southeast Michigan counties. Analyses were conducted using both year- and age-based measures of time. The analyses were adjusted for arsenic exposure, education, smoking, family history of bladder cancer, occupational exposure to bladder cancer carcinogens, age, gender, and race. Significant global clustering of cases was not found. Such a finding would indicate large-scale clustering of cases relative to controls through time. However, highly significant local clusters were found in Ingham County near Lansing, in Oakland County, and in the City of Jackson, Michigan. The Jackson City cluster was observed in working-ages and is thus consistent with occupational causes. The Ingham County cluster persists over time, suggesting a broad-based geographically defined exposure. Focused clusters were found for 20 industrial sites engaged in manufacturing activities associated with known or suspected bladder cancer carcinogens. Set-based tests that adjusted for multiple testing were not significant, although local clusters persisted through time and temporal trends in probability of local tests were observed. Q analyses provide a powerful tool for unpacking unexplained disease risk from case-control studies. This is particularly useful when the effect of risk factors varies spatially, through time, or through both space and time. For bladder cancer in Michigan, the next step is to investigate causal hypotheses that may explain the excess bladder cancer risk localized to areas of Oakland and Ingham

Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

NASA Astrophysics Data System (ADS)

Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

2017-12-01

Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China.

PubMed

Wu, Zhiwei; He, Hong S; Yang, Jian; Liu, Zhihua; Liang, Yu

2014-09-15

Fire significantly affects species composition, structure, and ecosystem processes in boreal forests. Our study objective was to identify the relative effects of climate, vegetation, topography, and human activity on fire occurrence in Chinese boreal forest landscapes. We used historical fire ignition for 1966-2005 and the statistical method of Kernel Density Estimation to derive fire-occurrence density (number of fires/km(2)). The Random Forest models were used to quantify the relative effects of climate, vegetation, topography, and human activity on fire-occurrence density. Our results showed that fire-occurrence density tended to be spatially clustered. Human-caused fire occurrence was highly clustered at the southern part of the region, where human population density is high (comprising about 75% of the area's population). In the north-central areas where elevations are the highest in the region and less densely populated, lightning-caused fires were clustered. Climate factors (e.g., fine fuel and duff moisture content) were important at both regional and landscape scales. Human activity factors (e.g., distance to nearest settlement and road) were secondary to climate as the primary fire occurrence factors. Predictions of fire regimes often assume a strong linkage between climate and fire but usually with less emphasis placed on the effects of local factors such as human activity. We therefore suggest that accurate forecasting of fire regime should include human influences such as those measured by forest proximity to roads and human settlements. Copyright © 2014 Elsevier B.V. All rights reserved.

Seasonal prediction of lightning activity in North Western Venezuela: Large-scale versus local drivers

NASA Astrophysics Data System (ADS)

Muñoz, Á. G.; Díaz-Lobatón, J.; Chourio, X.; Stock, M. J.

2016-05-01

The Lake Maracaibo Basin in North Western Venezuela has the highest annual lightning rate of any place in the world (~ 200 fl km- 2 yr- 1), whose electrical discharges occasionally impact human and animal lives (e.g., cattle) and frequently affect economic activities like oil and natural gas exploitation. Lightning activity is so common in this region that it has a proper name: Catatumbo Lightning (plural). Although short-term lightning forecasts are now common in different parts of the world, to the best of the authors' knowledge, seasonal prediction of lightning activity is still non-existent. This research discusses the relative role of both large-scale and local climate drivers as modulators of lightning activity in the region, and presents a formal predictability study at seasonal scale. Analysis of the Catatumbo Lightning Regional Mode, defined in terms of the second Empirical Orthogonal Function of monthly Lightning Imaging Sensor (LIS-TRMM) and Optical Transient Detector (OTD) satellite data for North Western South America, permits the identification of potential predictors at seasonal scale via a Canonical Correlation Analysis. Lightning activity in North Western Venezuela responds to well defined sea-surface temperature patterns (e.g., El Niño-Southern Oscillation, Atlantic Meridional Mode) and changes in the low-level meridional wind field that are associated with the Inter-Tropical Convergence Zone migrations, the Caribbean Low Level Jet and tropical cyclone activity, but it is also linked to local drivers like convection triggered by the topographic configuration and the effect of the Maracaibo Basin Nocturnal Low Level Jet. The analysis indicates that at seasonal scale the relative contribution of the large-scale drivers is more important than the local (basin-wide) ones, due to the synoptic control imposed by the former. Furthermore, meridional CAPE transport at 925 mb is identified as the best potential predictor for lightning activity in the Lake

Predicting Factors of Chronic Subdural Hematoma Following Surgical Clipping in Unruptured and Ruptured Intracranial Aneurysm.

PubMed

Kwon, Min-Yong; Kim, Chang-Hyun; Lee, Chang-Young

2016-09-01

The aim of this study is to analyze the differences in the incidence, predicting factors, and clinical course of chronic subdural hematoma (CSDH) following surgical clipping between unruptured (UIA) and ruptured intracranial aneurysm (RIA). We conducted a retrospective analysis of 752 patients (UIA : 368 and RIA : 384) who underwent surgical clipping during 8 years. The incidence and predicting factors of CSDH development in the UIA and RIA were compared according to medical records and radiological data. The incidence of postoperative CSDH was higher in the UIA (10.9%) than in the RIA (3.1%) (p=0.000). In multivariate analysis, a high Hounsfield (HF) unit (blood clots) for subdural fluid collection (SFC), persistence of SFC ≥5 mm and male sex in the UIA and A high HF unit for SFC and SFC ≥5 mm without progression to hydrocephalus in the RIA were identified as the independent predicting factors for CSDH development (p<0.05). There were differences in the incidence and predicting factors for CSDH following surgical clipping between UIA and RIA. Blood clots in the subdural space and persistence of SFC ≥5 mm were predicting factors in both UIA and RIA. However, progression to hydrocephalus may have in part contributed to low CSDH development in the RIA. We suggest that cleaning of blood clots in the subdural space and efforts to minimize SFC ≥5 mm at the end of surgery is helpful to prevent CSDH following aneurysmal clipping.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

NASA Astrophysics Data System (ADS)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

[Cesarean after labor induction: Risk factors and prediction score].

PubMed

Branger, B; Dochez, V; Gervier, S; Winer, N

2018-05-01

The objective of the study is to determine the risk factors for caesarean section at the time of labor induction, to establish a prediction algorithm, to evaluate its relevance and to compare the results with observation. A retrospective study was carried out over a year at Nantes University Hospital with 941 cervical ripening and labor inductions (24.1%) terminated by 167 caesarean sections (17.8%). Within the cohort, a case-control study was conducted with 147 caesarean sections and 148 vaginal deliveries. A multivariate analysis was carried out with a logistic regression allowing the elaboration of an equation of prediction and an ROC curve and the confrontation between the prediction and the reality. In univariate analysis, six variables were significant: nulliparity, small size of the mother, history of scarried uterus, use of prostaglandins as a mode of induction, unfavorable Bishop score<6, variety of posterior release. In multivariate analysis, five variables were significant: nulliparity, maternal size, maternal BMI, scar uterus and Bishop score. The most predictive model corresponded to an area under the curve of 0.86 (0.82-0.90) with a correct prediction percentage ("well classified") of 67.6% for a caesarean section risk of 80%. The prediction criteria would make it possible to inform the woman and the couple about the potential risk of Caesarean section in urgency or to favor a planned Caesarean section or a low-lying attempt on more objective, repeatable and transposable arguments in a medical team. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Can the big five factors of personality predict lymphocyte counts?

PubMed

Ožura, Ana; Ihan, Alojz; Musek, Janek

2012-03-01

Psychological stress is known to affect the immune system. The Limbic Hypothalamic Pituitary Adrenal (LHPA) axis has been identified as the principal path of the bidirectional communication between the immune system and the central nervous system with significant psychological activators. Personality traits acted as moderators of the relationship between life conflicts and psychological distress. This study focuses on the relationship between the Big Five factors of personality and immune regulation as indicated by Lymphocyte counts. Our study included 32 professional soldiers from the Slovenian Army that completed the Big Five questionnaire (Goldberg IPIP-300). We also assessed their white blood cell counts with a detailed lymphocyte analysis using flow cytometry. The correlations between personality variables and immune system parameters were calculated. Furthermore, regression analyses were performed using personality variables as predictors and immune parameters as criteria. The results demonstrated that the model using the Big Five factors as predictors of Lymphocyte counts is significant in predicting the variance in NK and B cell counts. Agreeableness showed the strongest predictive function. The results offer support for the theoretical models that stressed the essential links between personality and immune regulation. Further studies with larger samples examining the Big five factors and immune system parameters are needed.

Localized mRNA translation and protein association

NASA Astrophysics Data System (ADS)

Zhdanov, Vladimir P.

2014-08-01

Recent direct observations of localization of mRNAs and proteins both in prokaryotic and eukaryotic cells can be related to slowdown of diffusion of these species due to macromolecular crowding and their ability to aggregate and form immobile or slowly mobile complexes. Here, a generic kinetic model describing both these factors is presented and comprehensively analyzed. Although the model is non-linear, an accurate self-consistent analytical solution of the corresponding reaction-diffusion equation has been constructed, the types of localized protein distributions have been explicitly shown, and the predicted kinetic regimes of gene expression have been classified.

Predicting College Success: The Relative Contributions of Five Social/Personality Factors, Five Cognitive/Learning Factors, and SAT Scores

PubMed Central

Hannon, Brenda

2014-01-01

To-date, studies have examined simultaneously the relative predictive powers of two or three factors on GPA. The present study examines the relative powers of five social/personality factors, five cognitive/learning factors, and SAT scores to predict freshmen and non-freshmen (sophomores, juniors, seniors) academic success (i.e., GPA). The results revealed many significant predictors of GPA for both freshmen and non-freshmen. However, subsequent regressions showed that only academic self-efficacy, epistemic belief of learning, and high-knowledge integration explained unique variance in GPA (19%-freshmen, 23.2%-non-freshmen). Further for freshmen, SAT scores explained an additional unique 10.6% variance after the influences attributed to these three predictors was removed whereas for non-freshmen, SAT scores failed to explain any additional variance. These results highlight the unique and important contributions of academic self-efficacy, epistemic belief of learning and high-knowledge integration to GPA beyond other previously-identified predictors. PMID:25568884

Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences.

PubMed

Andrabi, Munazah; Hutchins, Andrew Paul; Miranda-Saavedra, Diego; Kono, Hidetoshi; Nussinov, Ruth; Mizuguchi, Kenji; Ahmad, Shandar

2017-06-22

DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist, Minor groove width and Roll. The contributions of other shape features e.g. Shift, Slide and Opening cannot be evaluated using DNAshape. Here, we report a novel method DynaSeq, which predicts molecular dynamics-derived ensembles of a more exhaustive set of DNA shape features. We compared the DNAshape and DynaSeq predictions for the common features and applied both to predict the genome-wide binding sites of 1312 TFs available from protein interaction quantification (PIQ) data. The results indicate a good agreement between the two methods for the common shape features and point to advantages in using DynaSeq. Predictive models employing ensembles from individual conformational parameters revealed that base-pair opening - known to be important in strand separation - was the best predictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape. Of note, TFBS could be predicted not only from the features at the target motif sites, but also from those as far as 200 nucleotides away from the motif.

Predicting trends of invasive plants richness using local socio-economic data: An application in North Portugal

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

Santos, Mario, E-mail: mgsantoss@gmail.com; Freitas, Raul, E-mail: raulfreitas@portugalmail.com; Crespi, Antonio L., E-mail: aluis.crespi@gmail.com

2011-10-15

This study assesses the potential of an integrated methodology for predicting local trends in invasive exotic plant species (invasive richness) using indirect, regional information on human disturbance. The distribution of invasive plants was assessed in North Portugal using herbarium collections and local environmental, geophysical and socio-economic characteristics. Invasive richness response to anthropogenic disturbance was predicted using a dynamic model based on a sequential modeling process (stochastic dynamic methodology-StDM). Derived scenarios showed that invasive richness trends were clearly associated with ongoing socio-economic change. Simulations including scenarios of growing urbanization showed an increase in invasive richness while simulations in municipalities with decreasingmore » populations showed stable or decreasing levels of invasive richness. The model simulations demonstrate the interest and feasibility of using this methodology in disturbance ecology. - Highlights: {yields} Socio-economic data indicate human induced disturbances. {yields} Socio-economic development increase disturbance in ecosystems. {yields} Disturbance promotes opportunities for invasive plants.{yields} Increased opportunities promote richness of invasive plants.{yields} Increase in richness of invasive plants change natural ecosystems.« less

Local adaptation in migrated interior Douglas-fir seedlings is mediated by ectomycorrhizas and other soil factors.

PubMed

Pickles, Brian J; Twieg, Brendan D; O'Neill, Gregory A; Mohn, William W; Simard, Suzanne W

2015-08-01

Separating edaphic impacts on tree distributions from those of climate and geography is notoriously difficult. Aboveground and belowground factors play important roles, and determining their relative contribution to tree success will greatly assist in refining predictive models and forestry strategies in a changing climate. In a common glasshouse, seedlings of interior Douglas-fir (Pseudotsuga menziesii var. glauca) from multiple populations were grown in multiple forest soils. Fungicide was applied to half of the seedlings to separate soil fungal and nonfungal impacts on seedling performance. Soils of varying geographic and climatic distance from seed origin were compared, using a transfer function approach. Seedling height and biomass were optimized following seed transfer into drier soils, whereas survival was optimized when elevation transfer was minimised. Fungicide application reduced ectomycorrhizal root colonization by c. 50%, with treated seedlings exhibiting greater survival but reduced biomass. Local adaptation of Douglas-fir populations to soils was mediated by soil fungi to some extent in 56% of soil origin by response variable combinations. Mediation by edaphic factors in general occurred in 81% of combinations. Soil biota, hitherto unaccounted for in climate models, interacts with biogeography to influence plant ranges in a changing climate. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Influence of local meshing size on stress intensity factor of orthopedic lag screw

NASA Astrophysics Data System (ADS)

Husain, M. N.; Daud, R.; Basaruddin, K. S.; Mat, F.; Bajuri, M. Y.; Arifin, A. K.

2017-09-01

Linear elastic fracture mechanics (LEFM) concept is generally used to study the influence of crack on the performance of structures. In order to study the LEFM concept on damaged structure, the usage of finite element analysis software is implemented to do the simulation of the structure. Mesh generation is one of the most crucial procedures in finite element method. For the structure that crack or damaged, it is very important to determine the accurate local meshing size at the crack tip of the crack itself in order to get the accurate value of stress intensity factor, KI. Pre crack will be introduced to the lag screw based on the von mises' stress result that had been performed in previous research. This paper shows the influence of local mesh arrangement on numerical value of the stress intensity factor, KI obtained by the displacement method. This study aims to simulate the effect of local meshing which is the singularity region on stress intensity factor, KI to the critical point of failure in screw. Five different set of wedges meshing size are introduced during the simulation of finite element analysis. The number of wedges used to simulate this research is 8, 10, 14, 16 and 20. There are three set of numerical equations used to validate the results which are brown and srawley, gross and brown and Tada equation. The result obtained from the finite element software (ANSYS APDL) has a positive agreement with the numerical analysis which is Brown and Srawley compared to other numerical formula. Radius of first row size of 0.014 and singularity element with 14 numbers of wedges is proved to be the best local meshing for this study.

Nuclear Localization of the C1 Factor (Host Cell Factor) in Sensory Neurons Correlates with Reactivation of Herpes Simplex Virus from Latency

NASA Astrophysics Data System (ADS)

Kristie, Thomas M.; Vogel, Jodi L.; Sears, Amy E.

1999-02-01

After a primary infection, herpes simplex virus is maintained in a latent state in neurons of sensory ganglia until complex stimuli reactivate viral lytic replication. Although the mechanisms governing reactivation from the latent state remain unknown, the regulated expression of the viral immediate early genes represents a critical point in this process. These genes are controlled by transcription enhancer complexes whose assembly requires and is coordinated by the cellular C1 factor (host cell factor). In contrast to other tissues, the C1 factor is not detected in the nuclei of sensory neurons. Experimental conditions that induce the reactivation of herpes simplex virus in mouse model systems result in rapid nuclear localization of the protein, indicating that the C1 factor is sequestered in these cells until reactivation signals induce a redistribution of the protein. The regulated localization suggests that C1 is a critical switch determinant of the viral lytic-latent cycle.

A Bridge from Optical to Infrared Galaxies: Explaining Local Properties and Predicting Galaxy Counts and the Cosmic Background Radiation

NASA Astrophysics Data System (ADS)

Totani, Tomonori; Takeuchi, Tsutomu T.

2002-05-01

We give an explanation for the origin of various properties observed in local infrared galaxies and make predictions for galaxy counts and cosmic background radiation (CBR) using a new model extended from that for optical/near-infrared galaxies. Important new characteristics of this study are that (1) mass scale dependence of dust extinction is introduced based on the size-luminosity relation of optical galaxies and that (2) the large-grain dust temperature Tdust is calculated based on a physical consideration for energy balance rather than by using the empirical relation between Tdust and total infrared luminosity LIR found in local galaxies, which has been employed in most previous works. Consequently, the local properties of infrared galaxies, i.e., optical/infrared luminosity ratios, LIR-Tdust correlation, and infrared luminosity function are outputs predicted by the model, while these have been inputs in a number of previous models. Our model indeed reproduces these local properties reasonably well. Then we make predictions for faint infrared counts (in 15, 60, 90, 170, 450, and 850 μm) and CBR using this model. We found results considerably different from those of most previous works based on the empirical LIR-Tdust relation; especially, it is shown that the dust temperature of starbursting primordial elliptical galaxies is expected to be very high (40-80 K), as often seen in starburst galaxies or ultraluminous infrared galaxies in the local and high-z universe. This indicates that intense starbursts of forming elliptical galaxies should have occurred at z~2-3, in contrast to the previous results that significant starbursts beyond z~1 tend to overproduce the far-infrared (FIR) CBR detected by COBE/FIRAS. On the other hand, our model predicts that the mid-infrared (MIR) flux from warm/nonequilibrium dust is relatively weak in such galaxies making FIR CBR, and this effect reconciles the prima facie conflict between the upper limit on MIR CBR from TeV gamma

Stress and anger as contextual factors and preexisting cognitive schemas: predicting parental child maltreatment risk.

PubMed

Rodriguez, Christina M; Richardson, Michael J

2007-11-01

Progress in the child maltreatment field depends on refinements in leading models. This study examines aspects of social information processing theory (Milner, 2000) in predicting physical maltreatment risk in a community sample. Consistent with this theory, selected preexisting schema (external locus-of-control orientation, inappropriate developmental expectations, low empathic perspective-taking ability, and low perceived attachment relationship to child) were expected to predict child abuse risk beyond contextual factors (parenting stress and anger expression). Based on 115 parents' self-report, results from this study support cognitive factors that predict abuse risk (with locus of control, perceived attachment, or empathy predicting different abuse risk measures, but not developmental expectations), although the broad contextual factors involving negative affectivity and stress were consistent predictors across abuse risk markers. Findings are discussed with regard to implications for future model evaluations, with indications the model may apply to other forms of maltreatment, such as psychological maltreatment or neglect.

Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.

PubMed

Zitnik, Marinka; Zupan, Blaž

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.

Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study.

PubMed

Ko, Chih-Hung; Yen, Ju-Yu; Yen, Cheng-Fang; Lin, Huang-Chi; Yang, Ming-Jen

2007-08-01

The aim of the study is to determine the incidence and remission rates for Internet addiction and the associated predictive factors in young adolescents over a 1-year follow-up. This was a prospective, population-based investigation. Five hundred seventeen students (267 male and 250 female) were recruited from three junior high schools in southern Taiwan. The factors examined included gender, personality, mental health, self-esteem, family function, life satisfaction, and Internet activities. The result revealed that the 1-year incidence and remission rates for Internet addiction were 7.5% and 49.5% respectively. High exploratory excitability, low reward dependence, low self-esteem, low family function, and online game playing predicted the emergency of the Internet addiction. Further, low hostility and low interpersonal sensitivity predicted remission of Internet addiction. The factors predictive incidence and remission of Internet addiction identified in this study could be provided for prevention and promoting remission of Internet addiction in adolescents.

Predicting preschool pain-related anticipatory distress: the relative contribution of longitudinal and concurrent factors.

PubMed

Racine, Nicole M; Pillai Riddell, Rebecca R; Flora, David B; Taddio, Anna; Garfield, Hartley; Greenberg, Saul

2016-09-01

Anticipatory distress prior to a painful medical procedure can lead to negative sequelae including heightened pain experiences, avoidance of future medical procedures, and potential noncompliance with preventative health care, such as vaccinations. Few studies have examined the longitudinal and concurrent predictors of pain-related anticipatory distress. This article consists of 2 companion studies to examine both the longitudinal factors from infancy as well as concurrent factors from preschool that predict pain-related anticipatory distress at the preschool age. Study 1 examined how well preschool pain-related anticipatory distress was predicted by infant pain response at 2, 4, 6, and 12 months of age. In study 2, using a developmental psychopathology framework, longitudinal analyses examined the predisposing, precipitating, perpetuating, and present factors that led to the development of anticipatory distress during routine preschool vaccinations. A sample of 202 caregiver-child dyads was observed during their infant and preschool vaccinations (the Opportunities to Understand Childhood Hurt cohort) and was used for both studies. In study 1, pain response during infancy was not found to significantly predict pain-related anticipatory distress at preschool. In study 2, a strong explanatory model was created whereby 40% of the variance in preschool anticipatory distress was explained. Parental behaviours from infancy and preschool were the strongest predictors of child anticipatory distress at preschool. Child age positively predicted child anticipatory distress. This strongly suggests that the involvement of parents in pain management interventions during immunization is one of the most critical factors in predicting anticipatory distress to the preschool vaccination.

Socioeconomic Factors Affecting Local Support for Black Bear Recovery Strategies

NASA Astrophysics Data System (ADS)

Morzillo, Anita T.; Mertig, Angela G.; Hollister, Jeffrey W.; Garner, Nathan; Liu, Jianguo

2010-06-01

There is global interest in recovering locally extirpated carnivore species. Successful efforts to recover Louisiana black bear in Louisiana have prompted interest in recovery throughout the species’ historical range. We evaluated support for three potential black bear recovery strategies prior to public release of a black bear conservation and management plan for eastern Texas, United States. Data were collected from 1,006 residents living in proximity to potential recovery locations, particularly Big Thicket National Preserve. In addition to traditional logistic regression analysis, we used conditional probability analysis to statistically and visually evaluate probabilities of public support for potential black bear recovery strategies based on socioeconomic characteristics. Allowing black bears to repopulate the region on their own (i.e., without active reintroduction) was the recovery strategy with the greatest probability of acceptance. Recovery strategy acceptance was influenced by many socioeconomic factors. Older and long-time local residents were most likely to want to exclude black bears from the area. Concern about the problems that black bears may cause was the only variable significantly related to support or non-support across all strategies. Lack of personal knowledge about black bears was the most frequent reason for uncertainty about preferred strategy. In order to reduce local uncertainty about possible recovery strategies, we suggest that wildlife managers focus outreach efforts on providing local residents with general information about black bears, as well as information pertinent to minimizing the potential for human-black bear conflict.

Factors associated with therapeutic inertia in hypertension: validation of a predictive model.

PubMed

Redón, Josep; Coca, Antonio; Lázaro, Pablo; Aguilar, Ma Dolores; Cabañas, Mercedes; Gil, Natividad; Sánchez-Zamorano, Miguel Angel; Aranda, Pedro

2010-08-01

To study factors associated with therapeutic inertia in treating hypertension and to develop a predictive model to estimate the probability of therapeutic inertia in a given medical consultation, based on variables related to the consultation, patient, physician, clinical characteristics, and level of care. National, multicentre, observational, cross-sectional study in primary care and specialist (hospital) physicians who each completed a questionnaire on therapeutic inertia, provided professional data and collected clinical data on four patients. Therapeutic inertia was defined as a consultation in which treatment change was indicated (i.e., SBP >or= 140 or DBP >or= 90 mmHg in all patients; SBP >or= 130 or DBP >or= 80 in patients with diabetes or stroke), but did not occur. A predictive model was constructed and validated according to the factors associated with therapeutic inertia. Data were collected on 2595 patients and 13,792 visits. Therapeutic inertia occurred in 7546 (75%) of the 10,041 consultations in which treatment change was indicated. Factors associated with therapeutic inertia were primary care setting, male sex, older age, SPB and/or DBP values close to normal, treatment with more than one antihypertensive drug, treatment with an ARB II, and more than six visits/year. Physician characteristics did not weigh heavily in the association. The predictive model was valid internally and externally, with acceptable calibration, discrimination and reproducibility, and explained one-third of the variability in therapeutic inertia. Although therapeutic inertia is frequent in the management of hypertension, the factors explaining it are not completely clear. Whereas some aspects of the consultations were associated with therapeutic inertia, physician characteristics were not a decisive factor.

Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region

NASA Astrophysics Data System (ADS)

Chen, Ziyue; Cai, Jun; Gao, Bingbo; Xu, Bing; Dai, Shuang; He, Bin; Xie, Xiaoming

2017-01-01

Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM2.5 concentration should be better understood. This research examined seasonal variations of PM2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM2.5 concentration. For the Jing-Jin-Ji region, the higher PM2.5 concentration, the stronger influences meteorological factors exert on PM2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.

Cortical localization of phase and amplitude dynamics predicting access to somatosensory awareness.

PubMed

Hirvonen, Jonni; Palva, Satu

2016-01-01

Neural dynamics leading to conscious sensory perception have remained enigmatic in despite of large interest. Human functional magnetic resonance imaging (fMRI) studies have revealed that a co-activation of sensory and frontoparietal areas is crucial for conscious sensory perception in the several second time-scale of BOLD signal fluctuations. Electrophysiological recordings with magneto- and electroencephalography (MEG and EEG) and intracranial EEG (iEEG) have shown that event related responses (ERs), phase-locking of neuronal activity, and oscillation amplitude modulations in sub-second timescales are greater for consciously perceived than for unperceived stimuli. The cortical sources of ER and oscillation dynamics predicting the conscious perception have, however, remained unclear because these prior studies have utilized MEG/EEG sensor-level analyses or iEEG with limited neuroanatomical coverage. We used a somatosensory detection task, magnetoencephalography (MEG), and cortically constrained source reconstruction to identify the cortical areas where ERs, local poststimulus amplitudes and phase-locking of neuronal activity are predictive of the conscious access of somatosensory information. We show here that strengthened ERs, phase-locking to stimulus onset (SL), and induced oscillations amplitude modulations all predicted conscious somatosensory perception, but the most robust and widespread of these was SL that was sustained in low-alpha (6-10 Hz) band. The strength of SL and to a lesser extent that of ER predicted conscious perception in the somatosensory, lateral and medial frontal, posterior parietal, and in the cingulate cortex. These data suggest that a rapid phase-reorganization and concurrent oscillation amplitude modulations in these areas play an instrumental role in the emergence of a conscious percept. © 2015 Wiley Periodicals, Inc.

Identifying Factors that Most Strongly Predict Aircraft Reliability Behavior

DTIC Science & Technology

2013-06-01

time to perform a specific airlift mission or category of missions based on all pertinent operational and logistical factors.” ( Randall , 2004, p. 64...resources are contingent upon the demand and airfield environment. ( Randall , 2004) The challenge with researching and predicting MC rates is its...Departmental Publishing Office. http://www.e- publishing.af.mil/shared/media/epubs/AFDD3-17.pdf McClave JT, Benson PG, Sincich TS, (2011). Statistics for

Local Failure After Episcleral Brachytherapy for Posterior Uveal Melanoma: Patterns, Risk Factors, and Management.

PubMed

Bellerive, Claudine; Aziz, Hassan A; Bena, James; Wilkinson, Allan; Suh, John H; Plesec, Thomas; Singh, Arun D

2017-05-01

To evaluate the patterns, the risk factors, and the management of recurrence following brachytherapy in patients with posterior uveal melanoma, given that an understanding of the recurrence patterns can improve early recognition and management of local treatment failure in such patients. Retrospective cohort study. Setting: Multispecialty tertiary care center. A total of 375 eyes treated with episcleral brachytherapy for posterior uveal melanoma from January 2004 to December 2014. Exclusion criteria included inadequate follow-up (<1 year) and previous radiation therapy. Main Outcomes and Measures: Local control rate and time to recurrence were the primary endpoints. Kaplan-Meier estimation and Cox proportional hazards models were conducted to identify risk factors for recurrence. Twenty-one patients (5.6%) experienced recurrence (follow-up range 12-156 months; median 47 months). The median time to recurrence was 18 months (range 4-156 months). Five-year estimated local recurrence rate was 6.6%. The majority (90.5%) of the recurrences occurred within the first 5 years. The predominant site of recurrence was at the tumor margin (12 patients, 57.1%). Univariate analysis identified 3 statistically significant recurrence risk factors: advanced age, largest basal diameter, and the use of adjuvant transpupillary thermotherapy (TTT). Recurrent tumors were managed by repeat brachytherapy, TTT, or enucleation. Local recurrences following brachytherapy are uncommon 5 years after episcleral brachytherapy. Follow-up intervals can be adjusted to reflect time to recurrence. Most of the eyes with recurrent tumor can be salvaged by conservative methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Factors associated with local public health agency participation in obesity prevention in southern States.

PubMed

Hatala, Jeffrey J; Fields, Tina T

2015-05-01

Obesity rates in the southern US states are higher than in other states. Historically, large-scale community-based interventions in the United States have not proven successful. With local public health agencies (LPHAs) tasked with prevention, their role in obesity prevention is important, yet little research exists regarding what predicts the participation of LPHAs. Cross-sectional data from the 2008 National Association of City and County Health Officials profile study and two public health conceptual frameworks were used to assess structural and environmental predictors of LPHA participation in obesity prevention. The predictors were compared between southern and nonsouthern states. Univariate and weighted logistic regressions were performed. Analysis revealed that more LPHAs in southern states were engaged in nearly all of the 10 essential public health functions related to obesity prevention compared with nonsouthern states. Presence of community-based organizations and staffing levels were the only significant variables in two of the six logistic regression models. This study provides insights into the success rates of the obesity prevention efforts of LPHAs in southern and nonsouthern states. Future research is needed to understand why and how certain structural elements and any additional factors influence LPHA participation in obesity prevention.

Predicting Factors of Chronic Subdural Hematoma Following Surgical Clipping in Unruptured and Ruptured Intracranial Aneurysm

PubMed Central

Kwon, Min-Yong; Kim, Chang-Hyun

2016-01-01

Objective The aim of this study is to analyze the differences in the incidence, predicting factors, and clinical course of chronic subdural hematoma (CSDH) following surgical clipping between unruptured (UIA) and ruptured intracranial aneurysm (RIA). Methods We conducted a retrospective analysis of 752 patients (UIA : 368 and RIA : 384) who underwent surgical clipping during 8 years. The incidence and predicting factors of CSDH development in the UIA and RIA were compared according to medical records and radiological data. Results The incidence of postoperative CSDH was higher in the UIA (10.9%) than in the RIA (3.1%) (p=0.000). In multivariate analysis, a high Hounsfield (HF) unit (blood clots) for subdural fluid collection (SFC), persistence of SFC ≥5 mm and male sex in the UIA and A high HF unit for SFC and SFC ≥5 mm without progression to hydrocephalus in the RIA were identified as the independent predicting factors for CSDH development (p<0.05). Conclusion There were differences in the incidence and predicting factors for CSDH following surgical clipping between UIA and RIA. Blood clots in the subdural space and persistence of SFC ≥5 mm were predicting factors in both UIA and RIA. However, progression to hydrocephalus may have in part contributed to low CSDH development in the RIA. We suggest that cleaning of blood clots in the subdural space and efforts to minimize SFC ≥5 mm at the end of surgery is helpful to prevent CSDH following aneurysmal clipping. PMID:27651863

Factors predictive of complicated appendicitis in children.

PubMed

Pham, Xuan-Binh D; Sullins, Veronica F; Kim, Dennis Y; Range, Blake; Kaji, Amy H; de Virgilio, Christian M; Lee, Steven L

2016-11-01

The ability to predict whether a child has complicated appendicitis at initial presentation may influence clinical management. However, whether complicated appendicitis is associated with prehospital or inhospital factors is not clear. We also investigate whether hyponatremia may be a novel prehospital factor associated with complicated appendicitis. A retrospective review of all pediatric patients (≤12 y) with appendicitis treated with appendectomy from 2000 to 2013 was performed. The main outcome measure was intraoperative confirmation of gangrenous or perforated appendicitis. A multivariable analysis was performed, and the main predictors of interest were age <5 y, symptom duration >24 h, leukocytosis (white blood cell count >12 × 10 3 /mL), hyponatremia (sodium ≤135 mEq/L), and time from admission to appendectomy. Of 392 patients, 179 (46%) had complicated appendicitis at the time of operation. Univariate analysis demonstrated that patients with complicated appendicitis were younger, had a longer duration of symptoms, higher white blood cell count, and lower sodium levels than patients with noncomplicated appendicitis. Multivariable analysis confirmed that symptom duration >24 h (odds ratio [OR] = 5.5, 95% confidence interval [CI] = 3.5-8.9, P < 0.01), hyponatremia (OR = 3.1, 95% CI = 2.0-4.9, P < 0.01), age <5 y (OR = 2.3, 95% CI = 1.3-4.0, P < 0.01), and leukocytosis (OR = 1.9, 95% CI = 1.0-3.5, P = 0.04) were independent predictors of complicated appendicitis. Increased time from admission to appendectomy was not a predictor of complicated appendicitis (OR = 0.8, 95% CI = 0.5-1.2, P = 0.2). Prehospital factors can predict complicated appendicitis in children with suspected appendicitis. Hyponatremia is a novel marker associated with complicated appendicitis. Delaying appendectomy does not increase the risk of complicated appendicitis once intravenous antibiotics are administered. This information may help guide

HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2014-01-01

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

Predictive factors for lower extremity amputations in diabetic foot infections

PubMed Central

Aziz, Zameer; Lin, Wong Keng; Nather, Aziz; Huak, Chan Yiong

2011-01-01

The objective of this study was to evaluate the epidemiology of diabetic foot infections (DFIs) and its predictive factors for lower extremity amputations. A prospective study of 100 patients with DFIs treated at the National University Hospital of Singapore were recruited in the study during the period of January 2005–June 2005. A protocol was designed to document patient's demographics, type of DFI, presence of neuropathy and/or vasculopathy and its final outcome. Predictive factors for limb loss were determined using univariate and stepwise logistic regression analysis. The mean age of the study population was 59.8 years with a male to female ratio of about 1:1 and with a mean follow-up duration of about 24 months. All patients had type 2 diabetes mellitus. Common DFIs included abscess (32%), wet gangrene (29%), infected ulcers (19%), osteomyelitis (13%), necrotizing fasciitis (4%) and cellulitis (3%). Thirteen patients were treated conservatively, while surgical debridement or distal amputation was performed in 59 patients. Twenty-eight patients had major amputations (below or above knee) performed. Forty-eight percent had monomicrobial infections compared with 52% with polymicrobial infections. The most common pathogens found in all infections (both monomicrobial and polymicrobial) were Staphylococcus aureus (39.7%), Bacteroides fragilis (30.3%), Pseudomonas aeruginosa (26.0%) and Streptococcus agalactiae (21.0%). Significant univariate predictive factors for limb loss included age above 60 years, gangrene, ankle-brachial index (ABI) <0.8, monomicrobial infections, white blood cell (WBC) count ≥ 15.0×109/L, erythrocyte sedimentation rate ≥100 mm/hr, C-reactive protein ≥15.0 mg/dL, hemoglobin (Hb) ≤10.0g/dL and creatinine ≥150 µmol/L. Upon stepwise logistic regression, only gangrene, ABI <0.8, WBC ≥ 15.0×109/L and Hb ≤10.0g/dL were significant. PMID:22396824

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

PubMed

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Factors predictive of alcohol abstention after resident detoxication among alcoholics followed in an hospital outpatient center.

PubMed

Gelsi, Eve; Vanbiervliet, Geoffroy; Chérikh, Faredj; Mariné-Barjoan, Eugénia; Truchi, Régine; Arab, Kamel; Delmont, Jean-Marie; Tran, Albert

2007-01-01

A cohort of patient hospitalized for alcohol detoxification between January 2004 and January 2005 were followed prospectively to search for factors predictive factors of sustained abstinence. One hundred and fifteen patients (79 males, 36 females, median age 45.9+/-10.7 years), were hospitalized for alcohol detoxification. Demographic, social, and medical data including daily alcohol intake and co-addictions were noted at inclusion and six months later. Patients who did not attend their six-month visit were contacted by phone. Among the 115 included patients, six month follow-up data could be collected for 73. Abstinence rate was 54.8%. Factors predictive of unsuccessful cessation were homelessness (P=0.004), duration of alcohol consumption (P=0.004), smoking (P=0.02), drug substitution (P=0.04) and multiple addictions (P=0.04). At multivariate analysis, multiple addictions was the only independent factor predictive of unsuccessful detoxification. Naltrexone or acamprosate treatments were not associated with a better rate of alcohol detoxification. Patient follow-up is problematic due to the large number of dropouts among alcoholics. Early screening in search for factors predictive of unsuccessful detoxification (long duration of alcohol consumption, multiple addiction) would be helpful in elaborating appropriate pluridisciplinary management.

Predictive factors for the Nursing Diagnoses in people living with Acquired Immune Deficiency Syndrome 1

PubMed Central

da Silva, Richardson Augusto Rosendo; Costa, Romanniny Hévillyn Silva; Nelson, Ana Raquel Cortês; Duarte, Fernando Hiago da Silva; Prado, Nanete Caroline da Costa; Rodrigues, Eduardo Henrique Fagundes

2016-01-01

Abstract Objective: to identify the predictive factors for the nursing diagnoses in people living with Acquired Immune Deficiency Syndrome. Method: a cross-sectional study, undertaken with 113 people living with AIDS. The data were collected using an interview script and physical examination. Logistic regression was used for the data analysis, considering a level of significance of 10%. Results: the predictive factors identified were: for the nursing diagnosis of knowledge deficit-inadequate following of instructions and verbalization of the problem; for the nursing diagnosis of failure to adhere - years of study, behavior indicative of failure to adhere, participation in the treatment and forgetfulness; for the nursing diagnosis of sexual dysfunction - family income, reduced frequency of sexual practice, perceived deficit in sexual desire, perceived limitations imposed by the disease and altered body function. Conclusion: the predictive factors for these nursing diagnoses involved sociodemographic and clinical characteristics, defining characteristics, and related factors, which must be taken into consideration during the assistance provided by the nurse. PMID:27384466

Protein structure prediction with local adjust tabu search algorithm

PubMed Central

2014-01-01

Background Protein folding structure prediction is one of the most challenging problems in the bioinformatics domain. Because of the complexity of the realistic protein structure, the simplified structure model and the computational method should be adopted in the research. The AB off-lattice model is one of the simplification models, which only considers two classes of amino acids, hydrophobic (A) residues and hydrophilic (B) residues. Results The main work of this paper is to discuss how to optimize the lowest energy configurations in 2D off-lattice model and 3D off-lattice model by using Fibonacci sequences and real protein sequences. In order to avoid falling into local minimum and faster convergence to the global minimum, we introduce a novel method (SATS) to the protein structure problem, which combines simulated annealing algorithm and tabu search algorithm. Various strategies, such as the new encoding strategy, the adaptive neighborhood generation strategy and the local adjustment strategy, are adopted successfully for high-speed searching the optimal conformation corresponds to the lowest energy of the protein sequences. Experimental results show that some of the results obtained by the improved SATS are better than those reported in previous literatures, and we can sure that the lowest energy folding state for short Fibonacci sequences have been found. Conclusions Although the off-lattice models is not very realistic, they can reflect some important characteristics of the realistic protein. It can be found that 3D off-lattice model is more like native folding structure of the realistic protein than 2D off-lattice model. In addition, compared with some previous researches, the proposed hybrid algorithm can more effectively and more quickly search the spatial folding structure of a protein chain. PMID:25474708

Predictive factors for severe and febrile neutropenia during docetaxel chemotherapy for castration-resistant prostate cancer.

PubMed

Shigeta, Keisuke; Kosaka, Takeo; Yazawa, Satoshi; Yasumizu, Yota; Mizuno, Ryuichi; Nagata, Hirohiko; Shinoda, Kazunobu; Morita, Shinya; Miyajima, Akira; Kikuchi, Eiji; Nakagawa, Ken; Hasegawa, Shintaro; Oya, Mototsugu

2015-06-01

The aim of this study is to identify factors that increase the occurrence of severe neutropenia (SN) and febrile neutropenia (FN) during docetaxel treatment for castration-resistant prostate cancer (CRPC). We retrospectively reviewed 258 courses during the first three cycles among 95 patients. Docetaxel at a dose of 75 mg/m(2) was administered every 3 or 4 weeks. Patient background, laboratory data, and bone scan results were collected to assess predictive factors for SN and FN. We defined SN as an absolute neutrophil count (ANC) of <500/mm(3) and defined FN as an ANC of <1000/mm(3) with a body temperature of >38.3 °C. The mean age of the patients was 72.6 ± 6.4 years and the mean prostate-specific antigen was 135.4 ± 290.9 ng/ml. During the first three courses of treatment, SN occurred in 72.6% of patients and FN occurred in 9.5 % of patients. Univariate analysis demonstrated that age ≥ 75 years (p = 0.002), number of comorbidities ≥ 1.2 (p = 0.008 and p = 0.006) and previous external beam radiation therapy (EBRT) (p = 0.001) were predictive factors for the development of SN or FN. In multivariate analysis, significant predictors of SN or FN were age ≥ 75 years (hazard ratio [HR] 5.77; p = 0.004) and previous EBRT (HR 14.5; p = 0.012). According to the subgroup analysis dividing SN and FN separately, multivariate analysis also revealed that age ≥ 75 years and previous EBRT were also significant predictors for developing SN (HR 5.09; p = 0.023, HR 12.7; p = 0.020, respectively) and for developing FN (HR 5.45; p = 0.042, HR 7.72; p = 0.015, respectively). Patients aged ≥ 75 years and with a history of localized radiation therapy are at higher risk for significant neutropenic events and require closer surveillance.

Lorentz factor determination for local electric fields in semiconductor devices utilizing hyper-thin dielectrics

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

McPherson, J. W., E-mail: mcpherson.reliability@yahoo.com

The local electric field (the field that distorts, polarizes, and weakens polar molecular bonds in dielectrics) has been investigated for hyper-thin dielectrics. Hyper-thin dielectrics are currently required for advanced semiconductor devices. In the work presented, it is shown that the common practice of using a Lorentz factor of L = 1/3, to describe the local electric field in a dielectric layer, remains valid for hyper-thin dielectrics. However, at the very edge of device structures, a rise in the macroscopic/Maxwell electric field E{sub diel} occurs and this causes a sharp rise in the effective Lorentz factor L{sub eff}. At capacitor and transistor edges,more » L{sub eff} is found to increase to a value 2/3 < L{sub eff} < 1. The increase in L{sub eff} results in a local electric field, at device edge, that is 50%–100% greater than in the bulk of the dielectric. This increase in local electric field serves to weaken polar bonds thus making them more susceptible to breakage by standard Boltzmann and/or current-driven processes. This has important time-dependent dielectric breakdown (TDDB) implications for all electronic devices utilizing polar materials, including GaN devices that suffer from device-edge TDDB.« less

Socioeconomic factors related to surgical treatment for localized, non-small cell lung cancer.

PubMed

Jiang, Xiaqing; Lin, Ge; Islam, K M Monirul

2017-02-01

Various socioeconomic factors were reported to be associated with receiving surgical treatment in localized, non-small cell lung cancer (NSCLC) patients in previous studies. We wanted to assess the impact of residential poverty on receiving surgical treatment in a state-wide population of localized NSCLC, adjusting for demographic, clinical, residence and tumor factors. Data on 970 patients with primary localized NSCLC were collected from the Nebraska Cancer Registry (NCR), and linked with the Nebraska Hospital Discharge Data (NHDD) between 2005 and 2009, as well as the 2010 Census data. Characteristics of patients with and without surgery were compared using Chi-square tests. Unadjusted and adjusted odds ratios (ORs) of receiving surgery for low versus high poverty were generated based on the series of logistic regression models controlling for demographics, comorbidity, residence and tumor histology. Patients who were 65 year old or younger, without comorbidities, single or married, and with adenocarcinoma histologic type were more likely to receive surgery. Without adjustment, poverty was negatively associated with receiving surgery. Patients who resided in low poverty neighborhoods (less than 5% of the households under poverty line) were twice more likely to receive surgery than those who lived in high poverty neighborhoods (more than 15% of the households under poverty line) (OR 2.13, 95% CI 1.33-3.40). After adjustment, poverty was independently and negatively associated with receiving surgery treatment. Residents in low poverty neighborhoods were still about twice more likely to receive surgery than those in high poverty neighborhoods when the other demographic, urban/rural residency and clinical factors were adjusted (ORs 2.01-2.18, all p < 0.05). The mechanism of how living in poverty interacts with other factors and its impact on patient's choice and their chance of getting surgical treatment invites future studies. Copyright © 2017 Elsevier Ltd. All

Biological and Sociocultural Factors During the School Years Predicting Women's Lifetime Educational Attainment.

PubMed

Hendrick, C Emily; Cohen, Alison K; Deardorff, Julianna; Cance, Jessica D

2016-03-01

Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In this study, we examine the roles of sociocultural factors in youth and an understudied biological life event, pubertal timing, in predicting women's lifetime educational attainment. Using data from the National Longitudinal Survey of Youth 1997 cohort (N = 3889), we conducted sequential multivariate linear regression analyses to investigate the influences of macro-level and family-level sociocultural contextual factors in youth (region of country, urbanicity, race/ethnicity, year of birth, household composition, mother's education, and mother's age at first birth) and early menarche, a marker of early pubertal development, on women's educational attainment after age 24. Pubertal timing and all sociocultural factors in youth, other than year of birth, predicted women's lifetime educational attainment in bivariate models. Family factors had the strongest associations. When family factors were added to multivariate models, geographic region in youth, and pubertal timing were no longer significant. Our findings provide additional evidence that family factors should be considered when developing comprehensive and inclusive interventions in childhood and adolescence to promote lifetime educational attainment among girls. © 2016, American School Health Association.

Factors predicting survival in amyotrophic lateral sclerosis patients on non-invasive ventilation.

PubMed

Gonzalez Calzada, Nuria; Prats Soro, Enric; Mateu Gomez, Lluis; Giro Bulta, Esther; Cordoba Izquierdo, Ana; Povedano Panades, Monica; Dorca Sargatal, Jordi; Farrero Muñoz, Eva

2016-01-01

Non invasive ventilation (NIV) improves quality of life and extends survival in amyotrophic lateral sclerosis (ALS) patients. However, few data exist about the factors related to survival. We intended to assess the predictive factors that influence survival in patients after NIV initiation. Patients who started NIV from 2000 to 2014 and were tolerant (compliance ≥ 4 hours) were included; demographic, disease related and respiratory variables at NIV initiation were analysed. Statistical analysis was performed using the Kaplan-Meier test and Cox proportional hazard models. 213 patients were included with median survival from NIV initiation of 13.5 months. In univariate analysis, the identified risk factors for mortality were severity of bulbar involvement (HR 2), Forced Vital Capacity (FVC) % (HR 0.99) and ALSFRS-R (HR 0.97). Multivariate analysis showed that bulbar involvement (HR 1.92) and ALSFRS-R (HR 0.97) were independent predictive factors of survival in patients on NIV. In our study, the two prognostic factors in ALS patients following NIV were the severity of bulbar involvement and ALSFRS-R at the time on NIV initiation. A better assessment of bulbar involvement, including evaluation of the upper airway, and a careful titration on NIV are necessary to optimize treatment efficacy.

Predictive model of thrombospondin-1 and vascular endothelial growth factor in breast tumor tissue.

PubMed

Rohrs, Jennifer A; Sulistio, Christopher D; Finley, Stacey D

2016-01-01

Angiogenesis, the formation of new blood capillaries from pre-existing vessels, is a hallmark of cancer. Thus far, strategies for reducing tumor angiogenesis have focused on inhibiting pro-angiogenic factors, while less is known about the therapeutic effects of mimicking the actions of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important endogenous inhibitor of angiogenesis that has been investigated as an anti-angiogenic agent. TSP1 impedes the growth of new blood vessels in many ways, including crosstalk with pro-angiogenic factors. Due to the complexity of TSP1 signaling, a predictive systems biology model would provide quantitative understanding of the angiogenic balance in tumor tissue. Therefore, we have developed a molecular-detailed, mechanistic model of TSP1 and vascular endothelial growth factor (VEGF), a promoter of angiogenesis, in breast tumor tissue. The model predicts the distribution of the angiogenic factors in tumor tissue, revealing that TSP1 is primarily in an inactive, cleaved form due to the action of proteases, rather than bound to its cellular receptors or to VEGF. The model also predicts the effects of enhancing TSP1's interactions with its receptors and with VEGF. To provide additional predictions that can guide the development of new anti-angiogenic drugs, we simulate administration of exogenous TSP1 mimetics that bind specific targets. The model predicts that the CD47-binding TSP1 mimetic dramatically decreases the ratio of receptor-bound VEGF to receptor-bound TSP1, in favor of anti-angiogenesis. Thus, we have established a model that provides a quantitative framework to study the response to TSP1 mimetics.

Tumour size is the only predictive factor of distant recurrence after pathological complete response to neoadjuvant chemotherapy in patients with large operable or locally advanced breast cancers: a sub-study of EORTC 10994/BIG 1-00 phase III trial.

PubMed

Fei, F; Messina, C; Slaets, L; Chakiba, C; Cameron, D; Bogaerts, J; Bonnefoi, H

2015-02-01

Although achieving a pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) in breast cancer predicts a better outcome, some patients still relapse. The objectives of this study were to describe the types of events in this group of patients and to identify predictive factors for relapse. Patients with large operable or locally advanced breast cancers (T4d tumours were excluded) were randomised to receive either six cycles of anthracycline-based chemotherapy or three cycles of docetaxel followed by three cycles of eprirubicin/docetaxel. pCR was defined as no evidence of residual invasive cancer (or very few scattered tumour cells) in the primary tumour and axillary lymph nodes at surgery. Two Cox regression analyses were performed to identify predictive factors of relapse: one for recurrence-free interval (RFI) and one for distant recurrence-free interval (DRFI). Out of 283 eligible patients who achieved a pCR, 40 (14.1%) and 28 (9.9%) presented an event of interest for the RFI and DRFI analyses, respectively. Five-year RFI, DRFI and overall survival (OS) were 85.3% (95% confidence interval (CI), 80.1-89.3), 89.6% (95% CI, 85.0-92.9) and 91.9% (95% CI, 87.2-94.9), respectively. No predictors for RFI after pCR were identified. For DRFI, tumour size was the only predictor: Hazard ratio (HR) T3 versus T1-2=3.62 (95% CI, 1.66-7.89); HR T4 versus T1-2: HR, 2.80 (95% CI, 0.62-12.64) p=0.0048. In this study, clinical tumour size emerged as the only predictor for DRFI after pCR, with T3 and T4 tumours having an increased risk for distant recurrence compared to T1-2 tumours. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predictive Factors of Headache Resolution After Chiari Type 1 Malformation Surgery.

PubMed

Grangeon, Lou; Puy, Laurent; Gilard, Vianney; Hebant, Benjamin; Langlois, Olivier; Derrey, Stephane; Gerardin, Emmanuel; Maltete, David; Guegan-Massardier, Evelyne; Magne, Nicolas

2018-02-01

Headache is the main and often isolated symptom of patients with Chiari type 1 malformation (CM1). Classically described as occipital and exacerbated by cough, headaches may be poorly characterized, making it difficult to establish CM1 as the underlying cause. Current guidelines for surgical posterior fossa decompression are undefined. The challenge is to distinguish headaches related to CM1 from headaches coincidentally coexisting with CM1. We aimed to determine predictive factors of headache resolution after surgery and applied to our cohort the Chiari Severity Index, a recently developed predictive prognostic score. This retrospective study enrolled 49 patients with CM1 and preoperative headache. Standardized telephone interviews regarding headaches before and after surgery were conducted by the same neurologist; magnetic resonance imaging morphometric analyses were performed by an independent neuroradiologist. Headache resolution was defined as ≥50% reduction in frequency of headache days. Preoperative factors of headache resolution after multivariate analysis were attack duration <5 minutes (P = 0.001), triggering by Valsalva maneuvers (P = 0.003), severe intensity of attack (P = 0.05), occipital location (P = 0.05), and greater number of headache days per month (P = 0.04). These characteristics are part of International Headache Society diagnostic criteria for headache attributed to CM1. No radiologic predictive factor was demonstrated. Postoperative improvement was inversely correlated with Chiari Severity Index. This study confirms the relevance of International Headache Society criteria to identify headaches related to CM1. We propose their systematic use in a preoperative questionnaire. Copyright © 2017 Elsevier Inc. All rights reserved.

Commentary: Factors predicting family court decisions in high-conflict divorce.

PubMed

Stover, Carla Smith

2013-01-01

Factors that predict custody and visitation decisions are an important area of research, especially in the context of high-conflict divorce. In these cases, youths are at significantly higher risk for exposure to ongoing conflict, violence, and triangulation in their parents' disputes. What variables courts and evaluation clinics use to make custody decisions and whether they are the most salient requires further study. The work by Raub and colleagues in this issue extends our understanding of important factors considered by the courts and custody evaluators in high-conflict divorce and points to directions for future research in this area.

Local house prices and mental health.

PubMed

Joshi, Nayan Krishna

2016-03-01

This paper examines the impact of local (county-level) house prices on individual self-reported mental health using individual level data from the United States Behavioral Risk Factor Surveillance System between 2005 and 2011. Exploiting a fixed-effects model that relies on within-county variations, relative to the corresponding changes in other counties, I find that while individuals are likely to experience worse self-reported mental health when local house prices decline, this association is most pronounced for individuals who are least likely to be homeowners. This finding is not consistent with a prediction from a pure wealth mechanism but rather with the hypothesis that house prices act as an economic barometer. I also demonstrate that the association between self-reported mental health and local house prices is not driven by unemployment or foreclosure. The primary result-that lower local house prices have adverse impact on self-reported mental health of homeowners and renters-is consistent with studies using data from the United Kingdom.

Factors Predicting Meniscal Allograft Transplantation Failure

PubMed Central

Parkinson, Ben; Smith, Nicholas; Asplin, Laura; Thompson, Peter; Spalding, Tim

2016-01-01

Background: Meniscal allograft transplantation (MAT) is performed to improve symptoms and function in patients with a meniscal-deficient compartment of the knee. Numerous studies have shown a consistent improvement in patient-reported outcomes, but high failure rates have been reported by some studies. The typical patients undergoing MAT often have multiple other pathologies that require treatment at the time of surgery. The factors that predict failure of a meniscal allograft within this complex patient group are not clearly defined. Purpose: To determine predictors of MAT failure in a large series to refine the indications for surgery and better inform future patients. Study Design: Cohort study; Level of evidence, 3. Methods: All patients undergoing MAT at a single institution between May 2005 and May 2014 with a minimum of 1-year follow-up were prospectively evaluated and included in this study. Failure was defined as removal of the allograft, revision transplantation, or conversion to a joint replacement. Patients were grouped according to the articular cartilage status at the time of the index surgery: group 1, intact or partial-thickness chondral loss; group 2, full-thickness chondral loss 1 condyle; and group 3, full-thickness chondral loss both condyles. The Cox proportional hazards model was used to determine significant predictors of failure, independently of other factors. Kaplan-Meier survival curves were produced for overall survival and significant predictors of failure in the Cox proportional hazards model. Results: There were 125 consecutive MATs performed, with 1 patient lost to follow-up. The median follow-up was 3 years (range, 1-10 years). The 5-year graft survival for the entire cohort was 82% (group 1, 97%; group 2, 82%; group 3, 62%). The probability of failure in group 1 was 85% lower (95% CI, 13%-97%) than in group 3 at any time. The probability of failure with lateral allografts was 76% lower (95% CI, 16%-89%) than medial allografts at

Prognostic Factors Affecting Locally Recurrent Rectal Cancer and Clinical Significance of Hemoglobin

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

Rades, Dirk; Kuhn, Hildegard; Schultze, Juergen

2008-03-15

Purpose: To investigate potential prognostic factors, including hemoglobin levels before and during radiotherapy, for associations with survival and local control in patients with unirradiated locally recurrent rectal cancer. Patients and Methods: Ten potential prognostic factors were investigated in 94 patients receiving radiotherapy for recurrent rectal cancer: age ({<=}68 vs. {>=}69 years), gender, Eastern Cooperative Oncology Group performance status (0-1 vs. 2-3), American Joint Committee on Cancer (AJCC) stage ({<=}II vs. III vs. IV), grading (G1-2 vs. G3), surgery, administration of chemotherapy, radiation dose (equivalent dose in 2-Gy fractions: {<=}50 vs. >50 Gy), and hemoglobin levels before (<12 vs. {>=}12 g/dL)more » and during (majority of levels: <12 vs. {>=}12 g/dL) radiotherapy. Multivariate analyses were performed, including hemoglobin levels, either before or during radiotherapy (not both) because these are confounding variables. Results: Improved survival was associated with better performance status (p < 0.001), lower AJCC stage (p = 0.023), surgery (p = 0.011), chemotherapy (p = 0.003), and hemoglobin levels {>=}12 g/dL both before (p = 0.031) and during (p < 0.001) radiotherapy. On multivariate analyses, performance status, AJCC stage, and hemoglobin levels during radiotherapy maintained significance. Improved local control was associated with better performance status (p = 0.040), lower AJCC stage (p = 0.010), lower grading (p = 0.012), surgery (p < 0.001), chemotherapy (p < 0.001), and hemoglobin levels {>=}12 g/dL before (p < 0.001) and during (p < 0.001) radiotherapy. On multivariate analyses, chemotherapy, grading, and hemoglobin levels before and during radiotherapy remained significant. Subgroup analyses of the patients having surgery demonstrated the extent of resection to be significantly associated with local control (p = 0.011) but not with survival (p = 0.45). Conclusion: Predictors for outcome in patients who received

Exploring Local Level Factors Shaping the Implementation of a Blended Learning Module for Information and Geospatial Literacy in Ontario

ERIC Educational Resources Information Center

Vine, Michelle M.; Chiappetta-Swanson, Catherine; Maclachlan, John; Brodeur, Jason J.; Bagg, Julianne

2016-01-01

The objectives of this research study were to examine local level factors shaping the implementation of a blended pedagogical approach for geospatial- and information-literacy, and to understand implementer satisfaction. As such, we addressed the following research questions: What local-level factors shape the implementation of the blended…

Predictive factors for structural remission using abatacept: results from the ABROAD study.

PubMed

Murakami, Kosaku; Sekiguchi, Masahiro; Hirata, Shintaro; Fujii, Takao; Matsui, Kiyoshi; Morita, Satoshi; Ohmura, Koichiro; Kawahito, Yutaka; Nishimoto, Norihiro; Mimori, Tsuneyo; Sano, Hajime

2018-05-29

To investigate the effect of abatacept (ABA) on preventing joint destruction in biological disease-modifying anti-rheumatic drug (bDMARD)-naïve rheumatoid arthritis (RA) patients in real-world clinical practice. RA patients were collected from the ABROAD (ABatacept Research Outcomes as a First-line Biological Agent in the Real WorlD) study cohort. They had moderate or high disease activity and were treated with ABA as a first-line bDMARD. Radiographic change between baseline and 1 year after ABA treatment was assessed with the van der Heijde's modified total Sharp score (mTSS). Predictive factors for structural remission (St-REM), defined as ΔmTSS ≤0.5/year, were determined. Among 118 patients, 81 (67.5%) achieved St-REM. Disease duration <3 years (odds ratio (OR) = 3.152, p = 0.007) and slower radiographic progression (shown as "baseline mTSS/year <3", OR = 3.727, p = 0.004) were independently significant baseline predictive factors for St-REM irrespective of age and sex. St-REM prevalence increased significantly if clinical remission based on the Simplified Disease Activity Index was achieved at least once until 24 weeks after ABA treatment. Shorter disease duration, smaller radiographic progression at baseline, and rapid clinical response were predictive factors for sustained St-REM after ABA therapy in bDMARD-naïve RA patients.

New approach to predict photoallergic potentials of chemicals based on murine local lymph node assay.

PubMed

Maeda, Yosuke; Hirosaki, Haruka; Yamanaka, Hidenori; Takeyoshi, Masahiro

2018-05-23

Photoallergic dermatitis, caused by pharmaceuticals and other consumer products, is a very important issue in human health. However, S10 guidelines of the International Conference on Harmonization do not recommend the existing prediction methods for photoallergy because of their low predictability in human cases. We applied local lymph node assay (LLNA), a reliable, quantitative skin sensitization prediction test, to develop a new photoallergy prediction method. This method involves a three-step approach: (1) ultraviolet (UV) absorption analysis; (2) determination of no observed adverse effect level for skin phototoxicity based on LLNA; and (3) photoallergy evaluation based on LLNA. Photoallergic potential of chemicals was evaluated by comparing lymph node cell proliferation among groups treated with chemicals with minimal effect levels of skin sensitization and skin phototoxicity under UV irradiation (UV+) or non-UV irradiation (UV-). A case showing significant difference (P < .05) in lymph node cell proliferation rates between UV- and UV+ groups was considered positive for photoallergic reaction. After testing 13 chemicals, seven human photoallergens tested positive and the other six, with no evidence of causing photoallergic dermatitis or UV absorption, tested negative. Among these chemicals, both doxycycline hydrochloride and minocycline hydrochloride were tetracycline antibiotics with different photoallergic properties, and the new method clearly distinguished between the photoallergic properties of these chemicals. These findings suggested high predictability of our method; therefore, it is promising and effective in predicting human photoallergens. Copyright © 2018 John Wiley & Sons, Ltd.

Improving Predictions with Reliable Extrapolation Schemes and Better Understanding of Factorization

NASA Astrophysics Data System (ADS)

More, Sushant N.

New insights into the inter-nucleon interactions, developments in many-body technology, and the surge in computational capabilities has led to phenomenal progress in low-energy nuclear physics in the past few years. Nonetheless, many calculations still lack a robust uncertainty quantification which is essential for making reliable predictions. In this work we investigate two distinct sources of uncertainty and develop ways to account for them. Harmonic oscillator basis expansions are widely used in ab-initio nuclear structure calculations. Finite computational resources usually require that the basis be truncated before observables are fully converged, necessitating reliable extrapolation schemes. It has been demonstrated recently that errors introduced from basis truncation can be taken into account by focusing on the infrared and ultraviolet cutoffs induced by a truncated basis. We show that a finite oscillator basis effectively imposes a hard-wall boundary condition in coordinate space. We accurately determine the position of the hard-wall as a function of oscillator space parameters, derive infrared extrapolation formulas for the energy and other observables, and discuss the extension of this approach to higher angular momentum and to other localized bases. We exploit the duality of the harmonic oscillator to account for the errors introduced by a finite ultraviolet cutoff. Nucleon knockout reactions have been widely used to study and understand nuclear properties. Such an analysis implicitly assumes that the effects of the probe can be separated from the physics of the target nucleus. This factorization between nuclear structure and reaction components depends on the renormalization scale and scheme, and has not been well understood. But it is potentially critical for interpreting experiments and for extracting process-independent nuclear properties. We use a class of unitary transformations called the similarity renormalization group (SRG) transformations to

Prediction of alpha factor values for fine pore aeration systems.

PubMed

Gillot, S; Héduit, A

2008-01-01

The objective of this work was to analyse the impact of different geometric and operating parameters on the alpha factor value for fine bubble aeration systems equipped with EPDM membrane diffusers. Measurements have been performed on nitrifying plants operating under extended aeration and treating mainly domestic wastewater. Measurements performed on 14 nitrifying plants showed that, for domestic wastewater treatment under very low F/M ratios, the alpha factor is comprised between 0.44 and 0.98. A new composite variable (the Equivalent Contact Time, ECT) has been defined and makes it possible for a given aeration tank, knowing the MCRT, the clean water oxygen transfer coefficient and the supplied air flow rate, to predict the alpha factor value. ECT combines the effect on mass transfer of all generally accepted factors affecting oxygen transfer performances (air flow rate, diffuser submergence, horizontal flow). (c) IWA Publishing 2008.

Trait-specific responses of wild bee communities to landscape composition, configuration and local factors.

PubMed

Hopfenmüller, Sebastian; Steffan-Dewenter, Ingolf; Holzschuh, Andrea

2014-01-01

Land-use intensification and loss of semi-natural habitats have induced a severe decline of bee diversity in agricultural landscapes. Semi-natural habitats like calcareous grasslands are among the most important bee habitats in central Europe, but they are threatened by decreasing habitat area and quality, and by homogenization of the surrounding landscape affecting both landscape composition and configuration. In this study we tested the importance of habitat area, quality and connectivity as well as landscape composition and configuration on wild bees in calcareous grasslands. We made detailed trait-specific analyses as bees with different traits might differ in their response to the tested factors. Species richness and abundance of wild bees were surveyed on 23 calcareous grassland patches in Southern Germany with independent gradients in local and landscape factors. Total wild bee richness was positively affected by complex landscape configuration, large habitat area and high habitat quality (i.e. steep slopes). Cuckoo bee richness was positively affected by complex landscape configuration and large habitat area whereas habitat specialists were only affected by the local factors habitat area and habitat quality. Small social generalists were positively influenced by habitat area whereas large social generalists (bumblebees) were positively affected by landscape composition (high percentage of semi-natural habitats). Our results emphasize a strong dependence of habitat specialists on local habitat characteristics, whereas cuckoo bees and bumblebees are more likely affected by the surrounding landscape. We conclude that a combination of large high-quality patches and heterogeneous landscapes maintains high bee species richness and communities with diverse trait composition. Such diverse communities might stabilize pollination services provided to crops and wild plants on local and landscape scales.

Trait-Specific Responses of Wild Bee Communities to Landscape Composition, Configuration and Local Factors

PubMed Central

Hopfenmüller, Sebastian; Steffan-Dewenter, Ingolf; Holzschuh, Andrea

2014-01-01

Land-use intensification and loss of semi-natural habitats have induced a severe decline of bee diversity in agricultural landscapes. Semi-natural habitats like calcareous grasslands are among the most important bee habitats in central Europe, but they are threatened by decreasing habitat area and quality, and by homogenization of the surrounding landscape affecting both landscape composition and configuration. In this study we tested the importance of habitat area, quality and connectivity as well as landscape composition and configuration on wild bees in calcareous grasslands. We made detailed trait-specific analyses as bees with different traits might differ in their response to the tested factors. Species richness and abundance of wild bees were surveyed on 23 calcareous grassland patches in Southern Germany with independent gradients in local and landscape factors. Total wild bee richness was positively affected by complex landscape configuration, large habitat area and high habitat quality (i.e. steep slopes). Cuckoo bee richness was positively affected by complex landscape configuration and large habitat area whereas habitat specialists were only affected by the local factors habitat area and habitat quality. Small social generalists were positively influenced by habitat area whereas large social generalists (bumblebees) were positively affected by landscape composition (high percentage of semi-natural habitats). Our results emphasize a strong dependence of habitat specialists on local habitat characteristics, whereas cuckoo bees and bumblebees are more likely affected by the surrounding landscape. We conclude that a combination of large high-quality patches and heterogeneous landscapes maintains high bee species richness and communities with diverse trait composition. Such diverse communities might stabilize pollination services provided to crops and wild plants on local and landscape scales. PMID:25137311

Personality and Defense Styles: Clinical Specificities and Predictive Factors of Alcohol Use Disorder in Women.

PubMed

Ribadier, Aurélien; Dorard, Géraldine; Varescon, Isabelle

2016-01-01

This study investigated personality traits and defense styles in order to determine clinical specificities and predictive factors of alcohol use disorders (AUDs) in women. A female sample, composed of AUD outpatients (n = 48) and a control group (n = 50), completed a sociodemographic self-report and questionnaires assessing personality traits (BFI), defense mechanisms and defense styles (DSQ-40). Comparative and correlational analyses, as well as univariate and multivariate logistic regressions, were performed. AUD women presented with higher neuroticism and lower extraversion and conscientiousness. They used less mature and more neurotic and immature defense styles than the control group. Concerning personality traits, high neuroticism and lower conscientiousness were predictive of AUD, as well as low mature, high neurotic, and immature defense styles. Including personality traits and defense styles in a logistic model, high neuroticism was the only AUD predictive factor. AUD women presented clinical specificities and predictive factors in personality traits and defense styles that must be taken into account in AUD studies. Implications for specific treatment for women are discussed.

A Dynamical Model Reveals Gene Co-Localizations in Nucleus

PubMed Central

Yao, Ye; Lin, Wei; Hennessy, Conor; Fraser, Peter; Feng, Jianfeng

2011-01-01

Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency- or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes. PMID:21760760

Progression of diffuse esophageal spasm to achalasia: incidence and predictive factors.

PubMed

Fontes, L H S; Herbella, F A M; Rodriguez, T N; Trivino, T; Farah, J F M

2013-07-01

The progression of certain primary esophageal motor disorders to achalasia has been documented; however, the true incidence of this decay is still elusive. This study aims to evaluate: (i) the incidence of the progression of diffuse esophageal spasm to achalasia, and (ii) predictive factors to this progression. Thirty-five patients (mean age 53 years, 80% females) with a manometric picture of diffuse esophageal spasm were followed for at least 1 year. Patients with gastroesophageal reflux disease confirmed by pH monitoring or systemic diseases that may affect esophageal motility were excluded. Esophageal manometry was repeated in all patients. Five (14%) of the patients progressed to achalasia at a mean follow-up of 2.1 (range 1-4) years. Demographic characteristics were not predictive of transition to achalasia, while dysphagia (P= 0.005) as the main symptom and the wave amplitude of simultaneous waves less than 50 mmHg (P= 0.003) were statistically significant. In conclusion, the transition of diffuse esophageal spasm to achalasia is not frequent at a 2-year follow-up. Dysphagia and simultaneous waves with low amplitude are predictive factors for this degeneration. © 2012 Copyright the Authors. Journal compilation © 2012, Wiley Periodicals, Inc. and the International Society for Diseases of the Esophagus.

Factors Perceived as Influencing Local Health Department Involvement in Mental Health.

PubMed

Purtle, Jonathan; Peters, Rachel; Kolker, Jennifer; Klassen, Ann C

2017-01-01

Local health departments (LHDs) are potentially well positioned to implement population-based approaches to mental health promotion, but research indicates that most LHDs are not substantively engaged in activities to address mental health. Little is known about factors that influence if and how LHDs address population mental health. The objectives of this qualitative study were to (1) understand how LHD officials perceive population mental health; (2) identify factors that influence these perceptions and LHD activities to address population mental health; and (3) develop an empirically derived conceptual framework of LHD engagement in population mental health. Twenty-one semi-structured interviews were conducted with a purposive sample of LHD officials and analyzed using thematic content analysis in 2014-2015. Transcripts were double coded, inter-rater reliability statistics were calculated, and categories with κ ≥0.60 were retained. Respondents perceived mental health as a public health issue and expressed that it has emerged as a priority through community health needs assessment processes, such as those conducted for health department accreditation. However, most LHDs were not substantively engaged in population mental health activities because of limited resources, knowledge, data, and hesitancy to infringe upon the territory of local behavioral health agencies. LHDs and local behavioral health agencies had difficulty communicating and collaborating because of divergent perspectives and financing arrangements. LHD officials are eager to embrace population mental health, but resources, training and education, and systems-level changes are needed. Contemporary reforms to the structure and financing of the U.S. health system offer opportunities to address these challenges. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

As of 2012, what are the key predictive risk factors for pressure ulcers? Developing French guidelines for clinical practice.

PubMed

Michel, J-M; Willebois, S; Ribinik, P; Barrois, B; Colin, D; Passadori, Y

2012-10-01

An evaluation of predictive risk factors for pressure ulcers is essential in development of a preventive strategy on admission to hospitals and/or nursing homes. Identification of the predictive factors for pressure ulcers as of 2012. Systematic review of the literature querying the databases PASCAL Biomed, Cochrane Library and PubMed from 2000 through 2010. Immobility should be considered as a predictive risk factor for pressure ulcers (grade B). Undernutrition/malnutrition may also be a predictive risk factor for pressure ulcers (grade C). Even if the level of evidence is low, once these risk factors have been detected, management is essential. Sensitizing and mobilizing health care teams requires training in ways of tracking and screening. According to the experts, risk scales should be used. As decision aids, they should always be balanced and complemented by the clinical judgment of the treatment team. According to experts, it is important to know and predictively evaluate risk of pressure ulcers at the time of hospital admission. The predictive risk factors found in this study are identical to those highlighted at the 2001 consensus conference of which was PERSE was the promoter. Copyright © 2012. Published by Elsevier Masson SAS.

Moving beyond GPA: Alternative Measures of Success and Predictive Factors in Honors Programs

ERIC Educational Resources Information Center

Mould, Tom; DeLoach, Stephen B.

2017-01-01

While studies of predictive factors for success in honors have been increasingly creative and expansive on what these factors might include, they have rarely challenged the dominant, virtually monolithic definitions of success. The majority of studies measure success either by collegiate grade point averages (GPAs) or retention rates in honors,…

Local and regional factors affecting atmospheric mercury speciation at a remote location

USGS Publications Warehouse

Manolopoulos, H.; Schauer, J.J.; Purcell, M.D.; Rudolph, T.M.; Olson, M.L.; Rodger, B.; Krabbenhoft, D.P.

2007-01-01

Atmospheric concentrations of elemental (Hg0), reactive gaseous (RGM), and particulate (PHg) mercury were measured at two remote sites in the midwestern United States. Concurrent measurements of Hg0, PHg, and RGM obtained at Devil's Lake and Mt. Horeb, located approximately 65 km apart, showed that Hg0 and PHg concentrations were affected by regional, as well as local sources, while RGM was mainly impacted by local sources. Plumes reaching the Devil's Lake site from a nearby coal-fired power plant significantly impacted SO2 and RGM concentrations at Devil's Lake, but had little impact on Hg0. Our findings suggest that traditional modeling approaches to assess sources of mercury deposited that utilize source emissions and large-scale grids may not be sufficient to predict mercury deposition at sensitive locations due to the importance of small-scale sources and processes. We suggest the use of a receptor-based monitoring to better understand mercury source-receptor relationships. ?? 2007 NRC Canada.

From local uncertainty to global predictions: Making predictions on fractal basins

PubMed Central

2018-01-01

In nonlinear systems long term dynamics is governed by the attractors present in phase space. The presence of a chaotic saddle gives rise to basins of attraction with fractal boundaries and sometimes even to Wada boundaries. These two phenomena involve extreme difficulties in the prediction of the future state of the system. However, we show here that it is possible to make statistical predictions even if we do not have any previous knowledge of the initial conditions or the time series of the system until it reaches its final state. In this work, we develop a general method to make statistical predictions in systems with fractal basins. In particular, we have applied this new method to the Duffing oscillator for a choice of parameters where the system possesses the Wada property. We have computed the statistical properties of the Duffing oscillator for different phase space resolutions, to obtain information about the global dynamics of the system. The key idea is that the fraction of initial conditions that evolve towards each attractor is scale free—which we illustrate numerically. We have also shown numerically how having partial information about the initial conditions of the system does not improve in general the predictions in the Wada regions. PMID:29668687

mPLR-Loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2015-03-15

Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers' convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer). Copyright © 2014 Elsevier Inc. All rights reserved.

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

PubMed

Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

2013-04-01

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Predictive factors for postoperative severe hypocalcaemia after parathyroidectomy for primary hyperparathyroidism.

PubMed

Crea, Nicola; Pata, Giacomo; Casella, Claudio; Cappelli, Carlo; Salerni, Bruno

2012-03-01

Hypocalcaemia is a complication of parathyroidectomy. We retrospectively analyzed data on patients who underwent parathyroidectomy for primary hyperparathyroidism (pHPT) to identify predictive factors for severe postoperative hypocalcaemia. Since 2004 we performed 87 parathyroidectomies for pHPT. We divided the patients into two groups: subjects who presented with postoperative hypocalcaemia (group B) or otherwise (group A). We looked for a correlation between several variables and the incidence of postoperative hypocalcaemia. The median calcemia in group B (19 patients) was 6.9 mg/dL on the first postoperative day and 7.6 mg/dL on the third day. We observed hypocalcemia related clinical symptoms in every patient. In all 19 cases the reduction of intraoperative parathyroid hormone above 85 per cent after parathyroidectomy was related to the development of severe postoperative hypocalcaemia (P = 0.042). We found that the reduction of intraoperative parathyroid hormone over 85 per cent after parathyroidectomy can be considered a reliable predictive factor of postoperative hypocalcaemia after parathyroidectomy for primary hyperparathyroidism.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Factors predicting recovery from suicide in attempted suicide patients.

PubMed

Sun, Fan-Ko; Lu, Chu-Yun; Tseng, Yun Shan; Chiang, Chun-Ying

2017-12-01

The aim of this study was to explore the factors predicting suicide recovery and to provide guidance for healthcare professionals when caring for individuals who have attempted suicide. The high rate of suicide is a global health problem. Suicide prevention has become an important issue in contemporary mental health. Most suicide research has focused on suicidal prevention and care. There is a lack of research on the factors predicting suicidal recovery. A cross-sectional design was adopted. A correlational study with a purposive sample of 160 individuals from a suicide prevention centre in southern Taiwan was conducted. The questionnaires included the Brief Symptom Rating Scale-5, Suicidal Recovery Assessment Scale and Beck Hopelessness Scale. Descriptive statistics and linear regressions were used for the analysis. The mean age of the participants was 40.2 years. Many participants were striving to make changes to create a more stable and fulfilling life, had an improved recovery from suicide and had a good ability to adapt or solve problems. The linear regression showed that the Beck Hopelessness Scale scores (ß = -.551, p < .001) and Brief Symptom Rating Scale-5 (ß = -.218, p = .003) and past suicidal behaviour (ß = -.145, p = .008) were significant predictors of individuals' recovery from suicide. They accounted for 57.1% of the variance. Suicidal individuals who have a lower level of hopelessness, a better ability to cope with their mental condition and fewer past suicidal behaviours may better recover from suicide attempts. The nurses could use the results of this study to predict recovery from suicide in patients with attempted suicide. © 2017 John Wiley & Sons Ltd.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further

Cardiovascular risk factors predictive for survival and morbidity-free survival in the oldest-old Framingham Heart Study participants.

PubMed

Terry, Dellara F; Pencina, Michael J; Vasan, Ramachandran S; Murabito, Joanne M; Wolf, Philip A; Hayes, Margaret Kelly; Levy, Daniel; D'Agostino, Ralph B; Benjamin, Emelia J

2005-11-01

To examine whether midlife cardiovascular risk factors predict survival and survival free of major comorbidities to the age of 85. Prospective community-based cohort study. Framingham Heart Study, Massachusetts. Two thousand five hundred thirty-one individuals (1,422 women) who attended at least two examinations between the ages of 40 and 50. Risk factors were classified at routine examinations performed between the ages of 40 and 50. Stepwise sex-adjusted logistic regression models predicting the outcomes of survival and survival free of morbidity to age 85 were selected from the following risk factors: systolic and diastolic blood pressure, total serum cholesterol, glucose intolerance, cigarette smoking, education, body mass index, physical activity index, pulse pressure, antihypertensive medication, and electrocardiographic left ventricular hypertrophy. More than one-third of the study sample survived to age 85, and 22% of the original study sample survived free of morbidity. Lower midlife blood pressure and total cholesterol levels, absence of glucose intolerance, nonsmoking status, higher educational attainment, and female sex predicted overall and morbidity-free survival. The predicted probability of survival to age 85 fell in the presence of accumulating risk factors: 37% for men with no risk factors to 2% with all five risk factors and 65% for women with no risk factors to 14% with all five risk factors. Lower levels of key cardiovascular risk factors in middle age predicted overall survival and major morbidity-free survival to age 85. Recognizing and modifying these factors may delay, if not prevent, age-related morbidity and mortality.

The predictive factors for lymph node metastasis in early gastric cancer: A clinical study.

PubMed

Wang, Yinzhong

2015-01-01

To detect the clinicopathological factors associated with lymph node metastases in early gastric cancer. We retrospectively evaluated the distribution of metastatic nodes in 198 patients with early gastric cancer treated in our hospital between May 2008 and January 2015, the clinicopathological factors including age, gender, tumor location, tumor size, macroscopic type, depth of invasion, histological type and venous invasion were studied, and the relationship between various parameters and lymph node metastases was analyzed. In this study, one hundred and ninety-eight patients with early gastric cancer were included, and lymph node metastasis was detected in 28 patients. Univariate analysis revealed a close relationship between tumor size, depth of invasion, histological type, venous invasion, local ulceration and lymph node metastases. Multivariate analysis revealed that the five factors were independent risk factors for lymph node metastases. The clinicopathological parameters including tumor size, depth of invasion, local ulceration, histological type and venous invasion are closely correlated with lymph node metastases, should be paid high attention in early gastric cancer patients.

The financial impact of hospitals on the local economy--2 new factors.

PubMed

Rotarius, Timothy; Liberman, Aaron

2014-01-01

This research effort presents a descriptive analysis of the financial impact that several hospitals have on their local economy. An earlier study published by the authors included 3 distinct, yet overlapping components of financial impact: (1) the hospital system as a major health care provider, (2) the hospital system as a large employer, and (3) the hospital system as an entity whose employees contribute greatly to their local community. This new study added additional financial impact factors: (4) the hospital system as an organization committed to major construction projects in pursuit of its health services mission, and (5) the hospital system as an entity that pays taxes to government agencies. The inextricable relationship of these 5 categories both increases and enhances the impact of the hospital system on the local region. The results of this updated and expanded analysis suggest strongly that the hospital system represents 1 of the primary contributors to the economy of the region. The hospital system adds $3 billion to the $28 billion local economy, which means that the hospital system and its employees are responsible for 10.7% of the total economic prowess of the region.

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

PubMed Central

Eastin, Matthew D.; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron

2014-01-01

Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors—all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C—the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. PMID:24957546

Inflammatory Genes and Psychological Factors Predict Induced Shoulder Pain Phenotype

PubMed Central

George, Steven Z.; Parr, Jeffrey J.; Wallace, Margaret R.; Wu, Samuel S.; Borsa, Paul A.; Dai, Yunfeng; Fillingim, Roger B.

2014-01-01

Purpose The pain experience has multiple influences but little is known about how specific biological and psychological factors interact to influence pain responses. The current study investigated the combined influences of genetic (pro-inflammatory) and psychological factors on several pre-clinical shoulder pain phenotypes. Methods An exercise-induced shoulder injury model was used, and a priori selected genetic (IL1B, TNF/LTA region, IL6 single nucleotide polymorphisms, SNPs) and psychological (anxiety, depressive symptoms, pain catastrophizing, fear of pain, kinesiophobia) factors were included as the predictors of interest. The phenotypes were pain intensity (5-day average and peak reported on numerical rating scale), upper-extremity disability (5-day average and peak reported on the QuickDASH instrument), and duration of shoulder pain (in days). Results After controlling for age, sex, and race, the genetic and psychological predictors were entered separately as main effects and interaction terms in regression models for each pain phenotype. Results from the recruited cohort (n = 190) indicated strong statistical evidence for the interactions between 1) TNF/LTA SNP rs2229094 and depressive symptoms for average pain intensity and duration and 2) IL1B two-SNP diplotype and kinesiophobia for average shoulder pain intensity. Moderate statistical evidence for prediction of additional shoulder pain phenotypes included interactions of kinesiophobia, fear of pain, or depressive symptoms with TNF/LTA rs2229094 and IL1B. Conclusion These findings support the combined predictive ability of specific genetic and psychological factors for shoulder pain phenotypes by revealing novel combinations that may merit further investigation in clinical cohorts, to determine their involvement in the transition from acute to chronic pain conditions. PMID:24598699

Local perceptions on factors influencing the introduction of international healthcare accreditation in Pakistan.

PubMed

Sax, Sylvia; Marx, Michael

2014-12-01

One contributor to poor health outcomes in developing countries is weak health systems; key to strengthening them are interventions to improve quality of health services. Though the value of healthcare accreditation is increasingly recognized, there are few case studies exploring its adaptation in developing countries. The aim of our study in Pakistan was to identify perceived factors influencing the adaptation of international healthcare accreditation within a developing country context. We used qualitative methods including semi-structured interviews, a structured group discussion, focus groups and non-participant observation of management meetings. Data analysis used a grounded theory approach and a conceptual framework adapted from implementation science. Using our conceptual framework categories of 'inner' and 'outer' setting, we found six perceived inner health system factors that could influence the introduction of healthcare accreditation and two 'outer' setting factors, perceived as external to the health system but able to influence its introduction. Our research identified that there is no 'one size fits all' approach to introducing healthcare accreditation as a means to improve healthcare quality. Those planning to support healthcare accreditation, such as national and provincial ministries and international development partners, need to understand how the three components of healthcare accreditation fit into the local health system and into the broader political and social environment. In our setting this included moving to supportive and transparent external evaluation mechanisms, with a first step of using locally developed and agreed standards. In addition, sustainable implementation of the three components was seen as a major challenge, especially establishment of a well-managed, transparent accreditation agency able to lead processes such as training and support for peer surveyors. Consideration of local change mechanisms and cultural practices is

Factors associated with diabetes mellitus prediction among pregnant Arab subjects with gestational diabetes.

PubMed

Aljohani, Naji; Al Serehi, Amal; Ahmed, Amjad M; Buhary, Badr Aldin M; Alzahrani, Saad; At-Taras, Eeman; Almujally, Najla; Alsharqi, Maha; Alqahtani, Mohammed; Almalki, Mussa

2015-01-01

There is scarcity of available information on the possible significant risk factors related to diabetes mellitus (DM) prediction among expectant Saudi mothers with gestational diabetes mellitus (GDM). The present study is the first to identify such risk factors in the Arab cohort. A total of 300 pregnant subjects (mean age 33.45 ± 6.5 years) were randomly selected from all the deliveries registered at the Obstetrics Department of King Fahad Medical City, Riyadh Saudi Arabia from April 2011 to March 2013. Demographic and baseline glycemic information were collected. A total of 7 highly significant and independent risk factors were identified: age, obesity, and family history of DM, GDM < 20 weeks, macrosomia, insulin therapy and recurrent GDM. Among these factors, subjects who had insulin therapy use are 5 times more likely to develop DMT2 (p-value 3.94 × 10(-14)) followed by recurrent GDM [odds-ratio 4.69 (Confidence Interval 2.34-4.84); P = 1.24 × 10(-13)). The identification of the risk factors mentioned with their respective predictive powers in the detection of DMT2 needs to be taken seriously in the post-partum assessment of Saudi pregnant patients at highest risk.

Antipsychotic therapeutic drug monitoring: psychiatrists’ attitudes and factors predicting likely future use

PubMed Central

Law, Suzanne; Haddad, Peter M.; Chaudhry, Imran B.; Husain, Nusrat; Drake, Richard J.; Flanagan, Robert J.; David, Anthony S.

2015-01-01

Background: This study aimed to explore predictive factors for future use of therapeutic drug monitoring (TDM) and to further examine psychiatrists’ current prescribing practices and perspectives regarding antipsychotic TDM using plasma concentrations. Method: A cross-sectional study for consultant psychiatrists using a postal questionnaire was conducted in north-west England. Data were combined with those of a previous London-based study and principal axis factor analysis was conducted to identify predictors of future use of TDM. Results: Most of the 181 participants (82.9%, 95% confidence interval 76.7–87.7%) agreed that ‘if TDM for antipsychotics were readily available, I would use it’. Factor analysis identified five factors from the original 35 items regarding TDM. Four of the factors significantly predicted likely future use of antipsychotic TDM and together explained 40% of the variance in a multivariate linear regression model. Likely future use increased with positive attitudes and expectations, and decreased with potential barriers, negative attitudes and negative expectations. Scientific perspectives of TDM and psychiatrist characteristics were not significant predictors. Conclusion: Most senior psychiatrists indicated that they would use antipsychotic TDM if available. However, psychiatrists’ attitudes and expectations and the potential barriers need to be addressed, in addition to the scientific evidence, before widespread use of antipsychotic TDM is likely in clinical practice. PMID:26301077

Factors Predicting Burnout Among Chaplains: Compassion Satisfaction, Organizational Factors, and the Mediators of Mindful Self-Care and Secondary Traumatic Stress.

PubMed

Hotchkiss, Jason T; Lesher, Ruth

2018-06-01

This study predicted Burnout from the self-care practices, compassion satisfaction, secondary traumatic stress, and organizational factors among chaplains who participated from all 50 states (N = 534). A hierarchical regression model indicated that the combined effect of compassion satisfaction, secondary traumatic stress, mindful self-care, demographic, and organizational factors explained 83.2% of the variance in Burnout. Chaplains serving in a hospital were slightly more at risk for Burnout than those in hospice or other settings. Organizational factors that most predicted Burnout were feeling bogged down by the "system" (25.7%) and an overwhelming caseload (19.9%). Each self-care category was a statistically significant protective factor against Burnout risk. The strongest protective factors against Burnout in order of strength were self-compassion and purpose, supportive structure, mindful self-awareness, mindful relaxation, supportive relationships, and physical care. For secondary traumatic stress, supportive structure, mindful self-awareness, and self-compassion and purpose were the strongest protective factors. Chaplains who engaged in multiple and frequent self-care strategies experienced higher professional quality of life and low Burnout risk. In the chaplain's journey toward wellness, a reflective practice of feeling good about doing good and mindful self-care are vital. The significance, implications, and limitations of the study were discussed.

REJUVENATION OF PERIOSTEAL CHONDROGENESIS USING LOCAL GROWTH FACTOR INJECTION

PubMed Central

Reinholz, G.G.; Fitzsimmons, J.S.; Casper, M.; Ruesink, T.J.; Chung, H.W.; Schagemann, J.C.; O’Driscoll, S.W.

2015-01-01

Objective To examine the potential for rejuvenation of aged periosteum by local injection of transforming growth factor-beta1 (TGF-β1) and insulin-like growth factor-1 (IGF-1) alone or in combination to induce cambium cell proliferation and enhance in vitro periosteal cartilage formation. Methods A total of 367 New Zealand white rabbits (6, 12, and 24+ month-old) received subperiosteal injections of TGF-β1 and/or IGF-1 percutaneously. After 1, 3, 5, or 7 days, the rabbits were sacrificed and cambium cellularity or in vitro cartilage forming capacity was determined. Results A significant increase in cambium cellularity and thickness, and in vitro cartilage formation was observed after injection of TGF-β1 alone or in combination with IGF-1. In 12 month-old rabbits, mean cambium cellularity increased 5-fold from 49 to 237 cells/mm and in vitro cartilage production increased 12-fold from 0.8 to 9.7 mg seven days after TGF-β1 (200 ng) injection compared to vehicle controls (p<0.0001). A correlation was observed between cambium cellularity and in vitro cartilage production (R2=0.98). An added benefit of IGF-1 plus TGF-β1 on in vitro cartilage production compared to TGF-β1 alone was observed in the 2 year old rabbits. IGF-1 alone generally had no effect on either cambium cellularity or in vitro cartilage production in any of the age groups. Conclusions These results clearly demonstrate that it is possible to increase cambium cellularity and in vitro cartilage production in aged rabbit periosteum, to levels comparable to younger rabbits, using local injection of TGF-β1 alone or in combination with IGF-1, thereby rejuvenating aged periosteum. PMID:19064326

Intracellular mediators of transforming growth factor beta superfamily signaling localize to endosomes in chicken embryo and mouse lenses in vivo.

PubMed

Rajagopal, Ramya; Ishii, Shunsuke; Beebe, David C

2007-06-25

Endocytosis is a key regulator of growth factor signaling pathways. Recent studies showed that the localization to endosomes of intracellular mediators of growth factor signaling may be required for their function. Although there is substantial evidence linking endocytosis and growth factor signaling in cultured cells, there has been little study of the endosomal localization of signaling components in intact tissues or organs. Proteins that are downstream of the transforming growth factor-beta superfamily signaling pathway were found on endosomes in chicken embryo and postnatal mouse lenses, which depend on signaling by members of the TGFbeta superfamily for their normal development. Phosphorylated Smad1 (pSmad1), pSmad2, Smad4, Smad7, the transcriptional repressors c-Ski and TGIF and the adapter molecules Smad anchor for receptor activation (SARA) and C184M, localized to EEA-1- and Rab5-positive vesicles in chicken embryo and/or postnatal mouse lenses. pSmad1 and pSmad2 also localized to Rab7-positive late endosomes. Smad7 was found associated with endosomes, but not caveolae. Bmpr1a conditional knock-out lenses showed decreased nuclear and endosomal localization of pSmad1. Many of the effectors in this pathway were distributed differently in vivo from their reported distribution in cultured cells. Based on the findings reported here and data from other signaling systems, we suggest that the localization of activated intracellular mediators of the transforming growth factor-beta superfamily to endosomes is important for the regulation of growth factor signaling.

The influence of local- and landscape-level factors on wetland breeding birds in the Prairie Pothole Region of North and South Dakota

USGS Publications Warehouse

Igl, Lawrence D.; Shaffer, Jill A.; Johnson, Douglas H.; Buhl, Deborah A.

2017-08-17

restored Federal wetlands. After adjusting for wetland size and the date and location of the surveys, our results demonstrated that incorporating wetland- and landscape-level factors in models can improve our ability to predict abundances of wetland birds in this region. The top model for eight of the nine focal species included wetland- and landscape-level factors, whereas the best model for Blue-winged Teal included only wetland-level attributes. Although local factors (for example, percent open water or emergent vegetation) in individual wetlands are important factors for some wetland breeding birds, it is important that natural resource managers consider landscape-level factors beyond the local factors in their conservation plans for wetland birds.

Predictive factors of user acceptance on the primary educational mathematics aids product

NASA Astrophysics Data System (ADS)

Hidayah, I.; Margunani; Dwijanto

2018-03-01

Mathematics learning in primary schools requires instructional media. According to Piaget's theory, students are still in the concrete operational stage. For this reason, the development of the primary level mathematics aids is needed to support the development of successful mathematics learning. The stages of this research are the stages of commercialization with preparatory, marketing, and measurement analysis procedures. Promotion as part of marketing is done by doing a demonstration to the teacher. Measurements were performed to explore the predictive factors of user feasibility in adopting the product. Measurements were conducted using the concept of Technology Acceptance Model (TAM). Measurement variables include external variables, perceived usefulness, perceived ease of use, attitude, intention to use, and actual use. The result of this research shows that the contribution of predictive factors of mathematics teachers on the teaching aids product as follows: the external variable and perceived ease of use at 74%, perceived usefulness at 72%, intention to use (behavioral) at 58%, attitude at 52%, and the consequence factor (actual use) at 42%.

Predictive factors of thyroid cancer in patients with Graves' disease.

PubMed

Ren, Meng; Wu, Mu Chao; Shang, Chang Zhen; Wang, Xiao Yi; Zhang, Jing Lu; Cheng, Hua; Xu, Ming Tong; Yan, Li

2014-01-01

The best preoperative examination in Graves' disease with thyroid cancer still remains uncertain. The objectives of the present study were to investigate the prevalence of thyroid cancer in Graves' disease patients, and to identify the predictive factors and ultrasonographic features of thyroid cancer that may aid the preoperative diagnosis in Graves' disease. This retrospective study included 423 patients with Graves' disease who underwent surgical treatment from 2002 to 2012 at our institution. The clinical features and ultrasonographic findings of thyroid nodules were recorded. The diagnosis of thyroid cancer was determined according to the pathological results. Thyroid cancer was discovered in 58 of the 423 (13.7 %) surgically treated Graves' disease patients; 46 of those 58 patients had thyroid nodules, and the other 12 patients were diagnosed with incidentally discovered thyroid carcinomas without thyroid nodules. Among the 58 patients with thyroid cancer, papillary microcarcinomas were discovered in 50 patients, and multifocality and lymph node involvement were detected in the other 8 patients. Multivariate regression analysis showed younger age was the only significant factor predictive of metastatic thyroid cancer. Ultrasonographic findings of calcification and intranodular blood flow in thyroid nodules indicate that they are more likely to harbor thyroid cancers. Because the influencing factor of metastatic thyroid cancers in Graves' disease is young age, every suspicious nodule in Graves' disease patients should be evaluated and treated carefully, especially in younger patients because of the potential for metastasis.

Preoperative Erythrocyte Sedimentation Rate Independently Predicts Overall Survival in Localized Renal Cell Carcinoma following Radical Nephrectomy

PubMed Central

Cross, Brian W.; Johnson, Timothy V.; DeRosa, Austin B.; Ogan, Kenneth; Pattaras, John G.; Nieh, Peter T.; Kucuk, Omer; Harris, Wayne B.; Master, Viraj A.

2012-01-01

Objectives. To determine the relationship between preoperative erythrocyte sedimentation rate (ESR) and overall survival in localized renal cell carcinoma (RCC) following nephrectomy. Methods. 167 patients undergoing nephrectomy for localized RCC had ESR levels measured preoperatively. Receiver Operating Characteristics curves were used to determine Area Under the Curve and relative sensitivity and specificity of preoperative ESR in predicting overall survival. Cut-offs for low (0.0–20.0 mm/hr), intermediate (20.1–50.0 mm/hr), and high risk (>50.0 mm/hr) groups were created. Kaplan-Meier analysis was conducted to assess the univariate impact of these ESR-based groups on overall survival. Univariate and multivariate Cox regression analysis was conducted to assess the potential of these groups to predict overall survival, adjusting for other patient and tumor characteristics. Results. Overall, 55.2% were low risk, while 27.0% and 17.8% were intermediate and high risk, respectively. Median (95% CI) survival was 44.1 (42.6–45.5) months, 35.5 (32.3–38.8) months, and 32.1 (25.5–38.6) months, respectively. After controlling for other patient and tumor characteristics, intermediate and high risk groups experienced a 4.5-fold (HR: 4.509, 95% CI: 0.735–27.649) and 18.5-fold (HR: 18.531, 95% CI: 2.117–162.228) increased risk of overall mortality, respectively. Conclusion. Preoperative ESR values represent a robust predictor of overall survival following nephrectomy in localized RCC. PMID:22900160

[Predictive factors of contamination in a blood culture with bacterial growth in an Emergency Department].

PubMed

Hernández-Bou, S; Trenchs Sainz de la Maza, V; Esquivel Ojeda, J N; Gené Giralt, A; Luaces Cubells, C

2015-06-01

The aim of this study is to identify predictive factors of bacterial contamination in positive blood cultures (BC) collected in an emergency department. A prospective, observational and analytical study was conducted on febrile children aged on to 36 months, who had no risk factors of bacterial infection, and had a BC collected in the Emergency Department between November 2011 and October 2013 in which bacterial growth was detected. The potential BC contamination predicting factors analysed were: maximum temperature, time to positivity, initial Gram stain result, white blood cell count, absolute neutrophil count, band count, and C-reactive protein (CRP). Bacteria grew in 169 BC. Thirty (17.8%) were finally considered true positives and 139 (82.2%) false positives. All potential BC contamination predicting factors analysed, except maximum temperature, showed significant differences between true positives and false positives. CRP value, time to positivity, and initial Gram stain result are the best predictors of false positives in BC. The positive predictive values of a CRP value≤30mg/L, BC time to positivity≥16h, and initial Gram stain suggestive of a contaminant in predicting a FP, are 95.1, 96.9 and 97.5%, respectively. When all 3 conditions are applied, their positive predictive value is 100%. Four (8.3%) patients with a false positive BC and discharged to home were revaluated in the Emergency Department. The majority of BC obtained in the Emergency Department that showed positive were finally considered false positives. Initial Gram stain, time to positivity, and CRP results are valuable diagnostic tests in distinguishing between true positives and false positives in BC. The early detection of false positives will allow minimising their negative consequences. Copyright © 2014 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

Factors That Predict Persistence for Non-Immigrant, International Students at a Private, Four-Year University in Georgia

ERIC Educational Resources Information Center

Adams, Shawn M.