Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

PubMed

Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

2017-01-01

Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets.

PubMed

Nan, Shengyu; Sun, Lei; Chen, Badong; Lin, Zhiping; Toh, Kar-Ann

2017-01-01

Based on the knowledge that input data distribution is important for learning, a data density-dependent quantization scheme (DQS) is proposed for sparse input data representation. The usefulness of the representation scheme is demonstrated by using it as a data preprocessing unit attached to the well-known least squares support vector machine (LS-SVM) for application on big data sets. Essentially, the proposed DQS adopts a single shrinkage threshold to obtain a simple quantization scheme, which adapts its outputs to input data density. With this quantization scheme, a large data set is quantized to a small subset where considerable sample size reduction is generally obtained. In particular, the sample size reduction can save significant computational cost when using the quantized subset for feature approximation via the Nyström method. Based on the quantized subset, the approximated features are incorporated into LS-SVM to develop a data density-dependent quantized LS-SVM (DQLS-SVM), where an analytic solution is obtained in the primal solution space. The developed DQLS-SVM is evaluated on synthetic and benchmark data with particular emphasis on large data sets. Extensive experimental results show that the learning machine incorporating DQS attains not only high computational efficiency but also good generalization performance.

Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

NASA Astrophysics Data System (ADS)

Erener, A.

2013-04-01

Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal. Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

PubMed

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

99mTc-EDDA/HYNIC-TOC in the diagnosis of differentiated thyroid carcinoma refractory to radioiodine treatment.

PubMed

Czepczyński, Rafał; Gryczyńska, Maria; Ruchała, Marek

2016-01-01

In majority of cases of differentiated thyroid carcinoma (DTC), the ablative radioiodine treatment shows high efficacy. In a small number of patients, mechanism of selective iodine uptake by the DTC cells is insufficient and alternative methods of diagnosis and treatment are needed. As demonstrated in vitro, DTC cells show expression of somatostatin recep-tors. Radiolabeled somatostatin analogs are widely used in the diagnosis of neuroendocrine tumors. The aim of the study was to evaluate the utility of peptide receptor scintigraphy with the use of 99mTc-EDDA/HYNIC-TOC in the diagnosis of DTC in patients with elevated thyroglobulin concentrations (Tg), negative WBS and no effect of the consecutive radioiodine therapies. Whole body scintigraphy as well as SPECT of neck and chest were performed 3 and 24 h after i.v. administration of 740 MBq 99mTc-EDDA/HYNIC-TOC. The obtained images were compared with other radionuclide and ra-diological imaging methods. Forty-three patients with DTC after surgery and ablative radioiodine treatment with negative WBS and elevated Tg were qualified. Patients' age: 18-83 years (mean 58.0). SRS showed foci of tracer accumulation in 29 cases (67.4%). Sensitivity was 69.0% specificity 78.6%. SRS correctly identified local recurrence in 8 pts., metastatic lymph nodes in 19 pts., lung metastases in 12 pts. and bone metastases in 5 pts. SRS showed high sensitivity in the detection of metastatic lymph nodes (100%) and bone metastases (83.3%) and lung metastases (63.2%). Positive SRS was found in pts. with higher Tg concentrations (130 ± 144 vs. 30 ± 54 ng/ml). Scintigraphy with the use of the studied technetium-99m-labeled somatostatin analog is useful in the evaluation of patients with advanced DTC. It shows relatively good sensitivity and specificity but not high enough to be recommended as a routine imaging method. The role of somatostatin receptor scintigraphy in DTC is complementary to other imaging modalities.

Tuning support vector machines for minimax and Neyman-Pearson classification.

PubMed

Davenport, Mark A; Baraniuk, Richard G; Scott, Clayton D

2010-10-01

This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can lead to poor classifier performance. To address this issue, we first prove that the usual cost-sensitive SVM, here called the 2C-SVM, is equivalent to another formulation called the 2nu-SVM. We then exploit a characterization of the 2nu-SVM parameter space to develop a simple yet powerful approach to error estimation based on smoothing. In an extensive experimental study, we demonstrate that smoothing significantly improves the accuracy of cross-validation error estimates, leading to dramatic performance gains. Furthermore, we propose coordinate descent strategies that offer significant gains in computational efficiency, with little to no loss in performance.

Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

PubMed

Yoo, Tae Keun; Kim, Sung Kean; Kim, Deok Won; Choi, Joon Yul; Lee, Wan Hyung; Oh, Ein; Park, Eun-Cheol

2013-11-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

Health-Care Referrals from Direct-to-Consumer Genetic Testing

PubMed Central

Giovanni, Monica A.; Fickie, Matthew R.; Lehmann, Lisa S.; Green, Robert C.; Meckley, Lisa M.; Veenstra, David

2010-01-01

Background: Direct-to-consumer genetic testing (DTC-GT) provides personalized genetic risk information directly to consumers. Little is known about how and why consumers then communicate the results of this testing to health-care professionals. Aim: To query specialists in clinical genetics about their experience with individuals who consulted them after DTC-GT. Methods: Invitations to participate in a questionnaire were sent to three different groups of genetic professionals, totaling 4047 invitations, asking questions about individuals who consulted them after DTC-GT. For each case reported, respondents were asked to describe how the case was referred to them, the patient's rationale for DTC-GT, and the type of DTC-GT performed. Respondents were also queried about the consequences of the consultations in terms of additional testing ordered. The costs associated with each consultation were estimated. A clinical case series was compiled based upon clinician responses. Results: The invitation resulted in 133 responses describing 22 cases of clinical interactions following DTC-GT. Most consultations (59.1%) were self-referred to genetics professionals, but 31.8% were physician referred. Among respondents, 52.3% deemed the DTC-GT to be “clinically useful.” BRCA1/2 testing was considered clinically useful in 85.7% of cases; 35.7% of other tests were considered clinically useful. Subsequent referrals from genetics professionals to specialists and/or additional diagnostic testing were common, generating individual downstream costs estimated to range from $40 to $20,600. Conclusions: This clinical case series suggests that approximately half of clinical geneticists who saw patients after DTC-GT judged that testing was clinically useful, especially the BRCA1/2 testing. Further studies are needed in larger and more diverse populations to better understand the interactions between DTC-GT and the health-care system. PMID:20979566

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Self-reported responsiveness to direct-to-consumer drug advertising and medication use: results of a national survey.

PubMed

Dieringer, Nicholas J; Kukkamma, Lisa; Somes, Grant W; Shorr, Ronald I

2011-09-23

ABSTRACT: BACKGROUND: Direct-to-consumer (DTC) marketing of pharmaceuticals is controversial, yet effective. Little is known relating patterns of medication use to patient responsiveness to DTC. METHODS: We conducted a secondary analysis of data collected in national telephone survey on knowledge of and attitudes toward DTC advertisements. The survey of 1081 U.S. adults (response rate = 65%) was conducted by the Food and Drug Administration (FDA). Responsiveness to DTC was defined as an affirmative response to the item: "Has an advertisement for a prescription drug ever caused you to ask a doctor about a medical condition or illness of your own that you had not talked to a doctor about before?" Patients reported number of prescription and over-the-counter (OTC) medicines taken as well as demographic and personal health information. RESULTS: Of 771 respondents who met study criteria, 195 (25%) were responsive to DTC. Only 7% respondents taking no prescription were responsive, whereas 45% of respondents taking 5 or more prescription medications were responsive. This trend remained significant (p trend .0009) even when controlling for age, gender, race, educational attainment, income, self-reported health status, and whether respondents "liked" DTC advertising. There was no relationship between the number of OTC medications taken and the propensity to discuss health-related problems in response to DTC advertisements (p = .4). CONCLUSION: There is a strong cross-sectional relationship between the number of prescription, but not OTC, drugs used and responsiveness to DTC advertising. Although this relationship could be explained by physician compliance with patient requests for medications, it is also plausible that DTC advertisements have a particular appeal to patients prone to taking multiple medications. Outpatients motivated to discuss medical conditions based on their exposure to DTC advertising may require a careful medication history to evaluate for therapeutic duplication or overmedication.

Diagnosis of periodontal diseases using different classification algorithms: a preliminary study.

PubMed

Ozden, F O; Özgönenel, O; Özden, B; Aydogdu, A

2015-01-01

The purpose of the proposed study was to develop an identification unit for classifying periodontal diseases using support vector machine (SVM), decision tree (DT), and artificial neural networks (ANNs). A total of 150 patients was divided into two groups such as training (100) and testing (50). The codes created for risk factors, periodontal data, and radiographically bone loss were formed as a matrix structure and regarded as inputs for the classification unit. A total of six periodontal conditions was the outputs of the classification unit. The accuracy of the suggested methods was compared according to their resolution and working time. DT and SVM were best to classify the periodontal diseases with a high accuracy according to the clinical research based on 150 patients. The performances of SVM and DT were found 98% with total computational time of 19.91 and 7.00 s, respectively. ANN had the worst correlation between input and output variable, and its performance was calculated as 46%. SVM and DT appeared to be sufficiently complex to reflect all the factors associated with the periodontal status, simple enough to be understandable and practical as a decision-making aid for prediction of periodontal disease.

Weight Changes in Patients with Differentiated Thyroid Carcinoma during Postoperative Long-Term Follow-up under Thyroid Stimulating Hormone Suppression

PubMed Central

Sohn, Seo Young; Joung, Ji Young; Cho, Yoon Young; Park, Sun Mi; Jin, Sang Man; Chung, Jae Hoon

2015-01-01

Background There are limited data about whether patients who receive initial treatment for differentiated thyroid cancer (DTC) gain or lose weight during long-term follow-up under thyroid stimulating hormone (TSH) suppression. This study was aimed to evaluate whether DTC patients under TSH suppression experience long-term weight gain after initial treatment. We also examined the impact of the radioactive iodine ablation therapy (RAIT) preparation method on changes of weight, comparing thyroid hormone withdrawal (THW) and recombinant human TSH (rhTSH). Methods We retrospectively reviewed 700 DTC patients who underwent a total thyroidectomy followed by either RAIT and levothyroxine (T4) replacement or T4 replacement alone. The control group included 350 age-matched patients with benign thyroid nodules followed during same period. Anthropometric data were measured at baseline, 1 to 2 years, and 3 to 4 years after thyroidectomy. Comparisons were made between weight and body mass index (BMI) at baseline and follow-up. Results Significant gains in weight and BMI were observed 3 to 4 years after initial treatment for female DTC but not in male patients. These gains among female DTC patients were also significant compared to age-matched control. Women in the THW group gained a significant amount of weight and BMI compared to baseline, while there was no increase in weight or BMI in the rhTSH group. There were no changes in weight and BMI in men according to RAIT preparation methods. Conclusion Female DTC patients showed significant gains in weight and BMI during long-term follow-up after initial treatment. These changes were seen only in patients who underwent THW for RAIT. PMID:26248858

Episode-Specific Drinking-to-Cope Motivation and Next-Day Stress-Reactivity

PubMed Central

Armeli, Stephen; O’Hara, Ross E.; Covault, Jon; Scott, Denise M.; Tennen, Howard

2016-01-01

Background Research consistently shows drinking-to-cope (DTC) motivation is uniquely associated with drinking-related problems. We furthered this line of research by examining whether DTC motivation is predictive of processes indicative of poor emotion regulation. Specifically, we tested whether nighttime levels of episode-specific DTC motivation, controlling for drinking level, were associated with intensified affective reactions to stress the following day (i.e., stress-reactivity). Design and Methods We used a micro-longitudinal design to test this hypothesis in two college student samples from demographically distinct institutions: a large, rural state university (N = 1421; 54% female) and an urban historically Black college/university (N = 452; 59% female). Results In both samples the within-person association between daily stress and negative affect on days following drinking episodes was stronger in the positive direction when previous night’s drinking was characterized by relatively higher levels of DTC motivation. We also found evidence among students at the state university that average levels of DTC motivation moderated the daily stress-negative affect association. Conclusions Findings are consistent with the notion that DTC motivation confers a unique vulnerability that affects processes associated with emotion regulation. PMID:26691066

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

PubMed

Li, Yuanpeng; Li, Fucui; Yang, Xinhao; Guo, Liu; Huang, Furong; Chen, Zhenqiang; Chen, Xingdan; Zheng, Shifu

2018-08-05

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm -1 ) and GA's characteristic region (1800-800 cm -1 ) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECV T ) = 0.523 g/L, calibration coefficient (R C ) = 0.937, Root Mean Square Error of Prediction (RMSEP T ) = 0.787 g/L, and prediction coefficient (R P ) = 0.938. The SVM modeling results are as follows: RMSECV T  = 0.0048 g/L, R C  = 0.998, RMSEP T  = 0.442 g/L, and R p  = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of Flavonoid in Medicinal Plant Extract Using Infrared Spectroscopy and Chemometrics

PubMed Central

Retnaningtyas, Yuni; Nuri; Lukman, Hilmia

2016-01-01

Infrared (IR) spectroscopy combined with chemometrics has been developed for simple analysis of flavonoid in the medicinal plant extract. Flavonoid was extracted from medicinal plant leaves by ultrasonication and maceration. IR spectra of selected medicinal plant extract were correlated with flavonoid content using chemometrics. The chemometric method used for calibration analysis was Partial Last Square (PLS) and the methods used for classification analysis were Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogies (SIMCA), and Support Vector Machines (SVM). In this study, the calibration of NIR model that showed best calibration with R 2 and RMSEC value was 0.9916499 and 2.1521897, respectively, while the accuracy of all classification models (LDA, SIMCA, and SVM) was 100%. R 2 and RMSEC of calibration of FTIR model were 0.8653689 and 8.8958149, respectively, while the accuracy of LDA, SIMCA, and SVM was 86.0%, 91.2%, and 77.3%, respectively. PLS and LDA of NIR models were further used to predict unknown flavonoid content in commercial samples. Using these models, the significance of flavonoid content that has been measured by NIR and UV-Vis spectrophotometry was evaluated with paired samples t-test. The flavonoid content that has been measured with both methods gave no significant difference. PMID:27529051

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.

PubMed

Becker, Natalia; Toedt, Grischa; Lichter, Peter; Benner, Axel

2011-05-09

Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'.We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data

PubMed Central

2011-01-01

Background Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net. We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone. Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error. Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. Conclusions The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters. The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'. We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets. PMID:21554689

[Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

PubMed

Li, Wu; Hu, Bing; Wang, Ming-wei

2014-12-01

In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species identification of borneol in Chinese medicine.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

In the process of drinking to cope among college students: An examination of specific vs. global coping motives for depression and anxiety symptoms.

PubMed

Bravo, Adrian J; Pearson, Matthew R

2017-10-01

The present study sought to address an issue in the drinking to cope (DTC) motives literature, namely the inconsistent application of treating DTC motives as a single construct and splitting it into DTC-depression and DTC-anxiety motives. Specifically, we aimed to determine if the effects of anxiety and depression on alcohol-related problems are best explained via their associations with DTC with specific affects or via their associations with a more global measure of DTC by testing four distinct models: the effects of anxiety/depression on alcohol-related problems mediated by DTC-anxiety only (Model 1), these effects mediated by DTC-depression only (Model 2), these effects mediated by a combined, global DTC factor (Model 3), and these effects mediated by both DTC-anxiety and DTC-depression (Model 4). Using path analysis/structural equation modeling across two independent samples, we found that there was a significant total indirect effect of both anxiety and depressive symptoms on alcohol-related problems in every model. However, there was a slightly larger indirect effect in all models using the global DTC motives factor compared to even the model that included the two distinct DTC motives. Our results provide some preliminary evidence that at least at the between-subjects level, a global DTC motives factor may have more predictive validity than separate DTC motives. Additional research is needed to examine how to best operationalize DTC motives at different levels of analysis (e.g., within-subjects vs. between subjects) and in different populations (e.g., college students vs. individuals with alcohol use disorder). Copyright © 2017. Published by Elsevier Ltd.

F18-FDG-PET for recurrent differentiated thyroid cancer: a systematic meta-analysis

PubMed Central

Haslerud, Torjan; Brauckhoff, Katrin; Reisæter, Lars; Küfner Lein, Regina; Heinecke, Achim; Varhaug, Jan Erik

2015-01-01

Background Positron emission tomography (PET) with fluor-18-deoxy-glucose (FDG) is widely used for diagnosing recurrent or metastatic disease in patients with differentiated thyroid cancer (DTC). Purpose To assess the diagnostic accuracy of FDG-PET for DTC in patients after ablative therapy. Material and Methods A systematic search was conducted in Medline/PubMed, EMBASE, Cochrane Library, Web of Science, and Open Grey looking for all English-language original articles on the performance of FDG-PET in series of at least 20 patients with DTC having undergone ablative therapy including total thyroidectomy. Diagnostic performance measures were pooled using Reitsma’s bivariate model. Results Thirty-four publications between 1996 and 2014 met the inclusion criteria. Pooled sensitivity and specificity were 79.4% (95% confidence interval [CI], 73.9–84.1) and 79.4% (95% CI, 71.2–85.4), respectively, with an area under the curve of 0.858. Conclusion F18-FDG-PET is a useful method for detecting recurrent DTC in patients having undergone ablative therapy. PMID:26163534

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

PubMed

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

PubMed

Li, Xun-lan; Yi, Shi-lai; He, Shao-lan; Lü, Qiang; Xie, Rang-jin; Zheng, Yong-qiang; Deng, Lie

2015-09-01

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.

Health literacy knowledge among direct-to-consumer pharmaceutical advertising professionals.

PubMed

Mackert, Michael

2011-09-01

While direct-to-consumer (DTC) prescription drug advertising has been the subject of ongoing debate, to this point the perspective of the advertising professionals engaged in creating these ads has been absent from the discussion. This study, consisting of in-depth interviews with advertising professionals (N = 22), was an initial investigation focused on these individuals. The primary purpose of this study was to explore advertising professionals' understanding of health literacy-consumers' ability to obtain, process, and act on health information; with that context in place, participants' views on the role of DTC advertising, industry regulations, and the future of the industry were also investigated. While some participants knew nothing about health literacy or had a relatively simple conceptualization (e.g., grade level of written materials), others exhibited more nuanced understanding of health literacy (e.g., the need to pair relevant images with text to enhance understanding). Participants spoke of the potential public health benefit of DTC advertising in educating consumers about health issues, but were realistic that such efforts on the part of pharmaceutical companies were driven primarily by business concerns-educational messages need to be tied directly to an advertised medication and its benefits. These professionals spoke of industry regulations as presenting additional barriers to effective communication and suggested that industry trends toward more niche products will necessitate more patient education about less well-known health issues. Directions for future research are considered, as more investigation of this understudied group is necessary to enrich the DTC prescription drug advertising debate.

The general public's understanding and perception of direct-to-consumer genetic test results.

PubMed

Leighton, J W; Valverde, K; Bernhardt, B A

2012-01-01

Direct-to-consumer (DTC) genetic testing allows consumers to discover their risk for common complex disorders. The extent to which consumers understand typical results provided by DTC genetic testing is currently unknown. Misunderstanding of the results could lead to negative consequences including unnecessary concern, false reassurance or unwarranted changes in screening behaviors. We conducted a study to investigate consumers' perceptions and understanding of DTC test results. An online survey was posted on Facebook that included questions relating to 4 sample test results for risk of developing colorectal cancer, heart disease and skin cancer. Genetic counselors were used as a comparison group. 145 individuals from the general public and 171 genetic counselors completed the survey. A significant difference was found between the way the general public and genetic counselors interpreted the meaning of the DTC results. The general public respondents also believed that results in all 4 scenarios would be significantly more helpful than the genetic counselors did. Although the majority of general public respondents rated the results as easy to understand, they often misinterpreted them. These findings imply that the general public has the potential to misinterpret DTC results without appropriate assistance. Further research is needed to explore optimal methods of providing DTC test results and ways to minimize the risk of negative consequences for consumers. Copyright © 2011 S. Karger AG, Basel.

A survey of direct-to-consumer teledermatology services available to US patients: Explosive growth, opportunities and controversy.

PubMed

Fogel, Alexander L; Sarin, Kavita Y

2017-01-01

Introduction Direct-to-consumer (DTC) teledermatology is radically changing the way patients obtain dermatological care. Now, with a few clicks, patients can obtain dermatological consultations and prescription medications without a prior physician-patient relationship. To analyse all DTC teledermatology services available to US patients. Methods We performed Internet searches to identify DTC teledermatology services available through Internet webpages or through smartphone applications. For each service, the scope of care provided, cost, wait times, prescription policies and other relevant information were recorded. Results Twenty-two DTC teledermatology services are available to US patients in 45 states. Six (27%) services offer care from international physicians. Sixteen (73%) services allow patients to seek care for any reason, while six (27%) limit care to acne or anti-aging. The median reported response time for DTC teledermatology services is 48 hours from the time of patient request. The median consultation fee for companies providing care from US board-certified physicians is US$59. Across all services, consultation fees range from US$1.59 to US$250. Conclusions DTC teledermatology services are readily available to patients in most states. These services may reduce the cost of patient visits, expand access to care and increase patient convenience. However, the presence of services staffed by physicians who are not US board-certified, as well as the use of incautious language regarding prescription medications, is concerning.

Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition

PubMed Central

2012-01-01

Background Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. Results This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of α-helix structure and thermophilic proteins to be soluble. Conclusions The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. Availability The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/. PMID:23282103

Drinking-to-cope motivation and negative mood-drinking contingencies in a daily diary study of college students.

PubMed

O'Hara, Ross E; Armeli, Stephen; Tennen, Howard

2014-07-01

This study examined whether global drinking-to-cope (DTC) motivation moderates negative mood-drinking contingencies and negative mood-motivation contingencies at the daily level of analysis. Data came from a daily diary study of college student drinking (N = 1,636; 53% female; Mage = 19.2 years). Fixed-interval models tested whether global DTC motivation moderated relations between daily negative mood and that evening's drinking and episodic DTC. Time-to-drink models examined whether global DTC motivation moderated the effects of weekly negative mood on the immediacy of drinking and DTC in the weekly cycle. More evening drinking occurred on days characterized by relatively higher anxiety or anger, and students were more likely to report DTC on days when they experienced greater sadness. However, only the daily Anxiety × Global DTC Motivation interaction for number of drinks consumed was consistent with hypotheses. Moreover, students reported drinking, heavy drinking, and DTC earlier in weeks characterized by relatively higher anxiety or anger, but no hypothesized interactions with global DTC motivation were found. RESULTS indicate that negative mood is associated with increased levels of drinking and drinking for coping reasons among college students but that the strength of these relations does not differ by global levels of DTC motivation. These findings raise the possibility that global DTC measures are insufficient for examining within-person DTC processes. Further implications of these results are discussed, including future directions that may determine the circumstances under which, and for whom, DTC occurs.

Ship Detection in Optical Satellite Image Based on RX Method and PCAnet

NASA Astrophysics Data System (ADS)

Shao, Xiu; Li, Huali; Lin, Hui; Kang, Xudong; Lu, Ting

2017-12-01

In this paper, we present a novel method for ship detection in optical satellite image based on the ReedXiaoli (RX) method and the principal component analysis network (PCAnet). The proposed method consists of the following three steps. First, the spatially adjacent pixels in optical image are arranged into a vector, transforming the optical image into a 3D cube image. By taking this process, the contextual information of the spatially adjacent pixels can be integrated to magnify the discrimination between ship and background. Second, the RX anomaly detection method is adopted to preliminarily extract ship candidates from the produced 3D cube image. Finally, real ships are further confirmed among ship candidates by applying the PCAnet and the support vector machine (SVM). Specifically, the PCAnet is a simple deep learning network which is exploited to perform feature extraction, and the SVM is applied to achieve feature pooling and decision making. Experimental results demonstrate that our approach is effective in discriminating between ships and false alarms, and has a good ship detection performance.

Rigidity, Criticality and Prethermalization of Discrete Time Crystals

NASA Astrophysics Data System (ADS)

Yao, Norman

2017-04-01

Despite being forbidden in equilibrium, spontaneous breaking of time translation symmetry can occur in periodically driven, Floquet systems with discrete time-translation symmetry. The period of the resulting discrete time crystal (DTC) is quantized to an integer multiple of the drive period, arising from a combination of collective synchronization and many body localization. In this talk, I will describe a simple model for a one dimensional discrete time crystal which explicitly reveals the rigidity of the emergent oscillations as the drive is varied. I will analyze the properties of the dynamical phase transition where the time crystal melts into a trivial Floquet insulator. Effects of long-range interactions and pre-thermalization will be considered in the context of recent DTC realizations in trapped ions and solid-state spins.

Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

PubMed

Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

2015-10-01

Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

FOXE1 Association with Differentiated Thyroid Cancer and Its Progression

PubMed Central

Penna-Martinez, Marissa; Epp, Friederike; Kahles, Heinrich; Ramos-Lopez, Elizabeth; Hinsch, Nora; Hansmann, Martin-Leo; Selkinski, Ivan; Grünwald, Frank; Holzer, Katharina; Bechstein, Wolf O.; Zeuzem, Stefan; Vorländer, Christian

2014-01-01

Background: Single nucleotide polymorphisms (SNPs) near thyroid transcription factor genes (FOXE1 rs965513/NKX2-1 rs944289) have been shown to be associated with differentiated thyroid cancer (DTC) in Caucasoid populations. We investigated the role of those SNPs in German patients with DTC and also extended our analysis to tumor stages and lymphocytic infiltration of the tumors (ITL). Methods: Patients with DTC (n=243; papillary, PTC; follicular, FTC) and healthy controls (HC; n=270) were analyzed for the rs965513 and rs944289 SNPs. Results: The case-control analysis for rs965513 SNP showed that the genotypes “AA,” “AG,” and minor allele “A” were more frequent in patients with DTC than in HC (pronounced in PTC pgenotype=0.000084, pallele=0.006 than FTC pgenotype=0.29 and pallele=0.06). Furthermore, subgroup analysis of the DTC patients stratified for primary tumor stage (T1–T2, T3–T4), the absence or presence of regional lymph node metastases (N0, N1), for distant metastases (M0, M1), as well as for ITL, showed an association of rs965513 with stages T1–T2, T1–T3, N1, and absence of ITL. The NKX2-1 SNP rs944289, however, was not associated with DTC. Conclusion: Our results confirm that the FOXE1 rs965513 SNP confers an increased risk for DTC in the German population, particularly allele “A” and the genotypes “AA” and “AG” for PTC. This increased risk was also observed in advanced tumor stages and absence of ITL, which may reflect the course of a more aggressive disease. The NKX2-1 rs944289 SNP, however, appears to play a secondary role in the development of DTC in the German population. PMID:24325646

Detection of β-Thalassemia Carriers by Red Cell Parameters Obtained from Automatic Counters using Mathematical Formulas

PubMed Central

Roth, Idit Lachover; Lachover, Boaz; Koren, Guy; Levin, Carina; Zalman, Luci; Koren, Ariel

2018-01-01

Background β-thalassemia major is a severe disease with high morbidity. The world prevalence of carriers is around 1.5–7%. The present study aimed to find a reliable formula for detecting β-thalassemia carriers using an extensive database of more than 22,000 samples obtained from a homogeneous population of childbearing age women with 3161 (13.6%) of β-thalassemia carriers and to check previously published formulas. Methods We applied a mathematical method based on the support vector machine (SVM) algorithm in the search for a reliable formula that can differentiate between thalassemia carriers and non-carriers, including normal counts or counts suspected to belong to iron-deficient women. Results Shine’s formula and our SVM formula showed >98% sensitivity and >99.77% negative predictive value (NPV). All other published formulas gave inferior results. Conclusions We found a reliable formula that can be incorporated into any automatic blood counter to alert health providers to the possibility of a woman being a β-thalassemia carrier. A further simple hemoglobin characterization by HPLC analysis should be performed to confirm the diagnosis, and subsequent family studies should be carried out. Our SVM formula is currently limited to women of fertility age until further analysis in other groups can be performed. PMID:29326805

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

Large-scale linear rankSVM.

PubMed

Lee, Ching-Pei; Lin, Chih-Jen

2014-04-01

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

Application of GA-SVM method with parameter optimization for landslide development prediction

NASA Astrophysics Data System (ADS)

Li, X. Z.; Kong, J. M.

2013-10-01

Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

SVM and SVM Ensembles in Breast Cancer Prediction.

PubMed

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

SVM and SVM Ensembles in Breast Cancer Prediction

PubMed Central

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. PMID:28060807

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

PubMed

Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

The effects of involvement and ad type on attitudes toward direct-to-consumer advertising of prescription drugs.

PubMed

Limbu, Yam; Torres, Ivonne M

2009-01-01

This article examines consumers' attitudes toward Direct-to-Consumer (DTC) advertising of prescription drugs that are influenced by the use different types of DTC ads and product involvement. Our findings suggest that product involvement and the type of DTC ad are significant predictors of consumers' attitudinal responses toward DTC advertising. High involvement consumers have more favorable attitudes toward the drug's price, DTC ad and brand name, and a higher intention to ask a doctor about the advertised drug than low involvement consumers. In contrast to Informational and Reminder DTC ads, Persuasive ads have more favorable effects on consumers' reactions to DTC prescription drug advertising.

Association of Arg194Trp, Arg280His and Arg399Gln Polymorphisms in X-ray Repair Cross-Complementing Group 1 Gene and Risk of Differentiated Thyroid Carcinoma in Iran

PubMed Central

Fard-Esfahani, Pezhman; Fard-Esfahani, Armaghan; Fayaz, Shima; Ghanbarzadeh, Bahareh; Saidi, Parinaz; Mohabati, Reyhaneh; Bidoki, Seyed Kazem; Majdi, Mina

2011-01-01

Background: X-ray repair cross-complementing group 1 (XRCC1) gene is a DNA repair gene and its non-synonymous single nucleotide polymorphisms (SNP) may influence DNA repair capacity which has been considered as a modifying risk factor for cancer development. Methods: A case-control study was conducted to investigate impact of three frequently studied polymorphisms (Arg194Trp, Arg280His and Arg399Gln) on developing differentiated thyroid carcinoma (DTC). Results: Increased risks for DTC were shown in homozygous (odds ratio [OR]: 3.66, 95% confidence interval [CI]: 0.38-35.60) and in dominant trait (OR: 1.22, 95% CI: 1.64-2.32) of Arg194Trp genotype. Also, for Arg280His genotype, an increased risk for DTC was shown in dominant trait (OR: 1.42, 95% confidence interval [CI]: 0.76-2.68), while a mildly reduction of risk for DTC (OR: 0.77, 95% [CI]: 0.50-1.17) was estimated in dominant Gln genotype of Arg399Gln. Considering combinatory effects of Arg194Trp and Arg280His genotypes on DTC, the calculated OR and 95% CI for being heterozygous for one of Arg194Trp or Arg280His genotypes were 1.57 and 0.90-2.74, respectively. Conclusion: Genotyping of codons 194, 280 and 399 in XRCC1 gene may use in risk assessment of DTC. PMID:21987112

American Thyroid Association consensus review and statement regarding the anatomy, terminology, and rationale for lateral neck dissection in differentiated thyroid cancer.

PubMed

Stack, Brendan C; Ferris, Robert L; Goldenberg, David; Haymart, Megan; Shaha, Ashok; Sheth, Sheila; Sosa, Julie Ann; Tufano, Ralph P

2012-05-01

Cervical lymph node metastases from differentiated thyroid cancer (DTC) are common. Thirty to eighty percent of patients with papillary thyroid cancer harbor lymph node metastases, with the central neck being the most common compartment involved. The goals of this study were to: (1) identify appropriate methods for determining metastatic DTC in the lateral neck and (2) address the extent of lymph node dissection for the lateral neck necessary to control nodal disease balanced against known risks of surgery. A literature review followed by formulation of a consensus statement was performed. Four proposals regarding management of the lateral neck are made for consideration by organizations developing management guidelines for patients with thyroid nodules and DTC including the next iteration of management guidelines developed by the American Thyroid Association (ATA). Metastases to lateral neck nodes must be considered in the evaluation of the newly diagnosed thyroid cancer patient and for surveillance of the previously treated DTC patient. Lateral neck lymph nodes are a significant consideration in the surgical management of patients with DTC. When current guidelines formulated by the ATA and by other international medical societies are followed, initial evaluation of the DTC patient with ultrasound (or other modalities when indicated) will help to identify lateral neck lymph nodes of concern. These findings should be addressed using fine-needle aspiration biopsy. A comprehensive neck dissection of at least nodal levels IIa, III, IV, and Vb should be performed when indicated to optimize disease control.

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

PubMed

Lu, Bingxin; Leong, Hon Wai

2016-02-01

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

Stabilization of high-valent Fe(IV)S6-cores by dithiocarbamate(1-) and 1,2-dithiolate(2-) ligands in octahedral [Fe(IV)(Et2dtc)(3-n)(mnt)(n)]((n-1)-) complexes (n=0, 1, 2, 3): a spectroscopic and density functional theory computational study.

PubMed

Milsmann, Carsten; Sproules, Stephen; Bill, Eckhard; Weyhermüller, Thomas; George, Serena DeBeer; Wieghardt, Karl

2010-03-22

A detailed spectroscopic and quantum chemical analysis is presented to elucidate the electronic structures of the octahedral complexes [Fe(Et(2)dtc)(3-n)(mnt)(n)](n-) (1-4, n=3, 2, 1, 0) and their one-electron oxidized analogues [Fe(Et(2)dtc)(3-n)(mnt)(n)]((n-1)-) (1(ox)-4(ox)); (mnt)(2-) represents maleonitriledithiolate(2-) and (Et(2)dtc)(1-) is the diethyldithiocarbamato(1-) ligand. By using X-ray crystallography, Mössbauer spectroscopy, and Fe and S K-edge X-ray absorption spectroscopy (XAS) it is convincingly shown that, in contrast to our previous studies on [Fe(cyclam)(mnt)](1+) (cyclam=1,4,8,11-tetraazacyclotetradecane), the oxidation of 1-4 is metal-centered yielding the genuine Fe(IV) complexes 1(ox)-4(ox). For the latter complexes, a spin ground state of S=1 has been established by magnetic susceptibility measurements, which indicates a low-spin d(4) configuration. DFT calculations at the B3LYP level support this electronic structure and exclude the presence of a ligand pi radical coordinated to an intermediate-spin ferric ion. Mössbauer parameters and XAS spectra have been calculated to calibrate our computational results against the experiment. Finally, a simple ligand-field approach is presented to correlate the structural features obtained from X-ray crystallography (100 K) with the spectroscopic data.

Using distances between Top-n-gram and residue pairs for protein remote homology detection.

PubMed

Liu, Bin; Xu, Jinghao; Zou, Quan; Xu, Ruifeng; Wang, Xiaolong; Chen, Qingcai

2014-01-01

Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Currently, discriminative methods based on Support Vector Machines (SVMs) achieve the state-of-the-art performance. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods. Two new methods for protein remote homology detection were proposed, called SVM-DR and SVM-DT. SVM-DR is a sequence-based method, in which the feature vector representation for protein is based on the distances between residue pairs. SVM-DT is a profile-based method, which considers the distances between Top-n-gram pairs. Top-n-gram can be viewed as a profile-based building block of proteins, which is calculated from the frequency profiles. These two methods are position dependent approaches incorporating the sequence-order information of protein sequences. Various experiments were conducted on a benchmark dataset containing 54 families and 23 superfamilies. Experimental results showed that these two new methods are very promising. Compared with the position independent methods, the performance improvement is obvious. Furthermore, the proposed methods can also provide useful insights for studying the features of protein families. The better performance of the proposed methods demonstrates that the position dependant approaches are efficient for protein remote homology detection. Another advantage of our methods arises from the explicit feature space representation, which can be used to analyze the characteristic features of protein families. The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

PubMed

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

PubMed Central

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-01-01

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction. PMID:26540059

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

PubMed

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-11-03

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

How sensitive (second-generation) thyroglobulin measurement is changing paradigms for monitoring patients with differentiated thyroid cancer, in the absence or presence of thyroglobulin autoantibodies

PubMed Central

Spencer, Carole; LoPresti, Jonathan; Fatemi, Shireen

2014-01-01

Purpose of review To discuss new insights regarding how sensitive (second-generation) thyroglobulin immunometric assays (Tg2GIMAs), (functional sensitivities ≤0.10 μg/L) necessitate different approaches for postoperative thyroglobulin monitoring of patients with differentiated thyroid cancer (DTC), depending on the presence of thyroglobulin autoantibodies (TgAbs). Recent findings Reliable low-range serum thyroglobulin measurement has both enhanced clinical utility and economic advantages, provided TgAb is absent (∼75% DTC patients). Basal [nonthyroid-stimulating hormone (TSH) stimulated] Tg2GIMA measurement obviates the need for recombinant human TSH stimulation because basal Tg2GIMA below 0.20 μg/L has comparable negative predictive value (>95%) to recombinant human TSH-stimulated thyroglobulin values below the cutoff of 2 μg/L. Now that radioiodine remnant ablation is no longer considered necessary to treat low-risk DTC, the trend and doubling time of low basal thyroglobulin values arising from postsurgical thyroid remnants have recognized prognostic significance. The major limitation of Tg2GIMA testing is interference by TgAb (∼25% DTC patients), causing Tg2GIMA underestimation that can mask disease. When TgAb is present, the trend in TgAb concentrations (measured by the same method) can serve as the primary (surrogate) tumor-marker and be augmented by thyroglobulin measured by a TgAb-resistant class of method (radioimmunoassay or liquid chromatography-tandem mass spectrometry). Summary The growing use of Tg2GIMA measurement is changing paradigms for postoperative DTC monitoring. When TgAb is absent, it is optimal to monitor the basal Tg2GIMA trend and doubling time (using the same method) in preference to recombinant human TSH-stimulated thyroglobulin testing. When TgAb is present, interference renders Tg2GIMA testing unreliable and the trend in serum TgAb concentrations per se (same method) can serve as a (surrogate) tumor-marker. PMID:25122493

Observation of Discrete-Time-Crystal Signatures in an Ordered Dipolar Many-Body System

NASA Astrophysics Data System (ADS)

Rovny, Jared; Blum, Robert L.; Barrett, Sean E.

2018-05-01

A discrete time crystal (DTC) is a robust phase of driven systems that breaks the discrete time translation symmetry of the driving Hamiltonian. Recent experiments have observed DTC signatures in two distinct systems. Here we show nuclear magnetic resonance observations of DTC signatures in a third, strikingly different system: an ordered spatial crystal. We use a novel DTC echo experiment to probe the coherence of the driven system. Finally, we show that interactions during the pulse of the DTC sequence contribute to the decay of the signal, complicating attempts to measure the intrinsic lifetime of the DTC.

Observation of Discrete-Time-Crystal Signatures in an Ordered Dipolar Many-Body System.

PubMed

Rovny, Jared; Blum, Robert L; Barrett, Sean E

2018-05-04

A discrete time crystal (DTC) is a robust phase of driven systems that breaks the discrete time translation symmetry of the driving Hamiltonian. Recent experiments have observed DTC signatures in two distinct systems. Here we show nuclear magnetic resonance observations of DTC signatures in a third, strikingly different system: an ordered spatial crystal. We use a novel DTC echo experiment to probe the coherence of the driven system. Finally, we show that interactions during the pulse of the DTC sequence contribute to the decay of the signal, complicating attempts to measure the intrinsic lifetime of the DTC.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

PubMed Central

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-01-01

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. PMID:27548179

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

PubMed

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-08-19

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

Internet-Based Direct-to-Consumer Genetic Testing: A Systematic Review

PubMed Central

Rubinelli, Sara; Ceretti, Elisabetta; Gelatti, Umberto

2015-01-01

Background Direct-to-consumer genetic tests (DTC-GT) are easily purchased through the Internet, independent of a physician referral or approval for testing, allowing the retrieval of genetic information outside the clinical context. There is a broad debate about the testing validity, their impact on individuals, and what people know and perceive about them. Objective The aim of this review was to collect evidence on DTC-GT from a comprehensive perspective that unravels the complexity of the phenomenon. Methods A systematic search was carried out through PubMed, Web of Knowledge, and Embase, in addition to Google Scholar according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist with the key term “Direct-to-consumer genetic test.” Results In the final sample, 118 articles were identified. Articles were summarized in five categories according to their focus on (1) knowledge of, attitude toward use of, and perception of DTC-GT (n=37), (2) the impact of genetic risk information on users (n=37), (3) the opinion of health professionals (n=20), (4) the content of websites selling DTC-GT (n=16), and (5) the scientific evidence and clinical utility of the tests (n=14). Most of the articles analyzed the attitude, knowledge, and perception of DTC-GT, highlighting an interest in using DTC-GT, along with the need for a health care professional to help interpret the results. The articles investigating the content analysis of the websites selling these tests are in agreement that the information provided by the companies about genetic testing is not completely comprehensive for the consumer. Given that risk information can modify consumers’ health behavior, there are surprisingly few studies carried out on actual consumers and they do not confirm the overall concerns on the possible impact of DTC-GT. Data from studies that investigate the quality of the tests offered confirm that they are not informative, have little predictive power, and do not measure genetic risk appropriately. Conclusions The impact of DTC-GT on consumers’ health perceptions and behaviors is an emerging concern. However, negative effects on consumers or health benefits have yet to be observed. Nevertheless, since the online market of DTC-GT is expected to grow, it is important to remain aware of a possible impact. PMID:26677835

Trust in prescription drug brand websites: website trust cues, attitude toward the website, and behavioral intentions.

PubMed

Huh, Jisu; Shin, Wonsun

2014-01-01

Direct-to-consumer (DTC) prescription drug brand websites, as a form of DTC advertising, are receiving increasing attention due to the growing number and importance as an ad and a consumer information source. This study examined consumer trust in a DTC website as an important factor influencing consumers' attitude toward the website and behavioral intention. Applying the conceptual framework of website trust, the particular focus of investigation was the effect of the website trust cue factor on consumers' perceived DTC website trust and subsequent attitudinal and behavioral responses. Results show a significant relation between the website trust cue factor and consumers' perceived DTC website trust. Perceived DTC website trust, in turn, was found to be significantly associated with consumers' attitude toward the DTC website and behavioral intention.

Identification of eggs from different production systems based on hyperspectra and CS-SVM.

PubMed

Sun, J; Cong, S L; Mao, H P; Zhou, X; Wu, X H; Zhang, X D

2017-06-01

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky-Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition

PubMed Central

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

PubMed

She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

2015-01-01

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Choice, Transparency, Coordination, and Quality Among Direct-to-Consumer Telemedicine Websites and Apps Treating Skin Disease.

PubMed

Resneck, Jack S; Abrouk, Michael; Steuer, Meredith; Tam, Andrew; Yen, Adam; Lee, Ivy; Kovarik, Carrie L; Edison, Karen E

2016-07-01

Evidence supports use of teleconsultation for improving patient access to dermatology. However, little is known about the quality of rapidly expanding direct-to-consumer (DTC) telemedicine websites and smartphone apps diagnosing and treating skin disease. To assess the performance of DTC teledermatology services. Simulated patients submitted a series of structured dermatologic cases with photographs, including neoplastic, inflammatory, and infectious conditions, using regional and national DTC telemedicine websites and smartphone apps offering services to California residents. Choice of clinician, transparency of credentials, clinician location, demographic and medical data requested, diagnoses given, treatments recommended or prescribed, adverse effects discussed, care coordination. We received responses for 62 clinical encounters from 16 DTC telemedicine websites from February 4 to March 11, 2016. None asked for identification or raised concerns about pseudonym use or falsified photographs. During most encounters (42 [68%]), patients were assigned a clinician without any choice. Only 16 (26%) disclosed information about clinician licensure, and some used internationally based physicians without California licenses. Few collected the name of an existing primary care physician (14 [23%]) or offered to send records (6 [10%]). A diagnosis or likely diagnosis was proffered in 48 encounters (77%). Prescription medications were ordered in 31 of 48 diagnosed cases (65%), and relevant adverse effects or pregnancy risks were disclosed in a minority (10 of 31 [32%] and 6 of 14 [43%], respectively). Websites made several correct diagnoses in clinical scenarios where photographs alone were adequate, but when basic additional history elements (eg, fever, hypertrichosis, oligomenorrhea) were important, they regularly failed to ask simple relevant questions and diagnostic performance was poor. Major diagnoses were repeatedly missed, including secondary syphilis, eczema herpeticum, gram-negative folliculitis, and polycystic ovarian syndrome. Regardless of the diagnoses given, treatments prescribed were sometimes at odds with existing guidelines. Telemedicine has potential to expand access to high-value health care. Our findings, however, raise concerns about the quality of skin disease diagnosis and treatment provided by many DTC telemedicine websites. Ongoing expansion of health plan coverage of these services may be premature. Until improvements are made, patients risk using health care services that lack transparency, choice, thoroughness, diagnostic and therapeutic quality, and care coordination. We offer several suggestions to improve the quality of DTC telemedicine websites and apps and avoid further growth of fragmented, low-quality care.

Telehealth physician oversight over direct to consumer testing: doctors working with patients towards patient empowerment.

PubMed

Chung, Rick

2012-06-01

Patient empowerment has increased the demand for direct to consumer (DTC) laboratory testing. Multiple professional societies and advocacy groups have raised concerns over how DTC laboratory testing is being offered to consumers without proper physician oversight. Physician telehealth services can properly oversee DTC laboratory testing in a safe and medically sound manner. Using telehealth protocols and standards established by professional health organizations and state regulators, physician telehealth oversight in DTC laboratory test ordering can be effective to increase patient access to healthcare services. With proper physician oversight in test interpretation, post-test counseling, and information collaboration, DTC laboratory testing can remain a reliable and convenient option for consumers. Working within the channel of distribution of most DTC laboratory testing, physician telehealth services can properly oversee DTC laboratory testing in a safe and medically sound manner to ensure that proper test interpretation, counseling, and information collaboration are achieved. Physician telehealth services can properly oversee DTC laboratory testing to ensure that proper test interpretation, counseling, and information collaboration are achieved.

A structural SVM approach for reference parsing.

PubMed

Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

2011-06-09

Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

A real-time neutron-gamma discriminator based on the support vector machine method for the time-of-flight neutron spectrometer

NASA Astrophysics Data System (ADS)

Wei, ZHANG; Tongyu, WU; Bowen, ZHENG; Shiping, LI; Yipo, ZHANG; Zejie, YIN

2018-04-01

A new neutron-gamma discriminator based on the support vector machine (SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination (PSD) property. The SVM algorithm is implemented in field programmable gate array (FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.

lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

PubMed

Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

2015-01-01

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile.

PubMed

Verma, Ruchi; Varshney, Grish C; Raghava, G P S

2010-06-01

The rate of human death due to malaria is increasing day-by-day. Thus the malaria causing parasite Plasmodium falciparum (PF) remains the cause of concern. With the wealth of data now available, it is imperative to understand protein localization in order to gain deeper insight into their functional roles. In this manuscript, an attempt has been made to develop prediction method for the localization of mitochondrial proteins. In this study, we describe a method for predicting mitochondrial proteins of malaria parasite using machine-learning technique. All models were trained and tested on 175 proteins (40 mitochondrial and 135 non-mitochondrial proteins) and evaluated using five-fold cross validation. We developed a Support Vector Machine (SVM) model for predicting mitochondrial proteins of P. falciparum, using amino acids and dipeptides composition and achieved maximum MCC 0.38 and 0.51, respectively. In this study, split amino acid composition (SAAC) is used where composition of N-termini, C-termini, and rest of protein is computed separately. The performance of SVM model improved significantly from MCC 0.38 to 0.73 when SAAC instead of simple amino acid composition was used as input. In addition, SVM model has been developed using composition of PSSM profile with MCC 0.75 and accuracy 91.38%. We achieved maximum MCC 0.81 with accuracy 92% using a hybrid model, which combines PSSM profile and SAAC. When evaluated on an independent dataset our method performs better than existing methods. A web server PFMpred has been developed for predicting mitochondrial proteins of malaria parasites ( http://www.imtech.res.in/raghava/pfmpred/).

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

PubMed Central

Ma, Xiaoqi

2015-01-01

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

NASA Astrophysics Data System (ADS)

Zhan, Liwei; Li, Chengwei

2017-02-01

A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

Information Extraction from Multiple Syntactic Sources

DTIC Science & Technology

2004-05-01

Performance of SVM and KNN (k=3) on different kernel setups. Types are ordered in decreasing order of frequency of occurrence in the ACE corpus. For SVM, the...name. But it is not easy to recognize “A Real New York Bargain” as a company name. In other languages or transcripts of English speech where...symbolic rules for extraction of posted computer jobs. It only assumed simple syntactic preprocessing such as tokeniza- tion and Part-of- Speech tagging

Direct-to-consumer advertising of prescription medications: implications for patients with cancer.

PubMed

Viale, Pamela Hallquist

2002-04-01

To review the phenomenon of direct-to-consumer (DTC) advertising of prescription medications in the media, with an overview of pertinent studies in the literature regarding patients' and healthcare professionals' perspectives on DTC advertising. Journal articles, media, and clinical experience. DTC advertising of prescription medications is extremely prevalent in U.S. society. Advertising of medications is an expensive business; yearly spending is expected to reach $7.5 billion by 2005. Although opinions vary regarding DTC advertising, healthcare professionals, including oncology nurses, must be prepared to discuss DTC-advertised medications and treatments with their patients. Communication is the key to helping patients decipher the deluge of DTC advertisements in the media and determine the accuracy of this ever-increasing source of medical information. Oncology nurses need to be aware of the increases in DTC advertising of prescription medications and the importance of guiding patients through appropriate medication choices by education.

76 FR 77281 - Self-Regulatory Organizations; The Depository Trust Company; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-12

... SECURITIES AND EXCHANGE COMMISSION [Release No. 34-65901; File No. SR-DTC-2011-10] Self-Regulatory... DTC participants. DTC's experience demonstrates that when participants, issuers, underwriters, agents... Exchange Act Release 24818 (August 19, 1987), 52 FR 31833 (August 24, 1987) (File No. SR-DTC-87-10); 25948...

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Disease-specific direct-to-consumer advertising for reminding consumers to take medications.

PubMed

Bhutada, Nilesh S; Rollins, Brent L

2015-01-01

To assess the relationship between disease-specific direct-to-consumer (DTC) advertising, via traditional advertising effectiveness measures, and consumers' self-reported medication-taking behavior. Data were gathered for 514 respondents (age 18 and above) using an online survey panel. Participants were exposed to a disease-specific (i.e., nonbranded) DTC advertising for depression. The advertising stimulus created for the study was based on the Food and Drug Administration guidelines for disease-specific DTC advertising and modeled after current print disease-specific DTC advertising. Participants reviewed the advertising stimulus through the online program and then responded to a questionnaire containing closed-ended questions assessing the constructs. Data were analyzed using chi-square tests. All tests were interpreted at an a priori alpha of 0.05. Significantly more respondents who were highly involved, paid more attention to the advertisement, and were responsive to DTC advertisements in the past indicated that the disease-specific DTC advertising stimulus reminded them to take their depression and other medications. These exploratory results show disease-specific DTC advertising can help people remember to take their prescription medication when viewed, which may lead to more positive medication-taking behavior and increased medication adherence. Additionally, given the fair balance and legal issues surrounding product-specific DTC advertising, disease-specific DTC advertising can serve as an effective component of the marketing mix for pharmaceutical manufacturers. Future research should attempt to study the impact of disease-specific DTC advertising on consumers' actual medication adherence using standardized adherence measures such as prescription records.

"For the good of the patient," survey of the physicians of the National Medical Association regarding perceptions of DTC advertising, Part II, 2006.

PubMed Central

Morris, Albert W.; Gadson, Sandra L.; Burroughs, Valentine

2007-01-01

BACKGROUND: Since the advent of direct-to-consumer (DTC) advertising in the 1980s, there have been numerous studies and surveys on the topic, addressing issues as varied as its impact on patient understanding of health conditions to its repercussions for drug spending. However, until 2001, there was a dearth of research on DTC advertising's impact on minority populations, specifically the African-American community. The National Medical Association (NMA) remedied that in 2001 by undertaking a landmark study that gauged African-American physicians' perceptions of DTC advertising, its impact on the doctor-patient relationship and, perhaps most importantly, its role in educating underserved populations about critical health issues and potential treatments. In 2006, the NMA decided to once again poll its members on this critical issue to gauge not only current perceptions but how the community's understanding of DTC advertising has changed since 2001. RESULTS: The 2006 survey revealed several clear trends: NMA physicians are more positive toward DTC advertising now than they were in 2001; African-American physicians see DTC advertising as providing substantial educational benefits; physicians believe that DTC advertising helps rather than hurts the doctor-patient relationship; and African-American physicians see the benefits of DTC advertising outweighing its drawbacks. It must be noted that NMA physicians also had clear concerns about DTC advertising that point to potential areas of improvement for pharmaceutical companies. PMID:17393955

"For the good of the patient," survey of the physicians of the National Medical Association regarding perceptions of DTC advertising, Part II, 2006.

PubMed

Morris, Albert W; Gadson, Sandra L; Burroughs, Valentine

2007-03-01

Since the advent of direct-to-consumer (DTC) advertising in the 1980s, there have been numerous studies and surveys on the topic, addressing issues as varied as its impact on patient understanding of health conditions to its repercussions for drug spending. However, until 2001, there was a dearth of research on DTC advertising's impact on minority populations, specifically the African-American community. The National Medical Association (NMA) remedied that in 2001 by undertaking a landmark study that gauged African-American physicians' perceptions of DTC advertising, its impact on the doctor-patient relationship and, perhaps most importantly, its role in educating underserved populations about critical health issues and potential treatments. In 2006, the NMA decided to once again poll its members on this critical issue to gauge not only current perceptions but how the community's understanding of DTC advertising has changed since 2001. The 2006 survey revealed several clear trends: NMA physicians are more positive toward DTC advertising now than they were in 2001; African-American physicians see DTC advertising as providing substantial educational benefits; physicians believe that DTC advertising helps rather than hurts the doctor-patient relationship; and African-American physicians see the benefits of DTC advertising outweighing its drawbacks. It must be noted that NMA physicians also had clear concerns about DTC advertising that point to potential areas of improvement for pharmaceutical companies.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

PubMed Central

Manavalan, Balachandran; Shin, Tae H.; Lee, Gwang

2018-01-01

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html. PMID:29616000

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.

PubMed

Manavalan, Balachandran; Shin, Tae H; Lee, Gwang

2018-01-01

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

Comparison of differentiated thyroid cancer in children and adolescents (≤20 years) with young adults.

PubMed

Alzahrani, Ali S; Alkhafaji, Dania; Tuli, Mahmoud; Al-Hindi, Hindi; Sadiq, Bakr Bin

2016-04-01

Age is a major prognostic factor in differentiated thyroid cancer (DTC). It is not clear if paediatric DTC has a different histopathological profile and outcome than DTC in adult patients <45 years of age. To assess whether DTC in children and adolescents differs from young age group by comparing paediatric DTC (age ≤ 20) with DTC in patients >20 to <45 years of age. We studied all cases of paediatric DTC seen during the period 1998-2011. We compared this group with a large sample of 213 consecutive adult patients in the age group >20 to <45 years seen during the period 1998-1999 in terms of their pathological features, extent of the disease and long-term outcome. Both groups were managed by the same team at a single institution. A total of 310 DTC were studied including 97 paediatric patients [median age 17 years (range, 8-20)] and 213 young adult patients [median age 33 years (range, 20·5-44·9)]. There was no difference in gender distribution, tumour subtypes, size and tumour multifocality, but there was a significantly higher rate of extrathyroidal extension [40/75 (53·3%) vs 81/213 (38·0%), P = 0·03], lymph node [57/73 (78%) vs 102/183 (55·7%), P < 0·0001] and distant metastases [16/97 (16·5%) vs 8/213 (3·8%), P < 0·0001] in the paediatric than the adult groups. Kaplan-Meier analysis showed a higher risk of persistent/recurrent disease in the paediatric group than adults (log-rank test 0·03). However, there was no mortality secondary to DTC in both groups. Paediatric DTC is distinct from DTC in the young adults (age >20 to <45 years). It is characterized by a higher rate of extrathyroidal extension, lymph node and distant metastases and a higher risk of persistent/recurrent DTC. © 2015 John Wiley & Sons Ltd.

Prescription medication changes following direct-to-consumer personal genomic testing: Findings from the Impact of Personal Genomics (PGen) Study

PubMed Central

Carere, Deanna Alexis; VanderWeele, Tyler; Vassy, Jason L.; van der Wouden, Cathelijne; Roberts, J. Scott; Kraft, Peter; Green, Robert C.

2016-01-01

Purpose To measure the frequency of prescription medication changes following direct-to-consumer personal genomic testing (DTC-PGT) and their association with the pharmacogenomic results received. Methods New DTC-PGT customers were enrolled in 2012 and completed surveys prior to return of results and 6 months post-results; DTC-PGT results were linked to survey data. ‘Atypical response’ pharmacogenomic results were defined as those indicating an increase or decrease in risk of an adverse drug event or likelihood of therapeutic benefit. At follow-up, participants reported prescription medication changes and health care provider consultation. Results Follow-up data were available from 961 participants, of which 54 (5.6%) reported changing a medication they were taking, or starting a new medication, due to their DTC-PGT results. Of these, 45 (83.3%) reported consulting with a health care provider regarding the change. Pharmacogenomic results were available for 961 participants, of which 875 (91.2%) received ≥1 atypical response result. For each such result received, the odds of reporting a prescription medication change increased 1.57 times (95% confidence interval = 1.17, 2.11). Conclusion Receipt of pharmacogenomic results indicating atypical drug response is common with DTC-PGT, and associated with prescription medication changes; however, fewer than 1% of consumers report unsupervised changes at 6 months post-testing. PMID:27657683

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Application of Support Vector Machine to Forex Monitoring

NASA Astrophysics Data System (ADS)

Kamruzzaman, Joarder; Sarker, Ruhul A.

Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

NASA Astrophysics Data System (ADS)

Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

Fuzzy support vector machine for microarray imbalanced data classification

NASA Astrophysics Data System (ADS)

Ladayya, Faroh; Purnami, Santi Wulan; Irhamah

2017-11-01

DNA microarrays are data containing gene expression with small sample sizes and high number of features. Furthermore, imbalanced classes is a common problem in microarray data. This occurs when a dataset is dominated by a class which have significantly more instances than the other minority classes. Therefore, it is needed a classification method that solve the problem of high dimensional and imbalanced data. Support Vector Machine (SVM) is one of the classification methods that is capable of handling large or small samples, nonlinear, high dimensional, over learning and local minimum issues. SVM has been widely applied to DNA microarray data classification and it has been shown that SVM provides the best performance among other machine learning methods. However, imbalanced data will be a problem because SVM treats all samples in the same importance thus the results is bias for minority class. To overcome the imbalanced data, Fuzzy SVM (FSVM) is proposed. This method apply a fuzzy membership to each input point and reformulate the SVM such that different input points provide different contributions to the classifier. The minority classes have large fuzzy membership so FSVM can pay more attention to the samples with larger fuzzy membership. Given DNA microarray data is a high dimensional data with a very large number of features, it is necessary to do feature selection first using Fast Correlation based Filter (FCBF). In this study will be analyzed by SVM, FSVM and both methods by applying FCBF and get the classification performance of them. Based on the overall results, FSVM on selected features has the best classification performance compared to SVM.

Clinical management and outcomes in patients with hyperfunctioning distant metastases from differentiated thyroid cancer after total thyroidectomy and radioactive iodine therapy.

PubMed

Qiu, Zhong-Ling; Shen, Chen-Tian; Luo, Quan-Yong

2015-02-01

Hyperfunctioning distant metastasis (HFDM) from differentiated thyroid cancer (DTC) is a rare entity. This study aimed to assess the outcomes of DTC patients presenting with HFDM after total thyroidectomy and radioactive iodine therapy. A total of 5367 DTC patients treated with (131)I after total thyroidectomy were analyzed retrospectively from January 1991 to June 2013. Therapeutic efficacy was evaluated based on changes in serum thyroglobulin (Tg) and anatomical imaging changes in metastatic lesions. The relationships between survival time and several variables were assessed by univariate and multivariate analyses using the Kaplan-Meier method and Cox's proportional hazards model respectively. Thirty-eight patients with HFDM from DTC were diagnosed, including four with hyperthyroidism, four with subclinical hyperthyroidism, and three with subclinical hypothyroidism. The remaining 27 were euthyroid. Of 25 patients with lung metastases, 84% (21/25) showed disappearance or shrinkage of lung nodules; of 24 patients with bone metastases, 66.67% (16/24) exhibited no obvious imaging changes in metastatic bone lesions after (131)I therapy. Serum Tg decreased significantly in 81.58% (31/38) and increased in 18.42% (7/38) after (131)I therapy. The 10-year survival rate of DTC patients with HFDM was 65.79% (25/38). Multivariate analyses identified age at occurrence of distant metastases (<45 years), only lung metastases, and papillary thyroid cancer (PTC; p=0.032, NA, and 0.043) as independent predictors of survival. The response of hyperfunctioning lung metastases to (131)I treatment was better than that of non-hyperfunctioning lung metastases in DTC, while hyperfunctioning bone metastases responded similarly compared to non-hyperfunctioning bone metastases. Patients younger than 45 years at occurrence of distant metastases, those with only lung metastases, and patients with PTC had better prognoses.

Family History of Cancer and Risk of Sporadic Differentiated Thyroid Carcinoma

PubMed Central

Xu, Li; Li, Guojun; Wei, Qingyi; El-Naggar, Adel K.; Sturgis, Erich M.

2011-01-01

BACKGROUND Thyroid cancer incidence in the United States, particularly in women, has increased dramatically since 1980s. While the causes of thyroid cancer in most patients remain largely unknown, evidence suggests the existence of an inherited predisposition to development of differentiated thyroid cancer (DTC). Therefore, we explored the association between sporadic DTC and family history of cancer. METHODS In a retrospective hospital-based case-control study of prospectively recruited subjects who completed the study questionnaire upon enrollment, unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) as estimates of the DTC risk associated with first-degree family history of cancer. RESULTS The study included 288 patients with sporadic DTC and 591 cancer-free controls. Family history of thyroid cancer in first-degree relatives was associated with increased DTC risk (adjusted OR = 4.1, 95% CI: 1.7–9.9). All DTC cases in patients with a first-degree family history of thyroid cancer were cases of papillary thyroid carcinoma (PTC) (adjusted OR = 4.6, 95 CI%: 1.9–11.1). Notably, the risk of PTC was highest in subjects with a family history of thyroid cancer in siblings (OR = 7.4, 95% CI: 1.8–30.4). In addition, multifocal primary tumor was more common among PTC patients with first-degree family history of thyroid cancer than among PTC patients with no first-degree family history of thyroid cancer (68.8% vs. 35.5%, p = 0.01). CONCLUSIONS Our study suggests that family history of thyroid cancer in first-degree relatives, particularly in siblings, is associated with an increased risk of sporadic PTC. PMID:21800288

Intergenerational effects of parental substance-related convictions and adult drug treatment court participation on children’s school performance

PubMed Central

Gifford, Elizabeth J.; Sloan, Frank A.; Evans, Kelly E.

2015-01-01

Objective This study examined the intergenerational effects of parental conviction of a substance-related charge on children’s academic performance and, conditional on a conviction, whether completion of an adult drug treatment court (DTC) program was associated with improved school performance. Method State administrative data from North Carolina courts, birth records, and school records were linked for 2005–12. Math and reading end-of-grade test scores and absenteeism were examined for 5 groups of children, those with parents who: were not convicted on any criminal charge, were convicted on a substance-related charge and not referred by a court to a DTC, were referred to a DTC but did not enroll, enrolled in a DTC but did not complete, and completed a DTC program. Results Accounting for demographic and socioeconomic factors, the school performance of children whose parents were convicted of a substance-related offense was worse than that of children whose parents were not convicted on any charge. These differences were statistically significant but substantially reduced after controlling for socioeconomic characteristics, e.g., mother’s educational attainment. We found no evidence that parent participation in an adult DTC program led to improved school performance of their children. Conclusion While the children of convicted parents fared worse on average, much—but not all—of this difference was attributed to socioeconomic factors, with the result that parental conviction remained a risk factor for poorer school performance. Even though adult DTCs have been shown to have other benefits, we could detect no intergenerational benefit in improved school performance of their children. PMID:26460705

[Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

PubMed

Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

2015-01-01

In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

Using visible and near-infrared diffuse reflectance spectroscopy for predicting soil properties based on regression with peaks parameters as derived from continuum-removed spectra

NASA Astrophysics Data System (ADS)

Vasat, Radim; Klement, Ales; Jaksik, Ondrej; Kodesova, Radka; Drabek, Ondrej; Boruvka, Lubos

2014-05-01

Visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS) provides a rapid and inexpensive tool for simultaneous prediction of a variety of soil properties. Usually, some sophisticated multivariate mathematical or statistical methods are employed in order to extract the required information from the raw spectra measurement. For this purpose especially the Partial least squares regression (PLSR) and Support vector machines (SVM) are the most frequently used. These methods generally benefit from the complexity with which the soil spectra are treated. But it is interesting that also techniques that focus only on a single spectral feature, such as a simple linear regression with selected continuum-removed spectra (CRS) characteristic (e.g. peak depth), can often provide competitive results. Therefore, we decided to enhance the potential of CRS taking into account all possible CRS peak parameters (area, width and depth) and develop a comprehensive methodology based on multiple linear regression approach. The eight considered soil properties were oxidizable carbon content (Cox), exchangeable (pHex) and active soil pH (pHa), particle and bulk density, CaCO3 content, crystalline and amorphous (Fed) and amorphous Fe (Feox) forms. In four cases (pHa, bulk density, Fed and Feox), of which two (Fed and Feox) were predicted reliably accurately (0.50 < R2cv < 0.80) and the other two (pHa and bulk density) only poorly (R2cv < 0.50), we obtained slightly better results than with PLSR and SVM. In one case (pHex) we achieved a significantly higher, although just reliable, accuracy (R2cv = 0.601) than with PLSR and SVM (R2cv = 0.448 and 0.442, resp.). But most interestingly, in the case of particle density, the presented approach outperformed the PLSR and SVM dramatically offering a fairly accurate prediction (R2cv = 0.827) against two failures (R2cv = 0.034 and 0.121 for PLSR and SVM, resp.). In last two cases (Cox and CaCO3) a slightly worse results were achieved then with PLSR and SVM with overall fairly accurate prediction (R2cv > 0.80). Acknowledgment: Authors acknowledge the financial support of the Ministry of Agriculture of the Czech Republic (grant No. QJ1230319).

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

PubMed Central

2012-01-01

Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to select a single parameter. PMID:22703641

A comparison of non-parametric techniques to estimate incident photosynthetically active radiation from MODIS for monitoring primary production

NASA Astrophysics Data System (ADS)

Brown, M. G. L.; He, T.; Liang, S.

2016-12-01

Satellite-derived estimates of incident photosynthetically active radiation (PAR) can be used to monitor global change, are required by most terrestrial ecosystem models, and can be used to estimate primary production according to the theory of light use efficiency. Compared with parametric approaches, non-parametric techniques that include an artificial neural network (ANN), support vector machine regression (SVM), an artificial bee colony (ABC), and a look-up table (LUT) do not require many ancillary data as inputs for the estimation of PAR from satellite data. In this study, a selection of machine learning methods to estimate PAR from MODIS top of atmosphere (TOA) radiances are compared to a LUT approach to determine which techniques might best handle the nonlinear relationship between TOA radiance and incident PAR. Evaluation of these methods (ANN, SVM, and LUT) is performed with ground measurements at seven SURFRAD sites. Due to the design of the ANN, it can handle the nonlinear relationship between TOA radiance and PAR better than linearly interpolating between the values in the LUT; however, training the ANN has to be carried out on an angular-bin basis, which results in a LUT of ANNs. The SVM model may be better for incorporating multiple viewing angles than the ANN; however, both techniques require a large amount of training data, which may introduce a regional bias based on where the most training and validation data are available. Based on the literature, the ABC is a promising alternative to an ANN, SVM regression and a LUT, but further development for this application is required before concrete conclusions can be drawn. For now, the LUT method outperforms the machine-learning techniques, but future work should be directed at developing and testing the ABC method. A simple, robust method to estimate direct and diffuse incident PAR, with minimal inputs and a priori knowledge, would be very useful for monitoring global change of primary production, particularly of pastures and rangeland, which have implications for livestock and food security. Future work will delve deeper into the utility of satellite-derived PAR estimation for monitoring primary production in pasture and rangelands.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Genetic counseling and the ethical issues around direct to consumer genetic testing.

PubMed

Hawkins, Alice K; Ho, Anita

2012-06-01

Over the last several years, direct to consumer(DTC) genetic testing has received increasing attention in the public, healthcare and academic realms. DTC genetic testing companies face considerable criticism and scepticism,particularly from the medical and genetic counseling community. This raises the question of what specific aspects of DTC genetic testing provoke concerns, and conversely,promises, for genetic counselors. This paper addresses this question by exploring DTC genetic testing through an ethic allens. By considering the fundamental ethical approaches influencing genetic counseling (the ethic of care and principle-based ethics) we highlight the specific ethical concerns raised by DTC genetic testing companies. Ultimately,when considering the ethics of DTC testing in a genetic counseling context, we should think of it as a balancing act. We need careful and detailed consideration of the risks and troubling aspects of such testing, as well as the potentially beneficial direct and indirect impacts of the increased availability of DTC genetic testing. As a result it is essential that genetic counselors stay informed and involved in the ongoing debate about DTC genetic testing and DTC companies. Doing so will ensure that the ethical theories and principles fundamental to the profession of genetic counseling are promoted not just in traditional counseling sessions,but also on a broader level. Ultimately this will help ensure that the public enjoys the benefits of an increasingly genetic based healthcare system.

Nutrigenomics and ethics interface: direct-to-consumer services and commercial aspects.

PubMed

Ries, Nola M; Castle, David

2008-12-01

A growing variety and number of genetic tests are advertised and sold directly to consumers (DTC) via the Internet, including nutrigenomic tests and associated products and services. Consumers have more access to genetic information about themselves, but access does not entail certainty about the implications of test results. Potential personal and public health harms and benefits are associated with DTC access to genetic testing services. Early policy responses to direct-to-consumer (DTC) genetic testing often involved calls for bans, and some jurisdictions prohibited DTC genetic tests. Recent policy responses by oversight bodies acknowledge expansion in the range of DTC tests available and suggest that a "one-size-fits-all" regulatory approach is not appropriate for all genetic tests. This review discusses ethical and regulatory aspects of DTC genetic testing, focusing particularly on nutrigenomic tests. We identify policy options for regulating DTC genetic tests, including full or partial prohibitions, enforcement of existing truth-in-advertising laws, and more comprehensive information disclosure about genetic tests. We advocate the latter option as an important means to improve transparency about current evidence on the strengths and limits of gene-disease associations and allow consumers to make informed purchasing decisions in the DTC marketplace.

If It Works for Pills, Can It Work for Skills? Direct-to-Consumer Social Marketing of Evidence-Based Psychological Treatments.

PubMed

Friedberg, Robert D; Bayar, Hasan

2017-06-01

The emergence of evidence-based psychological treatments (EVPTs) is a scientific success story, but unfortunately the application of these empirically supported procedures has been slow to gain ground in treatment-as-usual settings. This Open Forum commentary argues that direct-to-consumer (DTC) marketing, which has worked well in communicating the advantages of various medicines, should perhaps be considered for use in social marketing of EVPTs. DTC marketing of pharmaceuticals is a long-standing advertising strategy in the United States. In fact, DTC marketing of psychotropic medicines is quite a success story. The authors recommend various strategies for using marketing science to devise DTC advertising of EVPTs, discuss previous research on DTC campaigns, and describe initiatives launched in the United Kingdom and Europe to promote EVPTs. Suggestions for evaluating and regulating DTC marketing of EVPTs are included. Finally, the potential for DTC marketing of EVPTs to increase mental health literacy and reduce health disparities is explored.

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease

NASA Astrophysics Data System (ADS)

Babaoğlu, Ismail; Baykan, Ömer Kaan; Aygül, Nazif; Özdemir, Kurtuluş; Bayrak, Mehmet

The aim of this study is to show a comparison of multi-layered perceptron neural network (MLPNN) and support vector machine (SVM) on determination of coronary artery disease existence upon exercise stress testing (EST) data. EST and coronary angiography were performed on 480 patients with acquiring 23 verifying features from each. The robustness of the proposed methods is examined using classification accuracy, k-fold cross-validation method and Cohen's kappa coefficient. The obtained classification accuracies are approximately 78% and 79% for MLPNN and SVM respectively. Both MLPNN and SVM methods are rather satisfactory than human-based method looking to Cohen's kappa coefficients. Besides, SVM is slightly better than MLPNN when looking to the diagnostic accuracy, average of sensitivity and specificity, and also Cohen's kappa coefficient.

An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets.

PubMed

Shi, Yingzhong; Chung, Fu-Lai; Wang, Shitong

2015-09-01

Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Disseminated Tumor Cells in Prostate Cancer Patients after Radical Prostatectomy and without Evidence of Disease Predicts Biochemical Recurrence

PubMed Central

Morgan, Todd M.; Lange, Paul H.; Porter, Michael P.; Lin, Daniel W.; Ellis, William J.; Gallaher, Ian S.; Vessella, Robert L.

2011-01-01

Purpose Men with apparently localized prostate cancer often relapse years after radical prostatectomy (RP). We sought to determine if epithelial-like cells identified from bone marrow (BM) in patients after RP (commonly called disseminated tumor cells, DTC) were associated with biochemical recurrence (BR). Experimental Design We obtained BM aspirates from 569 men prior to RP and from 34 healthy men with PSA<2.5 ng/ml to establish a comparison group. Additionally, an analytic cohort consisting of 98 patients after RP with no evidence of disease (NED) was established to evaluate the relationship between DTC and BR. Epithelial cells in the BM were detected by magnetic bead enrichment with antibodies to CD45 and CD61 (negative selection) followed by antibodies to human epithelial antigen (positive selection) and confirmation with FITC-labeled anti-BerEP4 antibody. Results DTC were present in 72% (408/569) of patients prior to RP. There was no correlation with pathologic stage, Gleason grade, or pre-operative PSA. Three of 34 controls (8.8%) had DTC present. In patients NED post-RP, DTC were present in 56/98 (57%). DTC were detected in 12/14 (86%) NED patients post-RP who subsequently suffered BR. Presence of DTC in NED patients was an independent predictor of recurrence (HR 6.9, CI 1.03–45.9). Conclusions Approximately 70% of men undergoing RP had DTC detected in their BM prior to surgery, suggesting that these cells escape early in the disease. Though pre-operative DTC status does not correlate with pathologic risk factors, persistence of DTC after RP in NED patients was an independent predictor of recurrence. PMID:19147774

Attitudes about regulation among direct-to-consumer genetic testing customers.

PubMed

Bollinger, Juli Murphy; Green, Robert C; Kaufman, David

2013-05-01

The first regulatory rulings by the U.S. Food and Drug Administration on direct-to-consumer (DTC) genetic testing services are expected soon. As the process of regulating these and other genetic tests moves ahead, it is important to understand the preferences of DTC genetic testing customers about the regulation of these products. An online survey of customers of three DTC genetic testing companies was conducted 2-8 months after they had received their results. Participants were asked about the importance of regulating the companies selling DTC genetic tests. Most of the 1,046 respondents responded that it would be important to have a nongovernmental (84%) or governmental agency (73%) monitor DTC companies' claims to ensure the consistency with scientific evidence. However, 66% also felt that it was important that DTC tests be available without governmental oversight. Nearly, all customers favored a policy to ensure that insurers and law enforcement officials could not access their information. Although many DTC customers want access to genetic testing services without restrictions imposed by the government regulation, most also favor an organization operating alongside DTC companies that will ensure that the claims made by the companies are consistent with sound scientific evidence. This seeming contradiction may indicate that DTC customers want to ensure that they have unfettered access to high-quality information. Additionally, policies to help ensure privacy of data would be welcomed by customers, despite relatively high confidence in the companies.

Lex-SVM: exploring the potential of exon expression profiling for disease classification.

PubMed

Yuan, Xiongying; Zhao, Yi; Liu, Changning; Bu, Dongbo

2011-04-01

Exon expression profiling technologies, including exon arrays and RNA-Seq, measure the abundance of every exon in a gene. Compared with gene expression profiling technologies like 3' array, exon expression profiling technologies could detect alterations in both transcription and alternative splicing, therefore they are expected to be more sensitive in diagnosis. However, exon expression profiling also brings higher dimension, more redundancy, and significant correlation among features. Ignoring the correlation structure among exons of a gene, a popular classification method like L1-SVM selects exons individually from each gene and thus is vulnerable to noise. To overcome this limitation, we present in this paper a new variant of SVM named Lex-SVM to incorporate correlation structure among exons and known splicing patterns to promote classification performance. Specifically, we construct a new norm, ex-norm, including our prior knowledge on exon correlation structure to regularize the coefficients of a linear SVM. Lex-SVM can be solved efficiently using standard linear programming techniques. The advantage of Lex-SVM is that it can select features group-wisely, force features in a subgroup to take equal weihts and exclude the features that contradict the majority in the subgroup. Experimental results suggest that on exon expression profile, Lex-SVM is more accurate than existing methods. Lex-SVM also generates a more compact model and selects genes more consistently in cross-validation. Unlike L1-SVM selecting only one exon in a gene, Lex-SVM assigns equal weights to as many exons in a gene as possible, lending itself easier for further interpretation.

Robust LS-SVM-based adaptive constrained control for a class of uncertain nonlinear systems with time-varying predefined performance

NASA Astrophysics Data System (ADS)

Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yuan, Jianping

2018-03-01

This paper focuses on robust adaptive control for a class of uncertain nonlinear systems subject to input saturation and external disturbance with guaranteed predefined tracking performance. To reduce the limitations of classical predefined performance control method in the presence of unknown initial tracking errors, a novel predefined performance function with time-varying design parameters is first proposed. Then, aiming at reducing the complexity of nonlinear approximations, only two least-square-support-vector-machine-based (LS-SVM-based) approximators with two design parameters are required through norm form transformation of the original system. Further, a novel LS-SVM-based adaptive constrained control scheme is developed under the time-vary predefined performance using backstepping technique. Wherein, to avoid the tedious analysis and repeated differentiations of virtual control laws in the backstepping technique, a simple and robust finite-time-convergent differentiator is devised to only extract its first-order derivative at each step in the presence of external disturbance. In this sense, the inherent demerit of backstepping technique-;explosion of terms; brought by the recursive virtual controller design is conquered. Moreover, an auxiliary system is designed to compensate the control saturation. Finally, three groups of numerical simulations are employed to validate the effectiveness of the newly developed differentiator and the proposed adaptive constrained control scheme.

Awareness and uptake of direct-to-consumer genetic testing among cancer cases, their relatives, and controls: the Northwest Cancer Genetics Network.

PubMed

Hall, Taryn O; Renz, Anne D; Snapinn, Katherine W; Bowen, Deborah J; Edwards, Karen L

2012-07-01

To determine if awareness of, interest in, and use of direct-to-consumer (DTC) genetic testing is greater in a sample of high-risk individuals (cancer cases and their relatives), compared to controls. Participants were recruited from the Northwest Cancer Genetics Network. A follow-up survey was mailed to participants to assess DTC genetic testing awareness, interest, and use. One thousand two hundred sixty-seven participants responded to the survey. Forty-nine percent of respondents were aware of DTC genetic testing. Of those aware, 19% indicated interest in obtaining and <1% reported having used DTC genetic testing. Additional information supplied by respondents who reported use of DTC genetic tests indicated that 55% of these respondents likely engaged in clinical genetic testing, rather than DTC genetic testing. Awareness of DTC genetic testing was greater in our sample of high-risk individuals than in controls and population-based studies. Although interest in and use of these tests among cases in our sample were equivalent to other population-based studies, interest in testing was higher among relatives and people who self-referred for a registry focused on cancer than among cases and controls. Additionally, our results suggest that there may be some confusion about what constitutes DTC genetic testing.

Thyroid lobe ablation with iodine- ¹³¹I in patients with differentiated thyroid carcinoma: a randomized comparison between 1.1 and 3.7 GBq activities.

PubMed

Giovanella, Luca; Piccardo, Arnoldo; Paone, Gaetano; Foppiani, Luca; Treglia, Giorgio; Ceriani, Luca

2013-08-01

The present study was undertaken to evaluate the ablation rate after administration of 1.1 or 3.7 GBq of iodine- (¹³¹I) to patients with low-risk differentiated thyroid carcinoma (DTC) primarily treated by lobectomy. Enrolled were 136 consecutive patients affected by histologically proven low-risk DTC previously treated by lobectomy. Patients were randomized to receive a single dose of 1.1 or 3.7 GBq of ¹³¹I in an equivalence trial. Successful thyroid ablation was defined as a negative diagnostic whole-body scan and stimulated thyroglobulin levels lower than 2 ng/ml in the absence of thyroglobulin antibodies. The patient demographic and clinical data were well balanced at baseline. The ablation rate was significantly (P<0.01) higher in patients treated with 3.7 GBq (75%) than in those treated with 1.1 GBq (54%) of radioiodine. No relevant side effects occurred in either group. Radioiodine lobe ablation with a single administration of 3.7 GBq is a simple and safe mode of treatment, achieving an ablation rate higher than that of 1.1 GBq. This procedure may be offered as an alternative to completion thyroidectomy in highly selected DTC patients who had experienced complications during initial surgery or for whom completion thyroidectomy is contraindicated.

Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine.

PubMed

Zhou, Jing; Wu, Xiao-ming; Zeng, Wei-jie

2015-12-01

Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30 min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30 s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p < 0.001). SVM was used and obtained high and consistent recognition rate: average classification accuracy reached 95.1% corresponding to the sensitivity 93.2% and specificity 98.6%. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea.

Silver-gelatine bionanocomposites for qualitative detection of a pesticide by SERS.

PubMed

Fateixa, S; Soares, S F; Daniel-da-Silva, A L; Nogueira, H I S; Trindade, T

2015-03-07

The controlled release of pesticides using hydrogel vehicles is an important procedure to limit the amount of these compounds in the environment, providing an effective way for crop protection. A key-step in the formulation of new materials for these purposes encompasses the monitoring of available pesticides in the gel matrix under variable working conditions. In this work, we report a series of bionanocomposites made of Ag nanoparticles (NPs) and gelatine A for the surface enhanced Raman scattering (SERS) detection of sodium diethyldithiocarbamate (EtDTC) as a pesticide model. These studies demonstrate the effectiveness of these substrates for the detection of EtDTC in aqueous solutions in a concentration as low as 10(-5) M. We have monitored the Raman signal enhancement of this analyte in bionanocomposites having an increasing amount of gelatine due to their relevance in formulating hydrogels of variable gel strengths. Under these conditions, the bionanocomposites have shown an effective SERS activity using EtDTC, demonstrating their effectiveness in the qualitative detection of this analyte. Finally, experiments involving the release of EtDTC from Ag/gelatine samples have been monitored by SERS, which attest the potential of this spectroscopic method in the laboratorial monitoring of hydrogels for pesticide release.

Optimal structural design of the midship of a VLCC based on the strategy integrating SVM and GA

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Deyu

2012-03-01

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine (SVM) and genetic algorithm (GA), was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR), which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

[Study on application of SVM in prediction of coronary heart disease].

PubMed

Zhu, Yue; Wu, Jianghua; Fang, Ying

2013-12-01

Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

An improved conjugate gradient scheme to the solution of least squares SVM.

PubMed

Chu, Wei; Ong, Chong Jin; Keerthi, S Sathiya

2005-03-01

The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.

Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

PubMed

Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

2015-01-01

Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

Critical Time Crystals in Dipolar Systems

NASA Astrophysics Data System (ADS)

Ho, Wen Wei; Choi, Soonwon; Lukin, Mikhail D.; Abanin, Dmitry A.

2017-07-01

We analyze the quantum dynamics of periodically driven, disordered systems in the presence of long-range interactions. Focusing on the stability of discrete time crystalline (DTC) order in such systems, we use a perturbative procedure to evaluate its lifetime. For 3D systems with dipolar interactions, we show that the corresponding decay is parametrically slow, implying that robust, long-lived DTC order can be obtained. We further predict a sharp crossover from the stable DTC regime into a regime where DTC order is lost, reminiscent of a phase transition. These results are in good agreement with the recent experiments utilizing a dense, dipolar spin ensemble in diamond [Nature (London) 543, 221 (2017), 10.1038/nature21426]. They demonstrate the existence of a novel, critical DTC regime that is stabilized not by many-body localization but rather by slow, critical dynamics. Our analysis shows that the DTC response can be used as a sensitive probe of nonequilibrium quantum matter.

What nurse practitioners should know about direct-to-consumer advertising of prescription medications.

PubMed

Viale, Pamela Hallquist

2003-07-01

To describe the marketing strategies of direct-to-consumer (DTC) advertising and the risks, benefits, and potential influence on the prescribing practices of nurse practitioners (NPs). Journal articles, media sources, and clinical experience. The effect of DTC advertising of prescription medications on NPs has not been well studied. Although there are studies that examine the effects of DTC advertising on physician prescribing as well as the effects of this practice on the consumer, opinions on the benefits of DTC advertising are varied. NPs need to recognize the potential influence of DTC advertising and to be prepared to guide patients toward appropriate medication choices by participating in a partnership with patients. Health care providers, including NPs, need to work with the pharmaceutical industry to encourage accountability of DTC advertising, thus improving dissemination of correct information and promoting positive outcomes for health consumers and patients.

A comparison of the International Classification of Functioning, Disability, and Health to the disability tax credit.

PubMed

Conti-Becker, Angela; Doralp, Samantha; Fayed, Nora; Kean, Crystal; Lencucha, Raphael; Leyshon, Rhysa; Mersich, Jackie; Robbins, Shawn; Doyle, Phillip C

2007-01-01

The Disability Tax Credit (DTC) Certification is an assessment tool used to provide Canadians with disability tax relief The International Classification of Functioning, Disability and Health (ICF) provides a universal framework for defining disability. The purpose of this study was to evaluate the DTC and familiarize occupational therapists with the process of mapping measures to the ICF classification system. Concepts within the DTC were identified and mapped to appropriate ICF codes (Cieza et al., 2005). The DTC was linked to 45 unique ICF codes (16 Body Functions, 19 Activities and Participation, and 8 Environmental Factors). The DTC encompasses various domains of the ICF; however, there is no consideration of Personal Factors, Body Structures, and key aspects of Activities and Participation. Refining the DTC to address these aspects will provide an opportunity for fair and just determinations for those who experience disability.

Radiation-Induced Differentiated Thyroid Cancer Is Associated with Improved Overall Survival but Not Thyroid Cancer-Specific Mortality or Disease-Free Survival.

PubMed

White, Michael G; Cipriani, Nicole A; Abdulrasool, Layth; Kaplan, Sharone; Aschebrook-Kilfoy, Briseis; Angelos, Peter; Kaplan, Edwin L; Grogan, Raymon H; Onel, Kenan

2016-08-01

Radiation is a well-described risk factor for differentiated thyroid carcinoma (DTC). Although the natural history of DTC following nuclear disasters and in healthcare workers with chronic radiation exposure (RE) has been described, little is known about DTC following short-term exposure to therapeutic medical radiation for benign disease. This study compares DTC morphology and outcomes in patients with and without a prior history of therapeutic external RE. A retrospective review was performed of patients with DTC treated at The University of Chicago between 1951 and 1987, with a median follow-up of 27 years (range 0.3-60 years). Patients were classified as either having (RE+) or not having (RE-) a history of therapeutic RE. Variables examined included sex, age at RE, dose of RE, indication for RE, DTC histology, and outcome. Morphology was determined by blinded retrospective review of all available histologic slides. Outcomes were assessed using Cox proportional hazards model and Kaplan-Meier curves. Of 257 DTC patients, 165 (64%) were RE- and 92 (36%) were RE+, with males comprising a greater proportion of the RE+ group (43.5% vs. 27.3%; p = 0.01). A total of 94.2% of DTC cases were classic papillary cancers; histology did not differ between RE+ and RE- cohorts (p = 0.73). RE was associated with an increased median overall survival (OS; 43 years vs. 38 years; hazard ratio [HR] = 0.55 [confidence interval (CI) 0.34-0.89]; p = 0.01). Survival for males in the RE- group was significantly worse than it was for RE- females (HR = 1.78 [CI 1.05-3.03]; p = 0.03) or RE+ males (HR = 2.98 [CI 1.39-6.38]; p = 0.01). Recurrence did not differ between the RE+ and RE- groups (HR = 0.85 [CI 0.52-1.41]; p = 0.54), nor did DTC-specific mortality (HR = 0.54 [CI 0.21-1.37]; p = 0.20). While DTC following RE has historically been considered a more aggressive variant than DTC in the absence of RE, the present data indicate that RE+ DTC is associated with better OS than RE- DTC, especially for males. Additionally, recent reports are confirmed of equivalent rates of thyroid cancer recurrence. These results warrant further investigation into the factors underlying this unexpected finding.

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

PubMed

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

PubMed Central

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

Efficient HIK SVM learning for image classification.

PubMed

Wu, Jianxin

2012-10-01

Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

PubMed Central

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. PMID:26089862

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

PubMed

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Extended robust support vector machine based on financial risk minimization.

PubMed

Takeda, Akiko; Fujiwara, Shuhei; Kanamori, Takafumi

2014-11-01

Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Semi-supervised SVM for individual tree crown species classification

NASA Astrophysics Data System (ADS)

Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

2015-12-01

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

Direct-to-consumer (DTC) antidepressant advertising and consumer misperceptions about the chemical imbalance theory of depression: the moderating role of skepticism.

PubMed

Park, Jin Seong; Ahn, Ho-Young Anthony

2013-01-01

Based on a survey with members of an online consumer panel (N= 699), this study revealed that: (a) a substantial percentage of consumers held misperceptions about the chemical imbalance theory of depression; (b) personal and interpersonal experiences with depression positively related to such misperceptions; (c) overall, exposure to direct-to-consumer (DTC) antidepressant advertising did not significantly relate to misperceptions; and (d) DTC exposure magnified misperceptions when consumers were highly trustful of DTC advertising, whereas exposure diluted misperceptions when consumers were highly skeptical. Theoretical and practical implications of the research are discussed, especially in light of the social responsibility of DTC advertising.

Pharmacy Students' Knowledge, Attitudes, and Evaluation of Direct-to-Consumer Advertising

PubMed Central

Borrego, Matthew E.; Gupchup, Gireesh V.; Dodd, Melanie; Sather, Mike R.

2007-01-01

Objectives To assess pharmacy students' knowledge, attitudes, and evaluation of direct-to-consumer advertising (DTCA). Methods A cross sectional, self-administered, 106-item survey instrument was used to assess first, second, and third professional year pharmacy students' knowledge about DTCA regulations, attitudes toward DTCA, and evaluation of DTC advertisements with different brief summary formats (professional labeling and patient labeling) and in different media sources (print and television). Results One hundred twenty (51.3%) of the 234 students enrolled participated in the study. The mean percentage knowledge score was 48.7% ± 12.5%. Based on the mean scores per item, pharmacy students had an overall negative attitude toward DTC advertisements. Students had an overall negative attitude toward television and print advertisements using the professional labeling format but an overall positive attitude toward the print advertisement using the patient labeling format. Conclusions Lectures discussing DTC advertising should be included in the pharmacy curriculum. PMID:17998983

The attitudes and beliefs of oncology nurse practitioners regarding direct-to-consumer advertising of prescription medications.

PubMed

Viale, Pamela Hallquist; Sanchez Yamamoto, Deanna

2004-07-01

To obtain information about the knowledge and attitudes of oncology nurse practitioners (ONPs) concerning the effect of direct-to-consumer (DTC) advertising of prescription medications on prescribing patterns. Exploratory survey. Oncology Nursing Society Nurse Practitioner Special Interest Group members in the United States. 221 of 376 ONPs completed the survey (58%). Researcher-developed 12-question postal survey. Knowledge and attitudes of ONPs on DTC advertising effects on prescribing patterns. The findings were similar to those of previous studies of physicians regarding the number of visits when patients requested DTC-advertised medications. Major differences were the positive attitudes of ONPs toward potentially longer patient visits to explain and educate patients regarding medication requests based on DTC advertising and smaller percentages of ONPs who felt "pressured" to prescribe requested medications. ONPs have mixed opinions regarding the practice of DTC advertising but do not believe that they are influenced heavily by advertising with regard to prescriptive practices. ONPs consider patient encounters for education purposes as appropriate and include information about requested DTC-advertised medications in their approach to patient care. This is an exploratory survey of a specialty group of ONPs. More research is needed to further explore the practice of DTC advertising and potential influences on the prescribing patterns of ONPs. DTC advertising of prescription medications is increasing; ONPs need to increase their knowledge base about the potential for influences of prescriptive practices.

Direct torque control method applied to the WECS based on the PMSG and controlled with backstepping approach

NASA Astrophysics Data System (ADS)

Errami, Youssef; Obbadi, Abdellatif; Sahnoun, Smail; Ouassaid, Mohammed; Maaroufi, Mohamed

2018-05-01

This paper proposes a Direct Torque Control (DTC) method for Wind Power System (WPS) based Permanent Magnet Synchronous Generator (PMSG) and Backstepping approach. In this work, generator side and grid-side converter with filter are used as the interface between the wind turbine and grid. Backstepping approach demonstrates great performance in complicated nonlinear systems control such as WPS. So, the control method combines the DTC to achieve Maximum Power Point Tracking (MPPT) and Backstepping approach to sustain the DC-bus voltage and to regulate the grid-side power factor. In addition, control strategy is developed in the sense of Lyapunov stability theorem for the WPS. Simulation results using MATLAB/Simulink validate the effectiveness of the proposed controllers.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

PubMed

Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

2018-03-27

P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

PubMed

Wu, Jiang; Ji, Yanju; Zhao, Ling; Ji, Mengying; Ye, Zhuang; Li, Suyi

2016-01-01

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

PubMed

Mei, Suyu; Zhu, Hao

2015-01-26

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

Prescription drug advertising: is it a driving force on drug pricing?

PubMed

Millstein, Lloyd G

2003-01-01

It has been shown that drug companies will sell more drugs when they use DTC advertising, but it is also true that many consumers who are suffering--unaware there is help for their symptoms--will learn from these ads that help is available. Advertising to consumers, like advertising to professionals, will continue to be one of the best methods of providing information. Of course, healthcare professionals also have the sales representatives, their colleagues, medical journals, and medical conventions as additional options for needed information. The consumer may or may not use other methods, such as the Internet, the library or friends or family, but the advertising is a starting point for a dialogue. If the DTC ad provides consumers with "information," which is different from "advertising," the drug company will be providing a worthwhile service to consumers and potential patients. No doubt consumers will begin demanding higher quality information from DTC ads and will frown upon the ads that are blatantly trying just to sell a drug. It will also reap the benefits of improved consumer awareness and patient compliance. A DTC ad that is consumer-friendly, does not use fear appeal, is educational in tone, and downplays the "hard sell" and hype will go a long way in offering important information to the casual observer. Oversight by the FDA will ensure the information meets the requirements they have set down for prescription drug advertising. That is, advertising will be truthful and fairly balanced and will meet what the government, consumers and, no doubt, the medical community wants. Attempting to control drug costs, by controlling advertising, will not be an easy task. This has an implication across all product areas, not just drugs. DTC advertising has become a lightening rod for cost containment issues, but is it alone driving demand for prescription products? I don't think so.

An Enhanced Three-Level Voltage Switching State Scheme for Direct Torque Controlled Open End Winding Induction Motor

NASA Astrophysics Data System (ADS)

Kunisetti, V. Praveen Kumar; Thippiripati, Vinay Kumar

2018-01-01

Open End Winding Induction Motors (OEWIM) are popular for electric vehicles, ship propulsion applications due to less DC link voltage. Electric vehicles, ship propulsions require ripple free torque. In this article, an enhanced three-level voltage switching state scheme for direct torque controlled OEWIM drive is implemented to reduce torque and flux ripples. The limitations of conventional Direct Torque Control (DTC) are: possible problems during low speeds and starting, it operates with variable switching frequency due to hysteresis controllers and produces higher torque and flux ripple. The proposed DTC scheme can abate the problems of conventional DTC with an enhanced voltage switching state scheme. The three-level inversion was obtained by operating inverters with equal DC-link voltages and it produces 18 voltage space vectors. These 18 vectors are divided into low and high frequencies of operation based on rotor speed. The hardware results prove the validity of proposed DTC scheme during steady-state and transients. From simulation and experimental results, proposed DTC scheme gives less torque and flux ripples on comparison to two-level DTC. The proposed DTC is implemented using dSPACE DS-1104 control board interface with MATLAB/SIMULINK-RTI model.

Assessment of Direct-to-Consumer Genetic Testing Policy in Korea Based on Consumer Preference.

PubMed

Jeong, Gicheol

2017-01-01

In June 2016, Korea permitted direct-to-consumer genetic testing (DTC-GT) on 42 genes. However, both the market and industry have not yet been fully activated. Considering the aforementioned context, this study provides important insights. The Korean DTC-GT policy assessment is based on consumer preference analysis using a discrete choice experiment. In August 2016, a web-based survey was conducted to collect data from 1,200 respondents. The estimation results show that consumers prefer a DTC-GT product that is cheap, tests various items or genes, offers accurate test results, and guarantees the confidentiality of all information. However, consumers are not entirely satisfied by current DTC-GT products due to the existence of insufficient and/or inadequate policies. First, the permitted testing of 42 genes is insufficient to satisfy consumers' curiosity regarding their genes. Second, the accuracy of the DTC-GT products has not been fully verified, assessed, and communicated to consumers. Finally, regulatory loopholes that allow information leaks in the DTC-GT process can occur. These findings imply that DTC-GT requires an improvement in government policy-making criteria and the implementation of practical measures to guarantee test accuracy and genetic information. © 2017 S. Karger AG, Basel.

An Enhanced Three-Level Voltage Switching State Scheme for Direct Torque Controlled Open End Winding Induction Motor

NASA Astrophysics Data System (ADS)

Kunisetti, V. Praveen Kumar; Thippiripati, Vinay Kumar

2018-06-01

Open End Winding Induction Motors (OEWIM) are popular for electric vehicles, ship propulsion applications due to less DC link voltage. Electric vehicles, ship propulsions require ripple free torque. In this article, an enhanced three-level voltage switching state scheme for direct torque controlled OEWIM drive is implemented to reduce torque and flux ripples. The limitations of conventional Direct Torque Control (DTC) are: possible problems during low speeds and starting, it operates with variable switching frequency due to hysteresis controllers and produces higher torque and flux ripple. The proposed DTC scheme can abate the problems of conventional DTC with an enhanced voltage switching state scheme. The three-level inversion was obtained by operating inverters with equal DC-link voltages and it produces 18 voltage space vectors. These 18 vectors are divided into low and high frequencies of operation based on rotor speed. The hardware results prove the validity of proposed DTC scheme during steady-state and transients. From simulation and experimental results, proposed DTC scheme gives less torque and flux ripples on comparison to two-level DTC. The proposed DTC is implemented using dSPACE DS-1104 control board interface with MATLAB/SIMULINK-RTI model.

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

NASA Astrophysics Data System (ADS)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Health-care referrals from direct-to-consumer genetic testing.

PubMed

Giovanni, Monica A; Fickie, Matthew R; Lehmann, Lisa S; Green, Robert C; Meckley, Lisa M; Veenstra, David; Murray, Michael F

2010-12-01

direct-to-consumer genetic testing (DTC-GT) provides personalized genetic risk information directly to consumers. Little is known about how and why consumers then communicate the results of this testing to health-care professionals. to query specialists in clinical genetics about their experience with individuals who consulted them after DTC-GT. invitations to participate in a questionnaire were sent to three different groups of genetic professionals, totaling 4047 invitations, asking questions about individuals who consulted them after DTC-GT. For each case reported, respondents were asked to describe how the case was referred to them, the patient's rationale for DTC-GT, and the type of DTC-GT performed. Respondents were also queried about the consequences of the consultations in terms of additional testing ordered. The costs associated with each consultation were estimated. A clinical case series was compiled based upon clinician responses. the invitation resulted in 133 responses describing 22 cases of clinical interactions following DTC-GT. Most consultations (59.1%) were self-referred to genetics professionals, but 31.8% were physician referred. Among respondents, 52.3% deemed the DTC-GT to be "clinically useful." BRCA1/2 testing was considered clinically useful in 85.7% of cases; 35.7% of other tests were considered clinically useful. Subsequent referrals from genetics professionals to specialists and/or additional diagnostic testing were common, generating individual downstream costs estimated to range from $40 to $20,600. this clinical case series suggests that approximately half of clinical geneticists who saw patients after DTC-GT judged that testing was clinically useful, especially the BRCA1/2 testing. Further studies are needed in larger and more diverse populations to better understand the interactions between DTC-GT and the health-care system.

Radiofrequency ablation for postsurgical thyroid removal of differentiated thyroid carcinoma

PubMed Central

Xu, Dong; Wang, Lipin; Long, Bin; Ye, Xuemei; Ge, Minghua; Wang, Kejing; Guo, Liang; Li, Linfa

2016-01-01

Differentiated thyroid carcinoma (DTC) is the most common endocrine malignancy. Surgical removal with radioactive iodine therapy is recommended for recurrent thyroid carcinoma, and the postsurgical thyroid removal is critical. This study evaluated the clinical values of radiofrequency ablation (RFA) in the postsurgical thyroid removal for DTC. 35 DTC patients who had been treated by subtotal thyroidectomy received RFA for postsurgical thyroid removal. Before and two weeks after RFA, the thyroid was examined by ultrasonography and 99mTcO4 - thyroid imaging, and the serum levels of free triiodothyronine (FT3), free thyroxin (FT4), thyroid stimulating hormone (TSH) and thyroglobulin (Tg) were detected. The efficacy and complications of RFA were evaluated. Results showed that, the postsurgical thyroid removal by RFA was successfully performed in 35 patients, with no significant complication. After RFA, the average largest diameter and volume were significantly decreased in 35 patients (P > 0.05), and no obvious contrast media was observed in ablation area in the majority of patients. After RFA, the serum FT3, FT4 and Tg levels were markedly decreased (P < 0.05), and TSH level was significantly increased (P < 0.05). After RFA, radioiodine concentration in the ablation area was significantly reduced in the majority of patients. The reduction rate of thyroid update was 0.69±0.20%. DTC staging and interval between surgery and RFA had negative correlation (Pearson coefficient = -0.543; P = 0.001), with no obvious correlation among others influential factors. RFA is an effective and safe method for postsurgical thyroid removal of DTC. PMID:27186311

[Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

PubMed

Wu, Gui-Fang; He, Yong

2009-06-01

One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

31P NMR study of discrete time-crystalline signatures in an ordered crystal of ammonium dihydrogen phosphate

NASA Astrophysics Data System (ADS)

Rovny, Jared; Blum, Robert L.; Barrett, Sean E.

2018-05-01

The rich dynamics and phase structure of driven systems include the recently described phenomenon of the "discrete time crystal" (DTC), a robust phase which spontaneously breaks the discrete time translation symmetry of its driving Hamiltonian. Experiments in trapped ions and diamond nitrogen vacancy centers have recently shown evidence for this DTC order. Here, we show nuclear magnetic resonance (NMR) data of DTC behavior in a third, strikingly different, system: a highly ordered spatial crystal in three dimensions. We devise a DTC echo experiment to probe the coherence of the driven system. We examine potential decay mechanisms for the DTC oscillations, and demonstrate the important effect of the internal Hamiltonian during nonzero duration pulses.

a Comparison of Empirical and Inteligent Methods for Dust Detection Using Modis Satellite Data

NASA Astrophysics Data System (ADS)

Shahrisvand, M.; Akhoondzadeh, M.

2013-09-01

Nowadays, dust storm in one of the most important natural hazards which is considered as a national concern in scientific communities. This paper considers the capabilities of some classical and intelligent methods for dust detection from satellite imagery around the Middle East region. In the study of dust detection, MODIS images have been a good candidate due to their suitable spectral and temporal resolution. In this study, physical-based and intelligent methods including decision tree, ANN (Artificial Neural Network) and SVM (Support Vector Machine) have been applied to detect dust storms. Among the mentioned approaches, in this paper, SVM method has been implemented for the first time in domain of dust detection studies. Finally, AOD (Aerosol Optical Depth) images, which are one the referenced standard products of OMI (Ozone Monitoring Instrument) sensor, have been used to assess the accuracy of all the implemented methods. Since the SVM method can distinguish dust storm over lands and oceans simultaneously, therefore the accuracy of SVM method is achieved better than the other applied approaches. As a conclusion, this paper shows that SVM can be a powerful tool for production of dust images with remarkable accuracy in comparison with AOT (Aerosol Optical Thickness) product of NASA.

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

Deceleration time of left ventricular outflow tract flow as a simple surrogate marker for central haemodynamics at rest and as well as during exercise.

PubMed

Cho, In-Jeong; Shim, Chi Young; Moon, Sun-Ha; Lee, Hyun-Jin; Hong, Geu-Ru; Chung, Namsik; Ha, Jong-Won

2017-05-01

The shape and duration of left ventricular outflow tract (LVOT) flow has not been applied to assess the central haemodynamics, although LVOT flow is confronted with afterload of arterial system during systole. The aim of this study was to evaluate whether the LVOT flow parameters are related with central systolic blood pressure (BP) and arterial compliance at rest and as well as during exercise. We studied 258 subjects (175 females, age 61 ± 11 years) with normal left ventricular (LV) systolic function who underwent supine bicycle stress echocardiography and arterial tonometry simultaneously at rest and at peak exercise. Deceleration time (DT) of LVOT flow and RR interval were measured and deceleration time corrected for heart rate (DTc) was calculated. Peripheral and central haemodynamic parameters including systolic and diastolic BP, and augmentation index at a heart rate of 75 (AIx@75) were assessed using radial artery tonometry. Carotid femoral pulse wave velocity (PWV) was measured. Deceleration time corrected for heart rate was independently associated with central systolic BP and AIx@75 at rest (P < 0.001 and 0.006). Similarly, it also showed significant independent correlations with central systolic BP and AIx@75 during peak exercise (P = 0.006 and P = 0.021). In addition, DTc which measured both at rest and at peak exercise demonstrated significant positive correlations with PWV, suggesting association of prolonged DTc with arterial stiffening (P = 0.023 and P = 0.005). Prolongation of LVOT flow DTc represents raised central systolic BP and increased arterial stiffness not only at rest but also during exercise. Therefore, central aortic pressures and arterial stiffness influence the DT of LVOT flow at rest as well as during exercise in individuals with normal LV systolic function. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

Analysis of miRNA expression profile based on SVM algorithm

NASA Astrophysics Data System (ADS)

Ting-ting, Dai; Chang-ji, Shan; Yan-shou, Dong; Yi-duo, Bian

2018-05-01

Based on mirna expression spectrum data set, a new data mining algorithm - tSVM - KNN (t statistic with support vector machine - k nearest neighbor) is proposed. the idea of the algorithm is: firstly, the feature selection of the data set is carried out by the unified measurement method; Secondly, SVM - KNN algorithm, which combines support vector machine (SVM) and k - nearest neighbor (k - nearest neighbor) is used as classifier. Simulation results show that SVM - KNN algorithm has better classification ability than SVM and KNN alone. Tsvm - KNN algorithm only needs 5 mirnas to obtain 96.08 % classification accuracy in terms of the number of mirna " tags" and recognition accuracy. compared with similar algorithms, tsvm - KNN algorithm has obvious advantages.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

pDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine.

PubMed

Zhang, Shanxin; Zhou, Zhiping; Chen, Xinmeng; Hu, Yong; Yang, Lindong

2017-08-07

DNase I hypersensitive sites (DHSs) are accessible chromatin regions hypersensitive to cleavages by DNase I endonucleases. DHSs are indicative of cis-regulatory DNA elements (CREs), all of which play important roles in global gene expression regulation. It is helpful for discovering CREs by recognition of DHSs in genome. To accelerate the investigation, it is an important complement to develop cost-effective computational methods to identify DHSs. However, there is a lack of tools used for identifying DHSs in plant genome. Here we presented pDHS-SVM, a computational predictor to identify plant DHSs. To integrate the global sequence-order information and local DNA properties, reverse complement kmer and dinucleotide-based auto covariance of DNA sequences were applied to construct the feature space. In this work, fifteen physical-chemical properties of dinucleotides were used and Support Vector Machine (SVM) was employed. To further improve the performance of the predictor and extract an optimized subset of nucleotide physical-chemical properties positive for the DHSs, a heuristic nucleotide physical-chemical property selection algorithm was introduced. With the optimized subset of properties, experimental results of Arabidopsis thaliana and rice (Oryza sativa) showed that pDHS-SVM could achieve accuracies up to 87.00%, and 85.79%, respectively. The results indicated the effectiveness of proposed method for predicting DHSs. Furthermore, pDHS-SVM could provide a helpful complement for predicting CREs in plant genome. Our implementation of the novel proposed method pDHS-SVM is freely available as source code, at https://github.com/shanxinzhang/pDHS-SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

PubMed

Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

2013-03-01

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

A Daily Process Examination of Episode-Specific Drinking to Cope Motivation among College Students

PubMed Central

HOWLAND, MARYHOPE; TENNEN, HOWARD

2016-01-01

Objective Theory suggests that state- and trait-like factors should interact in predicting drinking to cope (DTC) motivation, yet no research to date has demonstrated this at the drinking episode level of analysis. Thus, we examined whether daily variation in positive and negative affect and avoidance and active coping were associated with DTC motivation during discrete drinking episodes and whether these associations were moderated by tension-reduction expectancies and other person-level risk factors. Methods Using a secure website, 722 college student drinkers completed a one-time survey regarding their tension reduction expectancies and then reported daily for 30 days on their affect, coping strategies, drinking behaviors and motives for drinking. Results Individuals reported higher levels of DTC motivation on days when negative affect and avoidance coping were high and positive affect was low. We found only little support for the predicted interactive effects among the day- and person-level predictors. Conclusion Our results support the state and trait conceptualizations of DTC motivation and provide evidence for the antecedent roles of proximal levels of daily affect and avoidance coping. Our inconsistent results for interaction effects including day-level antecedents raises the possibility that some of these synergistic processes might not generalize across level of analysis. PMID:26894551

A comparison of numerical and machine-learning modeling of soil water content with limited input data

NASA Astrophysics Data System (ADS)

Karandish, Fatemeh; Šimůnek, Jiří

2016-12-01

Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a better choice for predicting SWCs with lower uncertainties when required data are available, and thus for designing water saving strategies for agriculture and for other environmental applications requiring estimates of SWCs.

A linear-RBF multikernel SVM to classify big text corpora.

PubMed

Romero, R; Iglesias, E L; Borrajo, L

2015-01-01

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

PubMed Central

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

PubMed

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Plenary III-04: Media Messages and Public Perceptions of Direct-to-Consumer Genetics: Results of a Media Analysis and Focus Group Study

PubMed Central

Rahm, Alanna Kulchak; Dearing, James; Feigelson, Heather Spencer; Tracer, David; Bull, Sheana

2011-01-01

Background Information about genetics and the promise of genomic medicine is commonplace in the mass media. How the mass media themselves contribute – or not – to the persistence of this issue and its perception by the public is the topic of this presentation. The purpose of this study is to investigate the structural and individual perspectives on the issue of direct-to-consumer (DTC) genetics, and assess the degree of correspondence across these perspectives. Methods I conducted a media analysis to determine how the issue of DTC genetics has been framed in mass media stories and the salient topics related to the issue. I conducted focus groups to determine individual knowledge, attitudes and beliefs about the issue of DTC genetics. Results A final sample of 398 mass media stories of DTC genetics from Lexis-Nexis Academic archives between September 1, 2007 and September 30, 2009 were coded for salience and frames. Fourteen focus groups were conducted between October, 2009 and March, 2010 with Kaiser Permanente Colorado members and medical staff. Focus group transcripts were coded for salience and framing of the issue and compared with the media analysis results.Study results found that the issue of DTC genetics was not very important to focus group participants except as it related to the topic of breast cancer. Mass media message topics and frames showed differences over time. Focus group participants were generally negative towards the issue while the mass media was mostly positive towards DTC genetics. Focus group participants used some of the many frames to understand the issue that were utilized by the mass media to package the issue, but participants mainly framed the issue in terms of prevention and a pandora’s box, while the mass media presented the issue more in terms of progressive and discrimination frames. Conclusions The mass media appears to function as a field of power for the issue of DTC genetics with the consumers in the middle of the contests. A higher-level concept of an “informed consumer” emerged from the focus groups that appears to provide consumers a degree of power in this battlefield as well.

Hashimoto's Thyroiditis Pathology and Risk for Thyroid Cancer

PubMed Central

Paparodis, Rodis; Imam, Shahnawaz; Todorova-Koteva, Kristina; Staii, Anca

2014-01-01

Background: Hashimoto's thyroiditis (HT) has been found to coexist with differentiated thyroid cancer (DTC) in surgical specimens, but an association between the two conditions has been discounted by the medical literature. Therefore, we performed this study to determine any potential relationship between HT and the risk of developing DTC. Methods: We collected data for thyrotropin (TSH), thyroxine (T4), thyroid peroxidase antibody (TPO-Ab) titers, surgical pathology, and weight-based levothyroxine (LT4) replacement dose for patients who were referred for thyroid surgery. Patients with HT at final pathology were studied further. To estimate thyroid function, patients with preoperative hypothyroid HT (Hypo-HT) were divided into three equal groups based on their LT4 replacement: LT4-Low (<0.90 μg/kg), LT4-Mid (0.90–1.43 μg/kg), and LT4-High (>1.43 μg/kg). A group of preoperatively euthyroid (Euth-HT) patients but with HT by pathology was also studied. All subjects were also grouped based on their TPO-Ab titer in TPO-high (titer >1:1000) or TPO-low/negative (titer <1:1000 or undetectable) groups. The relationship of HT and DTC was studied extensively. Results: Of 2811 subjects, 582 had HT on surgical pathology, 365 of whom were Euth-HT preoperatively. DTC was present in 47.9% of the Euth-HT, in 59.7% of LT4-Low, 29.8% of LT4-Mid, and 27.9% of LT4-High groups. The relative risk (RR) for DTC was significantly elevated for the Euth-HT and LT4-Low groups (p<0.001), but not for the LT4-Mid or LT4-High replacement dose groups. TPO-low/negative status conferred an increased RR in the Euth-HT and LT4-Low replacement dose groups (p<0.001 both), while TPO-high status decreased it in Euth-HT group (p<0.05) and made it nonsignificant in the LT4-Low group. Conclusions: HT pathology increases the risk for DTC only in euthyroid subjects and those with partially functional thyroid glands (LT4-Low) but not in fully hypothyroid HT (LT4-Mid and LT4-High). High TPO-Ab titers appear to protect against DTC in patients with HT. PMID:24708347

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

PubMed Central

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies’ ratings. The proposed PRS not only considers the movie’s content information but also integrates the users’ demographic and behavioral information to better capture the users’ interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set. PMID:27898691

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization.

PubMed

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.

Combining SVM and flame radiation to forecast BOF end-point

NASA Astrophysics Data System (ADS)

Wen, Hongyuan; Zhao, Qi; Xu, Lingfei; Zhou, Munchun; Chen, Yanru

2009-05-01

Because of complex reactions in Basic Oxygen Furnace (BOF) for steelmaking, the main end-point control methods of steelmaking have insurmountable difficulties. Aiming at these problems, a support vector machine (SVM) method for forecasting the BOF steelmaking end-point is presented based on flame radiation information. The basis is that the furnace flame is the performance of the carbon oxygen reaction, because the carbon oxygen reaction is the major reaction in the steelmaking furnace. The system can acquire spectrum and image data quickly in the steelmaking adverse environment. The structure of SVM and the multilayer feed-ward neural network are similar, but SVM model could overcome the inherent defects of the latter. The model is trained and forecasted by using SVM and some appropriate variables of light and image characteristic information. The model training process follows the structure risk minimum (SRM) criterion and the design parameter can be adjusted automatically according to the sampled data in the training process. Experimental results indicate that the prediction precision of the SVM model and the executive time both meet the requirements of end-point judgment online.

Study on for soluble solids contents measurement of grape juice beverage based on Vis/NIRS and chemomtrics

NASA Astrophysics Data System (ADS)

Wu, Di; He, Yong

2007-11-01

The aim of this study is to investigate the potential of the visible and near infrared spectroscopy (Vis/NIRS) technique for non-destructive measurement of soluble solids contents (SSC) in grape juice beverage. 380 samples were studied in this paper. Smoothing way of Savitzky-Golay and standard normal variate were applied for the pre-processing of spectral data. Least-squares support vector machines (LS-SVM) with RBF kernel function was applied to developing the SSC prediction model based on the Vis/NIRS absorbance data. The determination coefficient for prediction (Rp2) of the results predicted by LS-SVM model was 0. 962 and root mean square error (RMSEP) was 0. 434137. It is concluded that Vis/NIRS technique can quantify the SSC of grape juice beverage fast and non-destructively.. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BP-NN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SSC of grape juice beverage. In this study, the generation ability of LS-SVM, PLS and BP-NN models were also investigated. It is concluded that LS-SVM regression method is a promising technique for chemometrics in quantitative prediction.

[A prediction model for the activity of insecticidal crystal proteins from Bacillus thuringiensis based on support vector machine].

PubMed

Lin, Yi; Cai, Fu-Ying; Zhang, Guang-Ya

2007-01-01

A quantitative structure-property relationship (QSPR) model in terms of amino acid composition and the activity of Bacillus thuringiensis insecticidal crystal proteins was established. Support vector machine (SVM) is a novel general machine-learning tool based on the structural risk minimization principle that exhibits good generalization when fault samples are few; it is especially suitable for classification, forecasting, and estimation in cases where small amounts of samples are involved such as fault diagnosis; however, some parameters of SVM are selected based on the experience of the operator, which has led to decreased efficiency of SVM in practical application. The uniform design (UD) method was applied to optimize the running parameters of SVM. It was found that the average accuracy rate approached 73% when the penalty factor was 0.01, the epsilon 0.2, the gamma 0.05, and the range 0.5. The results indicated that UD might be used an effective method to optimize the parameters of SVM and SVM and could be used as an alternative powerful modeling tool for QSPR studies of the activity of Bacillus thuringiensis (Bt) insecticidal crystal proteins. Therefore, a novel method for predicting the insecticidal activity of Bt insecticidal crystal proteins was proposed by the authors of this study.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Public Awareness of Direct-to-Consumer Genetic Tests: Findings from the 2013 U.S. Health Information National Trends Survey.

PubMed

Agurs-Collins, Tanya; Ferrer, Rebecca; Ottenbacher, Allison; Waters, Erika A; O'Connell, Mary E; Hamilton, Jada G

2015-12-01

Although the availability of direct-to-consumer (DTC) genetic testing has increased in recent years, the general public's awareness of this testing is not well understood. This study examined levels of public awareness of DTC genetic testing, sources of information about testing, and psychosocial factors associated with awareness of testing in the USA. Data were obtained from the nationally representative 2013 U.S. Health Information National Trends Survey. Guided by a social-cognitive conceptual framework, univariable and multivariable logistic regressions were conducted to identify factors associated with awareness of DTC genetic tests. Of 3185 participants, 35.6% were aware of DTC genetic tests, with the majority learning about these tests through radio, television, and the Internet. In the final adjusted model, participants with annual incomes of $99,999 or less had lower odds of being aware of DTC genetic testing (ORs ranging from 0.46-0.61) than did those participants with incomes of $100,000 or more. The odds of awareness of DTC genetic tests were significantly higher for those who actively seek cancer information (OR=1.91, 95% CI=1.36-2.69), use the Internet (OR=1.81, 95% CI=1.05-3.13), and have high numeracy skills (OR=1.67, 95% CI=1.17-2.38). It will be critical for healthcare researchers and practitioners to understand predictors and consequences of the public's awareness of DTC genetic tests, as well as how such awareness may translate into DTC genetic testing uptake, health behavior change, and ultimately disease prevention.

Determinants of hypofibrinolysis in patients with digestive tract cancer.

PubMed

Gronostaj, Katarzyna; Richter, Piotr; Nowak, Wojciech; Undas, Anetta

2016-01-01

Recently, we demonstrated that digestive tract cancer (DTC) is associated with reduced fibrin clot permeability and impaired fibrinolysis. We investigated determinants of fibrinolysis in DTC patients. In 44 consecutive patients with DTC and 47 controls matched for age, sex, and cardiovascular risk, we evaluated fibrinolysis proteins, platelet activation markers, thrombin formation, together with plasma clot lysis time assays in the absence (CLT) and presence of carboxypeptidase potato inhibitor (CLT CPI) that blocks thrombin activatable fibrinolysis inhibitor (TAFI). In the DTC group CLT (by 22.3%) and CLT CPI (by 27.4%) were longer compared with controls. The DTC patients had higher plasma fibrinolysis inhibitors, plasminogen activator inhibitor 1 (PAI-1) (by 18.2%), TAFI activity (by 17.3%), and antigen (by 11.2%). The patients had markedly increased platelet markers - soluble CD40 ligand (by 338%) and P-selectin (by 97%), together with von Willebrand factor (vWF) antigen (by 61%). Thrombin-antithrombin complexes (TAT) (by 48.7%) and soluble thrombomodulin (sTM) (by 17.2%) were also increased in the DTC group (all p < 0.05). Patients with high-grade tumours (n = 26) compared with remainders (n = 18) had longer CLT, higher tissue-type plasminogen activator antigen, both TAFI antigen and activity levels, vWF, and sTM. Multiple regression analysis after adjustment for potential confounders showed that independent predictors of CLT in DTC patients were TAT, TAFI activity, and vWF. The only independent predictor of CLT CPI was TAT. Hypofibrinolysis in DTC patients is largely driven by enhanced thrombin generation, TAFI, and endothelial injury.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-06-28

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is superior by almost an order of magnitude over the ANN method in terms of the stability, generality, and robustness of the final model. The LS-SVM model needs a much smaller numbers of samples (a much smaller sample set) to make accurate prediction results. Potential energy surface (PES) approximations for molecular dynamics (MD) studies are discussed as a promising application for the LS-SVM calibration approach. This journal is © the Owner Societies 2011

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries.

PubMed

Kumar, Pankaj; Ma, Xiaohua; Liu, Xianghui; Jia, Jia; Bucong, Han; Xue, Ying; Li, Ze Rong; Yang, Sheng Yong; Wei, Yu Quan; Chen, Yu Zong

2011-05-01

Various in vitro and in-silico methods have been used for drug genotoxicity tests, which show limited genotoxicity (GT+) and non-genotoxicity (GT-) identification rates. New methods and combinatorial approaches have been explored for enhanced collective identification capability. The rates of in-silco methods may be further improved by significantly diversified training data enriched by the large number of recently reported GT+ and GT- compounds, but a major concern is the increased noise levels arising from high false-positive rates of in vitro data. In this work, we evaluated the effect of training data size and noise level on the performance of support vector machines (SVM) method known to tolerate high noise levels in training data. Two SVMs of different diversity/noise levels were developed and tested. H-SVM trained by higher diversity higher noise data (GT+ in any in vivo or in vitro test) outperforms L-SVM trained by lower noise lower diversity data (GT+ in in vivo or Ames test only). H-SVM trained by 4,763 GT+ compounds reported before 2008 and 8,232 GT- compounds excluding clinical trial drugs correctly identified 81.6% of the 38 GT+ compounds reported since 2008, predicted 83.1% of the 2,008 clinical trial drugs as GT-, and 23.96% of 168 K MDDR and 27.23% of 17.86M PubChem compounds as GT+. These are comparable to the 43.1-51.9% GT+ and 75-93% GT- rates of existing in-silico methods, 58.8% GT+ and 79% GT- rates of Ames method, and the estimated percentages of 23% in vivo and 31-33% in vitro GT+ compounds in the "universe of chemicals". There is a substantial level of agreement between H-SVM and L-SVM predicted GT+ and GT- MDDR compounds and the prediction from TOPKAT. SVM showed good potential in identifying GT+ compounds from large compound libraries based on higher diversity and higher noise training data.

New Molecular Design Concurrently Providing Superior Pure Blue, Thermally Activated Delayed Fluorescence and Optical Out-Coupling Efficiencies.

PubMed

Rajamalli, P; Senthilkumar, N; Huang, P-Y; Ren-Wu, C-C; Lin, H-W; Cheng, C-H

2017-08-16

Simultaneous enhancement of out-coupling efficiency, internal quantum efficiency, and color purity in thermally activated delayed fluorescence (TADF) emitters is highly desired for the practical application of these materials. We designed and synthesized two isomeric TADF emitters, 2DPyM-mDTC and 3DPyM-pDTC, based on di(pyridinyl)methanone (DPyM) cores as the new electron-accepting units and di(tert-butyl)carbazole (DTC) as the electron-donating units. 3DPyM-pDTC, which is structurally nearly planar with a very small ΔE ST , shows higher color purity, horizontal ratio, and quantum yield than 2DPyM-mDTC, which has a more flexible structure. An electroluminescence device based on 3DPyM-pDTC as the dopant emitter can reach an extremely high external quantum efficiency of 31.9% with a pure blue emission. This work also demonstrates a way to design materials with a high portion of horizontal molecular orientation to realize a highly efficient pure-blue device based on TADF emitters.

Episode-specific drinking-to-cope motivation and next-day stress-reactivity.

PubMed

Armeli, Stephen; O'Hara, Ross E; Covault, Jon; Scott, Denise M; Tennen, Howard

2016-11-01

Research consistently shows drinking-to-cope (DTC) motivation is uniquely associated with drinking-related problems. We furthered this line of research by examining whether DTC motivation is predictive of processes indicative of poor emotion regulation. Specifically, we tested whether nighttime levels of episode-specific DTC motivation, controlling for drinking level, were associated with intensified affective reactions to stress the following day (i.e. stress-reactivity). We used a micro-longitudinal design to test this hypothesis in two college student samples from demographically distinct institutions: a large, rural state university (N = 1421; 54% female) and an urban historically Black college/university (N = 452; 59% female). In both samples the within-person association between daily stress and negative affect on days following drinking episodes was stronger in the positive direction when previous night's drinking was characterized by relatively higher levels of DTC motivation. We also found evidence among students at the state university that average levels of DTC motivation moderated the daily stress-negative affect association. Findings are consistent with the notion that DTC motivation confers a unique vulnerability that affects processes associated with emotion regulation.

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

PubMed

Alejo, Luz; Atkinson, John; Guzmán-Fierro, Víctor; Roeckel, Marlene

2018-05-16

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.

PubMed

Sahran, Shahnorbanun; Albashish, Dheeb; Abdullah, Azizi; Shukor, Nordashima Abd; Hayati Md Pauzi, Suria

2018-04-18

Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components. We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC. We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods. We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods. Copyright © 2018 Elsevier B.V. All rights reserved.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

PubMed

Liu, Zhi; Xu, Shuqiong; Zhang, Yun; Chen, Chun Lung Philip

2014-11-01

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Zhang, Yanjiao; Xu, Junfa; Li, Linfang; Zeng, Qiuyao; Lin, Lin; Guo, Zhouyi; Liu, Zhiming; Xiong, Honglian; Liu, Songhao

2014-09-01

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mapping membrane activity in undiscovered peptide sequence space using machine learning

PubMed Central

Fulan, Benjamin M.; Wong, Gerard C. L.

2016-01-01

There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal candidates that simultaneously maximize the distance σ from the SVM hyperplane (thus maximize its “antimicrobialness”) and its ⍺-helicity, but minimize mutational distance to known AMPs. By calibrating SVM machine learning results with killing assays and small-angle X-ray scattering (SAXS), we find that the SVM metric σ correlates not with a peptide’s minimum inhibitory concentration (MIC), but rather its ability to generate negative Gaussian membrane curvature. This surprising result provides a topological basis for membrane activity common to AMPs. Moreover, we highlight an important distinction between the maximal recognizability of a sequence to a trained AMP classifier (its ability to generate membrane curvature) and its maximal antimicrobial efficacy. As mutational distances are increased from known AMPs, we find AMP-like sequences that are increasingly difficult for nature to discover via simple mutation. Using the sequence map as a discovery tool, we find a unexpectedly diverse taxonomy of sequences that are just as membrane-active as known AMPs, but with a broad range of primary functions distinct from AMP functions, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. The SVM classifier is useful as a general detector of membrane activity in peptide sequences. PMID:27849600

Crowdsourced direct-to-consumer genomic analysis of a family quartet.

PubMed

Corpas, Manuel; Valdivia-Granda, Willy; Torres, Nazareth; Greshake, Bastian; Coletta, Alain; Knaus, Alexej; Harrison, Andrew P; Cariaso, Mike; Moran, Federico; Nielsen, Fiona; Swan, Daniel; Weiss Solís, David Y; Krawitz, Peter; Schacherer, Frank; Schols, Peter; Yang, Huangming; Borry, Pascal; Glusman, Gustavo; Robinson, Peter N

2015-11-07

We describe the pioneering experience of a Spanish family pursuing the goal of understanding their own personal genetic data to the fullest possible extent using Direct to Consumer (DTC) tests. With full informed consent from the Corpas family, all genotype, exome and metagenome data from members of this family, are publicly available under a public domain Creative Commons 0 (CC0) license waiver. All scientists or companies analysing these data ("the Corpasome") were invited to return results to the family. We released 5 genotypes, 4 exomes, 1 metagenome from the Corpas family via a blog and figshare under a public domain license, inviting scientists to join the crowdsourcing efforts to analyse the genomes in return for coauthorship or acknowldgement in derived papers. Resulting analysis data were compiled via social media and direct email. Here we present the results of our investigations, combining the crowdsourced contributions and our own efforts. Four companies offering annotations for genomic variants were applied to four family exomes: BIOBASE, Ingenuity, Diploid, and GeneTalk. Starting from a common VCF file and after selecting for significant results from company reports, we find no overlap among described annotations. We additionally report on a gut microbiome analysis of a member of the Corpas family. This study presents an analysis of a diverse set of tools and methods offered by four DTC companies. The striking discordance of the results mirrors previous findings with respect to DTC analysis of SNP chip data, and highlights the difficulties of using DTC data for preventive medical care. To our knowledge, the data and analysis results from our crowdsourced study represent the most comprehensive exome and analysis for a family quartet using solely DTC data generation to date.

STAR-GALAXY CLASSIFICATION IN MULTI-BAND OPTICAL IMAGING

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

Fadely, Ross; Willman, Beth; Hogg, David W.

2012-11-20

Ground-based optical surveys such as PanSTARRS, DES, and LSST will produce large catalogs to limiting magnitudes of r {approx}> 24. Star-galaxy separation poses a major challenge to such surveys because galaxies-even very compact galaxies-outnumber halo stars at these depths. We investigate photometric classification techniques on stars and galaxies with intrinsic FWHM <0.2 arcsec. We consider unsupervised spectral energy distribution template fitting and supervised, data-driven support vector machines (SVMs). For template fitting, we use a maximum likelihood (ML) method and a new hierarchical Bayesian (HB) method, which learns the prior distribution of template probabilities from the data. SVM requires training datamore » to classify unknown sources; ML and HB do not. We consider (1) a best-case scenario (SVM{sub best}) where the training data are (unrealistically) a random sampling of the data in both signal-to-noise and demographics and (2) a more realistic scenario where training is done on higher signal-to-noise data (SVM{sub real}) at brighter apparent magnitudes. Testing with COSMOS ugriz data, we find that HB outperforms ML, delivering {approx}80% completeness, with purity of {approx}60%-90% for both stars and galaxies. We find that no algorithm delivers perfect performance and that studies of metal-poor main-sequence turnoff stars may be challenged by poor star-galaxy separation. Using the Receiver Operating Characteristic curve, we find a best-to-worst ranking of SVM{sub best}, HB, ML, and SVM{sub real}. We conclude, therefore, that a well-trained SVM will outperform template-fitting methods. However, a normally trained SVM performs worse. Thus, HB template fitting may prove to be the optimal classification method in future surveys.« less

Two new miniature inverted-repeat transposable elements in the genome of the clam Donax trunculus.

PubMed

Šatović, Eva; Plohl, Miroslav

2017-10-01

Repetitive sequences are important components of eukaryotic genomes that drive their evolution. Among them are different types of mobile elements that share the ability to spread throughout the genome and form interspersed repeats. To broaden the generally scarce knowledge on bivalves at the genome level, in the clam Donax trunculus we described two new non-autonomous DNA transposons, miniature inverted-repeat transposable elements (MITEs), named DTC M1 and DTC M2. Like other MITEs, they are characterized by their small size, their A + T richness, and the presence of terminal inverted repeats (TIRs). DTC M1 and DTC M2 are 261 and 286 bp long, respectively, and in addition to TIRs, both of them contain a long imperfect palindrome sequence in their central parts. These elements are present in complete and truncated versions within the genome of the clam D. trunculus. The two new MITEs share only structural similarity, but lack any nucleotide sequence similarity to each other. In a search for related elements in databases, blast search revealed within the Crassostrea gigas genome a larger element sharing sequence similarity only to DTC M1 in its TIR sequences. The lack of sequence similarity with any previously published mobile elements indicates that DTC M1 and DTC M2 elements may be unique to D. trunculus.

MIEC-SVM: automated pipeline for protein peptide/ligand interaction prediction.

PubMed

Li, Nan; Ainsworth, Richard I; Wu, Meixin; Ding, Bo; Wang, Wei

2016-03-15

MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency. The program is available at http://wanglab.ucsd.edu/MIEC-SVM CONTACT: : wei-wang@ucsd.edu Supplementary data available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

PubMed

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

2014-09-07

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

77 FR 11175 - Self-Regulatory Organizations; The Depository Trust Company; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-24

... support the Request. In order to facilitate this automation, DTC will create a function that will provide... automation, DTC will be able to reduce the notification time frame on full call MMIs so that effective April... automation input mechanism. Additionally, at the request of the Options Clearing Corporation (``OCC''), DTC...

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

General practitioner attitudes to direct-to-consumer genetic testing in New Zealand.

PubMed

Ram, Sanyogita; Russell, Bruce; Gubb, Mary; Taylor, Rebekah; Butler, Cassandra; Khan, Imran; Shelling, Andrew

2012-10-26

The aim of the study was to explore the attitudes of general practitioners (GPs) towards direct to consumer (DTC) genetic testing and elicit their perceptions of the risks and benefits associated with DTC genetic testing. A postal questionnaire was mailed to a national random sample of 300 registered GPs from a list provided by the New Zealand Medical Council. Non-responders were followed up with an abridged survey questionnaire. Responses were received from 38% of the GPs contacted. This consisted of 113 responses from the full questionnaire. The proportion of respondents who had heard about DTC genetic testing was 47.8%. Respondents considered convenience to be the greatest benefit for the individual requesting DTC genetic testing. Misunderstanding of results and inadequate provision of information were perceived to be the greatest risks associated. Lack of knowledge, experience and time were all considered barriers to GPs providing genetic counselling, and a genetic specialist was highlighted as the most appropriate to provide this. Respondents thought advertising of DTC genetic testing should be regulated in a similar manner to DTC advertising of prescription medicines. Clinical validity of tests and counselling were thought to be the most important aspects to be regulated. As public access to DTC genetic testing increases, the role of GPs knowledge and training to reflect this growth will become increasingly more important. The 'Patient-Doctor-Counsellor Model of Delivery of Genetic Services' may be more appropriate for the provision of this service than the current model of direct access by patients. The involvement of health professionals in the DTC genetic testing process will aid patients in making informed health decisions, and ensure increased benefit from recent advances in genetic information.

Prioritising drug and therapeutics committee (DTC) decisions: a national survey.

PubMed

Tan, Ee Lyn; Day, Richard O; Brien, Jo-anne E

2007-04-01

A national survey was conducted to explore stakeholder opinions about: (1) the domains of activity and criteria used to determine "important" decisions; (2) the "importance" of Drug and Therapeutics Committee (DTC) decisions as an appropriate approach for prioritising implementation and actions and (3) how DTC decisions could be prioritised for action. This is a study of DTCs conducted in the Australian health care setting. A semi-structured questionnaire was sent to Directors of Pharmacies or Chief Pharmacists in Australian hospitals. Questionnaires could be returned by email or by fax. Two weeks after initial mail-out, non-responders were followed-up. Responses were collated and analysed using descriptive statistics. Free-text responses were collated. QSR NVivo was used as a data management tool. The response rate was 61%. All respondents indicated that "patient safety" was a domain of importance for a decision. Other domains of important DTC decisions include: "ensuring the practice of evidence based medicine within their institution" (94%), "cost" (93%), "ensure practice according to legislative requirements" (87%). Most respondents agreed that some DTC decisions were more important than others. Given constraints on time and resources, the majority agreed that DTC decisions should be prioritised for implementation, although most had no suggestions about how this could be done. Some suggested that the domains of importance could be the basis for priority assignment. Currently DTC decisions and policies are implemented in an ad hoc manner. As a result implementation may be incomplete and ineffective, and may pose a risk of serious consequences in patient care. This study identifies the domains or criteria of DTC decisions so that DTCs may allocate scarce resources to the systematic implementation of important decisions.

Dietary habits during the 2 months following the Chernobyl accident and differentiated thyroid cancer risk in a population-based case-control study.

PubMed

Xhaard, Constance; Rubino, Carole; Souchard, Vincent; Maillard, Stéphane; Ren, Yan; Borson-Chazot, Françoise; Sassolas, Geneviève; Schvartz, Claire; Colonna, Marc; Lacour, Brigitte; Woronoff, Anne Sophie; Velten, Michel; Marrer, Emilie; Bailly, Laurent; Mariné Barjoan, Eugènia; Schlumberger, Martin; Drozdovitch, Vladimir; Bouville, Andre; Orgiazzi, Jacques; Adjadj, Elisabeth; de Vathaire, Florent

2018-02-01

The Chernobyl nuclear power plant accident occurred in Ukraine on April 26th 1986. In France, the radioactive fallout and thyroid radiation doses were much lower than in highly contaminated areas. However, a number of risk projections have suggested that a small excess in differentiated thyroid cancer (DTC) might occur in eastern France due to this low-level fallout. In order to investigate this potential impact, a case-control study on DTC risk factors was started in 2005, focusing on cases who were less than 15 years old at the time of the Chernobyl accident. Here, we aim to evaluate the relationship between some specific reports of potentially contaminated food between April and June 1986 - in particular fresh dairy products and leafy vegetables - and DTC risk. After excluding subjects who were not born before the Chernobyl accident, the study included 747 cases of DTC matched with 815 controls. Odds ratios were calculated using conditional logistic regression models and were reported for all participants, for women only, for papillary cancer only, and excluding microcarcinomas. The DTC risk was slightly higher for participants who had consumed locally produced leafy vegetables. However, this association was not stronger in the more contaminated areas than in the others. Conversely, the reported consumption of fresh dairy products was not statistically associated with DTC risk. Because the increase in DTC risk associated with a higher consumption of locally produced vegetables was not more important in the most contaminated areas, our study lacked power to provide evidence for a strong association between consumption of potentially contaminated food and DTC risk. Copyright © 2017 Elsevier Ltd. All rights reserved.

Advanced generation anti-prostate specific membrane antigen designer T cells for prostate cancer immunotherapy.

PubMed

Ma, Qiangzhong; Gomes, Erica M; Lo, Agnes Shuk-Yee; Junghans, Richard P

2014-02-01

Adoptive immunotherapy by infusion of designer T cells (dTc) engineered with chimeric antigen receptors (CARs) for tumoricidal activity represents a potentially highly specific modality for the treatment of cancer. In this study, 2nd generation (gen) anti-prostate specific membrane antigen (PSMA) dTc were developed for improving the efficacy of previously developed 1st gen dTc for prostate cancer immunotherapy. The 1st gen dTc are modified with chimeric immunoglobulin-T cell receptor (IgTCR) while the 2nd gen dTc are engineered with an immunoglobulin-CD28-T cell receptor (IgCD28TCR), which incorporates a CD28 costimulatory signal for optimal T cell activation. A 2nd gen anti-PSMA IgCD28TCR CAR was constructed by inserting the CD28 signal domain into the 1st gen CAR. 1st and 2nd gen anti-PSMA dTc were created by transducing human T cells with anti-PSMA CARs and their antitumor efficacy was compared for specific activation on PSMA-expressing tumor contact, cytotoxicity against PSMA-expressing tumor cells in vitro, and suppression of tumor growth in an animal model. The 2nd gen dTc can be optimally activated to secrete larger amounts of cytokines such as IL2 and IFNγ than 1st gen and to proliferate more vigorously on PSMA-expressing tumor contact. More importantly, the 2nd gen dTc preserve the PSMA-specific cytotoxicity in vitro and suppress tumor growth in animal models with significant higher potency. Our results demonstrate that 2nd gen anti-PSMA designer T cells exhibit superior antitumor functions versus 1st gen, providing a rationale for advancing this improved agent toward clinical application in prostate cancer immunotherapy. © 2013 Wiley Periodicals, Inc.

Decreased staging of differentiated thyroid cancer in patients with chronic lymphocytic thyroiditis.

PubMed

Borowczyk, M; Janicki, A; Dworacki, G; Szczepanek-Parulska, E; Danieluk, M; Barnett, J; Antonik, M; Kałużna, M; Bromińska, B; Czepczyński, R; Bączyk, M; Ziemnicka, K; Ruchała, M

2018-04-04

The biological association between chronic lymphocytic thyroiditis (CLT) and differentiated thyroid cancer (DTC) has not been elucidated yet. The aim of the study was to assess whether the presence of CLT exerts any influence on clinical or histological presentation of DTC. Nine hundred and seven consecutive patients with DTC treated in the years 1998-2016 were divided into two groups according to the presence or absence of concomitant CLT. The statistical differences were analysed. Out of 907 patients included in the study, 331 were diagnosed with DTC and CLT (studied group), while 576 patients with DTC but without CLT constituted a control group. The distribution of papillary and follicular thyroid cancer did not differ. In CLT group, the prevalence of pT1 was greater than for pT2-pT4 DTC (P = 0.0003; OR = 1.69, 95% CI 1.27-2.24) compared to controls (68.3 vs. 56.1%, respectively). The presence of multifocal lesions was similar. The thyroid capsule infiltration without extrathyroidal invasion (P < 0.0001; OR = 0.21, 95% CI 0.14-0.31) was more frequent in the studied group, unlike extracapsular invasion, which was significantly more often present in patients with DTC but without CLT (P = 0.004; OR = 1.66; 95% CI 1.17-2.34) as well as nodal involvement (P = 0.048; OR = 0.65, 95% CI 0.42-0.99). The collected data indicate a protective role of CLT in preventing the spread of the DTC. The presence of CLT might limit tumour growth to the primary site.

[Different wavelengths selection methods for identification of early blight on tomato leaves by using hyperspectral imaging technique].

PubMed

Cheng, Shu-Xi; Xie, Chuan-Qi; Wang, Qiao-Nan; He, Yong; Shao, Yong-Ni

2014-05-01

Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonaliza-tion, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380-1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4. 7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97. 83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.

QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components.

PubMed

Shahlaei, Mohsen; Sabet, Razieh; Ziari, Maryam Bahman; Moeinifard, Behzad; Fassihi, Afshin; Karbakhsh, Reza

2010-10-01

Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R(2)=0.91 and R(CV)(2)=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

High Ripples Reduction in DTC of Induction Motor by Using a New Reduced Switching Table

NASA Astrophysics Data System (ADS)

Mokhtari, Bachir; Benkhoris, Mohamed F.

2016-05-01

The direct torque and flux control (DTC) of electrical motors is characterized by ripples of torque and flux. Among the many solutions proposed to reduce them is to use modified switching tables which is very advantageous; because its implementation is easy and requires no additional cost compared to other solutions. This paper proposes a new reduced switching table (RST) to improve the DTC by reducing harmful ripples of torque and flux. This new switching table is smaller than the conventional one (CST) and depends principally at the flux error. This solution is studied by simulation under Matlab/Simulink and experimentally validated on a testbed with DSPACE1103. The results obtained of a DTC with RST applied to a three-phase induction motor (IM) show a good improvement and an effectiveness of proposed solution, the torque ripple decreases about 47% and 3% for the stator flux compared with a basic DTC.

Consumers' views of direct-to-consumer genetic information.

PubMed

McBride, Colleen M; Wade, Christopher H; Kaphingst, Kimberly A

2010-01-01

In this report, we describe the evolution and types of genetic information provided directly to consumers, discuss potential advantages and disadvantages of these products, and review research evaluating consumer responses to direct-to-consumer (DTC) genetic testing. The available evidence to date has focused on predictive tests and does not suggest that individuals, health care providers, or health care systems have been harmed by a DTC provision of genetic information. An understanding of consumer responses to susceptibility tests has lagged behind. The Multiplex Initiative is presented as a case study of research to understand consumers' responses to DTC susceptibility tests. Three priority areas are recommended for accelerated research activities to inform public policy regarding DTC genetic information: (a) exploring consumer's long-term responses to DTC genetic testing on a comprehensive set of outcomes, (b) evaluating optimal services to support decision making about genetic testing, and (c) evaluating best practices in promoting genetic competencies among health providers.

Randomized study of placebo and framing information in direct-to-consumer print advertisements for prescription drugs.

PubMed

O'Donoghue, Amie C; Sullivan, Helen W; Aikin, Kathryn J

2014-12-01

Research suggests that quantitative information in direct-to-consumer (DTC) prescription drug ads may be helpful for consumers. The objective was to examine the effect of adding placebo rates and framing to DTC ads. In study 1, 2,000 Internet panel members with chronic pain participated in a randomized controlled experiment of DTC ads varying in placebo rate and framing. In study 2, 596 physicians ranked DTC ads varying in placebo rate and framing by how well they conveyed scientific information and their usefulness for patients. In study 1, participants who viewed placebo rates were able to recall them and use them to form certain perceptions. A mixed frame led to lower placebo rate recall and perceived efficacy. In study 2, overall, physicians preferred a placebo/single frame ad. Adding placebo rates to DTC ads may be useful for consumers. The evidence does not support using a mixed frame.

Analysis of programming properties and the row-column generation method for 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper deals with fast methods for training a 1-norm support vector machine (SVM). First, we define a specific class of linear programming with many sparse constraints, i.e., row-column sparse constraint linear programming (RCSC-LP). In nature, the 1-norm SVM is a sort of RCSC-LP. In order to construct subproblems for RCSC-LP and solve them, a family of row-column generation (RCG) methods is introduced. RCG methods belong to a category of decomposition techniques, and perform row and column generations in a parallel fashion. Specially, for the 1-norm SVM, the maximum size of subproblems of RCG is identical with the number of Support Vectors (SVs). We also introduce a semi-deleting rule for RCG methods and prove the convergence of RCG methods when using the semi-deleting rule. Experimental results on toy data and real-world datasets illustrate that it is efficient to use RCG to train the 1-norm SVM, especially in the case of small SVs. Copyright © 2013 Elsevier Ltd. All rights reserved.

A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

NASA Astrophysics Data System (ADS)

Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

2017-08-01

The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM.

PubMed

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

2017-06-01

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

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

PubMed

Li, Qiang; Gu, Yu; Jia, Jing

2017-01-30

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Support Vector Machine algorithm for regression and classification

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

Yu, Chenggang; Zavaljevski, Nela

2001-08-01

The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by themore » capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PubMed

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

PubMed Central

Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

A study of speech emotion recognition based on hybrid algorithm

NASA Astrophysics Data System (ADS)

Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

2011-10-01

To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

PubMed

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

PubMed

Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

2017-12-26

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

Analytical method validation to evaluate dithiocarbamates degradation in biobeds in South of Brazil.

PubMed

Vareli, Catiucia S; Pizzutti, Ionara R; Gebler, Luciano; Cardoso, Carmem D; Gai, Daniela S H; Fontana, Marlos E Z

2018-07-01

In order to evaluate the efficiency of biobeds on DTC degradation, the aim of this study was to apply, optimize and validate a method to determine dithiocarbamate (mancozeb) in biobeds using gas chromatography-tandem mass spectrometry (GC-MS). The DTC pesticide mancozeb was hydrolysed in a tin (II) chloride solution at 1.5% in HCl (4 mol L -1 ), during 1 h in a water bath at 80 °C, and the CS 2 formed was extracted in isooctane. After cooling, 1 mL of the organic layer was transferred to an auto sampler vial and analyzed by GC-MS. A complete validation study was performed and the following parameters were assessed: linearity of the analytical curve (r 2 ), estimated method and instrument limits of detection and limits of quantification (LODm, LODi, LOQm and LOQi, respectively), accuracy (recovery%), precision (RSD%) and matrix effects. Recovery experiments were carried out with a standard spiking solution of the DTC pesticide thiram. Blank biobed (biomixture) samples were spiked at the three levels corresponding to the CS 2 concentrations of 1, 3 and 5 mg kg -1 , with seven replicates each (n = 7). The method presented satisfactory accuracy, with recoveries within the range of 89-96% and RSD ≤ 11%. The analytical curves were linear in the concentration range of 0.05-10 µg CS 2 mL -1 (r 2 > 0.9946). LODm and LOQm were 0.1 and 0.5 mg CS 2 kg -1 , respectively, and the calculated matrix effects were not significant (≤ 20%). The validated method was applied to 80 samples (biomixture), from sixteen different biobeds (collected at five sampling times) during fourteen months. Ten percent of samples presented CS 2 concentration below the LOD (0.1 mg CS 2 kg -1 ) and 49% of them showed results below the LOQ (0.5 mg CS 2 kg -1 ), which demonstrates the biobeds capability to degrade DTC. Copyright © 2018 Elsevier B.V. All rights reserved.

DTC advertising harms patients and should be tightly regulated.

PubMed

Lurie, Peter

2009-01-01

Like all interventions in health care, direct-to-consumer (DTC) advertising should be evaluated by comparing its risks to its benefits, in the context of the available or potentially available alternatives. The objective, of course, is to realize any unique benefits while minimizing the risks. On balance, the adverse effects of DTC advertising outweigh the still-undemonstrated benefits of the advertising.

78 FR 40250 - Self-Regulatory Organizations; The Depository Trust Company (“DTC”); Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-03

... SECURITIES AND EXCHANGE COMMISSION [Release No. 34-69864; File No. SR-DTC-2013-08] Self-Regulatory Organizations; The Depository Trust Company (``DTC''); Notice of Filing and Immediate Effectiveness of Proposed Rule Change To Implement a Fee Associated With the Expansion of DTC's Ability To Collect and Pass Through Fees Owed by Participants to...

Do perceptions of direct-to-consumer pharmaceutical advertising vary based on urban versus rural living?

PubMed

Spake, Deborah F; Joseph, Mathew; Megehee, Carol M

2014-01-01

This study explores the connection between perceptions of direct-to-consumer (DTC) advertising based on where people live and shop. Urban consumers were found to be more skeptical of DTC advertising, but more likely to believe that physicians select pharmaceuticals based on the efficacy of the product. Those living in rural areas were more motivated to visit a doctor and more likely to feel an equal doctor-patient relationship after exposure to DTC advertising. Interaction effects among gender, income, and education were detected, as well as an interaction effects between location and income on views of DTC advertising.

Subjective health literacy and older adults' assessment of direct-to-consumer prescription drug ads.

PubMed

An, Soontae; Muturi, Nancy

2011-01-01

Older adults are increasingly the intended target of direct-to-consumer (DTC) prescription drug ads, but limited evidence exists as to how they assess the educational value of DTC ads and, more importantly, whether their assessment depends on their level of health literacy. In-person interviews of 170 older adults revealed that those with low subjective health literacy evaluated the educational value of DTC ads significantly lower than did those with high subjective health literacy. The results prompt us to pay more scholarly attention to determining how effectively DTC ads convey useful medical information, particularly to those with limited health literacy.

"It's our DNA, we deserve the right to test!" A content analysis of a petition for the right to access direct-to-consumer genetic testing.

PubMed

Su, Yeyang; Borry, Pascal; Otte, Ina C; Howard, Heidi C

2013-09-01

Various companies are currently advertising or selling genetic tests over the internet using a model of provision referred to as 'direct-to-consumer' (DTC). This commercial offer of DTC genetic testing (GT) has fueled a number of scientific, ethical and policy debates. To date there have been few studies published regarding the users' perspective. This study aimed to obtain information regarding the issues raised by individuals who signed a petition in support of DTC GT and the 'unrestricted' access to their genetic information. We conducted qualitative content analysis of comments written by individuals who signed a public online petition initiated by DIYgenomics (CA, USA) to support "personal access to genetic information". Of the 523 individuals who signed the petition sponsored by DIYgenomics, 247 individuals also wrote individual comments. A content analysis of these comments reveals that petitioners raised six main issues in support of unrestricted access to DTC GT: that their ownership of their DNA should allow them to have unrestricted access to their genomic information; that they should have the right to their genomic information; that the government has no place in (further) regulating DTC GT; that healthcare professionals should not be placed as intermediaries when purchasing DTC GT services; that many petioners who had already obtained DTC GT had positive experiences with this model of provision; and that genealogy or ancestry DNA testing is one of the main activities petitioners wish to have 'unrestricted' or 'direct' access. These results give insight into why individuals may support unrestricted access to their genomic information and confirm some of the motivations of users for purchasing DTC GT. Our analysis also brings to the forefront themes that have been raised less often in empirical studies involving motivations to purchase DTC GT services; these include the strongly held beliefs of some petitioners that, since they own their DNA, they should have the right to access the information without (further) government control or physician involvement. Interestingly, the comments left by petitioners also reveal a certain distrust of governmental agencies and healthcare professionals. This urges us to further study the public's views of these services and the potential impact of these views in order to responsibly address the ongoing debate on DTC GT.

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

PubMed

Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

2015-01-01

Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

PubMed

Li, Ying Hong; Xu, Jing Yu; Tao, Lin; Li, Xiao Feng; Li, Shuang; Zeng, Xian; Chen, Shang Ying; Zhang, Peng; Qin, Chu; Zhang, Cheng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

2016-01-01

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

Cancer survival classification using integrated data sets and intermediate information.

PubMed

Kim, Shinuk; Park, Taesung; Kon, Mark

2014-09-01

Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles. Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses. Copyright © 2014 Elsevier B.V. All rights reserved.

Ethical and clinical practice considerations for genetic counselors related to direct-to-consumer marketing of genetic tests.

PubMed

Wade, Christopher H; Wilfond, Benjamin S

2006-11-15

Several companies utilize direct-to-consumer (DTC) advertising for genetic tests and some, but not all, bypass clinician involvement by offering DTC purchase of the tests. This article examines how DTC marketing strategies may affect genetic counselors, using available cardiovascular disease susceptibility tests as an illustration. The interpretation of these tests is complex and includes consideration of clinical validity and utility, and the further complications of gene-environment interactions and pleiotropy. Although it is unclear to what extent genetic counselors will encounter clients who have been exposed to DTC marketing strategies, these strategies may influence genetic counseling interactions if they produce directed interest in specific tests and unrealistic expectations for the tests' capacity to predict disease. Often, a client's concern about risk for cardiovascular diseases is best addressed by established clinical tests and a family history assessment. Ethical dilemmas may arise for genetic counselors who consider whether to accept clients who request test interpretation or to order DTC-advertised tests that require a clinician's authorization. Genetic counselors' obligations to care for clients extend to interpreting DTC tests, although this obligation may be fulfilled by referral or consultation with specialists. Genetic counselors do not have an obligation to order DTC-advertised tests that have minimal clinical validity and utility at a client's request. This can be a justified restriction on autonomy based on consideration of risks to the client, the costs, and the implications for society. Published 2006 Wiley-Liss, Inc.

Fatigue and fatigue-related symptoms in patients treated for different causes of hypothyroidism.

PubMed

Louwerens, Marloes; Appelhof, Bente C; Verloop, Herman; Medici, Marco; Peeters, Robin P; Visser, Theo J; Boelen, Anita; Fliers, Eric; Smit, Johannes W A; Dekkers, Olaf M

2012-12-01

Research on determinants of well-being in patients on thyroid hormone replacement therapy is warranted, as persistent fatigue-related complaints are common in this population. In this study, we evaluated the impact of different states of hypothyroidism on fatigue and fatigue-related symptoms. Furthermore, the relationship between fatigue and the TSH receptor (TSHR)-Asp727Glu polymorphism, a common genetic variant of the TSHR, was analyzed. A cross-sectional study was performed in 278 patients (140 patients treated for differentiated thyroid carcinoma (DTC) and 138 with autoimmune hypothyroidism (AIH)) genotyped for the TSHR-Asp727Glu polymorphism. The multidimensional fatigue inventory (MFI-20) was used to assess fatigue, with higher MFI-20 scores indicating more fatigue-related complaints. MFI-20 scores were related to disease status and Asp727Glu polymorphism status. AIH patients scored significantly higher than DTC patients on all five MFI-20 subscales (P<0.001), independent of clinical and thyroid hormone parameters. The frequency of the TSHR-Glu727 allele was 7.2%. Heterozygous DTC patients had more favorable MFI-20 scores than wild-type DTC patients on four of five subscales. The modest effect of the TSHR-Asp727Glu polymorphism on fatigue was found in DTC patients only. AIH patients had significantly higher levels of fatigue compared with DTC patients, which could not be attributed to clinical or thyroid hormone parameters. The modest effect of the TSHR-Asp727Glu polymorphism on fatigue in DTC patients should be confirmed in other cohorts.

A 3-stage model for assessing the probable economic effects of direct-to-consumer advertising of pharmaceuticals.

PubMed

Vogel, Ronald J; Ramachandran, Sulabha; Zachry, Woodie M

2003-01-01

The pharmaceutical industry employs a variety of marketing strategies that have previously been directed primarily toward physicians. However, mass media direct-to-consumer (DTC) advertising of prescription drugs has emerged as a ubiquitous promotional strategy. This article explores the economics of DTC advertising in greater depth than has been done in the past by using a 3-stage economic model to assess the pertinent literature and to show the probable effects of DTC advertising in the United States. Economics literature on the subject was searched using the Journal of Economic Literature. Health services literature was searched using computer callback devices. Spending on DTC advertising in the United States increased from $17 million in 1985 to $2.5 billion in 2000. Proponents of DTC advertising claim that it provides valuable product-related information to health care professionals and patients, may contribute to better use of medications, and helps patients take charge of their own health care. Opponents argue that DTC advertising provides misleading messages rather than well-balanced, evidence-based information. The literature is replete with opinions about the effects of prescription drug advertising on pharmaceutical drug prices and physician-prescribing patterns, but few studies have addressed the issues beyond opinion surveys. The economic literature on advertising effects in other markets, however, may provide insight. DTC advertising indirectly affects the price and the quantity of production of pharmaceuticals via its effect on changes in consumer demand.

Obatoclax and LY3009120 Efficiently Overcome Vemurafenib Resistance in Differentiated Thyroid Cancer.

PubMed

Wei, Wei-Jun; Sun, Zhen-Kui; Shen, Chen-Tian; Song, Hong-Jun; Zhang, Xin-Yun; Qiu, Zhong-Ling; Luo, Quan-Yong

2017-01-01

Although the prognosis of differentiated thyroid cancer (DTC) is relatively good, 30-40% of patients with distant metastases develop resistance to radioactive iodine therapy due to tumor dedifferentiation. For DTC patients harboring BRAF V600E mutation, Vemurafenib, a BRAF kinase inhibitor, has dramatically changed the therapeutic landscape, but side effects and drug resistance often lead to termination of the single agent treatment. In the present study, we showed that either LY3009120 or Obatoclax (GX15-070) efficiently inhibited cell cycle progression and induced massive death of DTC cells. We established that BRAF/CRAF dimerization was an underlying mechanism for Vemurafenib resistance. LY3009120, the newly discovered pan-RAF inhibitor, successfully overcame Vemurafenib resistance and suppressed the growth of DTC cells in vitro and in vivo. We also observed that expression of anti-apoptotic Bcl-2 increased substantially following BRAF inhibitor treatment in Vemurafenib-resistant K1 cells, and both Obatoclax and LY3009120 efficiently induced apoptosis of these resistant cells. Specifically, Obatoclax exerted its anti-cancer activity by inducing loss of mitochondrial membrane potential (ΔΨm), dysfunction of mitochondrial respiration, reduction of cellular glycolysis, autophagy, neutralization of lysosomes, and caspase-related apoptosis. Furthermore, the cancer killing effects of LY3009120 and Obatoclax extended to two more Vemurafenib-resistant DTC cell lines, KTC-1 and BCPAP. Taken together, our results highlighted the potential value of LY3009120 for both Vemurafenib-sensitive and -resistant DTC and provided evidence for the combination therapy using Vemurafenib and Obatoclax for radioiodine-refractory DTC.

Predicting metabolic syndrome using decision tree and support vector machine methods.

PubMed

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.

Comparison of SVM RBF-NN and DT for crop and weed identification based on spectral measurement over corn fields

USDA-ARS?s Scientific Manuscript database

It is important to find an appropriate pattern-recognition method for in-field plant identification based on spectral measurement in order to classify the crop and weeds accurately. In this study, the method of Support Vector Machine (SVM) was evaluated and compared with two other methods, Decision ...

3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM.

PubMed

Ghafouri, Hamidreza; Ranjbar, Mohsen; Sakhteman, Amirhossein

2017-08-01

A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q 2 LOO-CV =1, R 2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares. Copyright © 2017 Elsevier Ltd. All rights reserved.

Lamb Wave Damage Quantification Using GA-Based LS-SVM.

PubMed

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-06-12

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

PubMed Central

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-01-01

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification. PMID:28773003

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

PubMed

Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

2016-01-01

Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Classification of hadith into positive suggestion, negative suggestion, and information

NASA Astrophysics Data System (ADS)

Faraby, Said Al; Riviera Rachmawati Jasin, Eliza; Kusumaningrum, Andina; Adiwijaya

2018-03-01

As one of the Muslim life guidelines, based on the meaning of its sentence(s), a hadith can be viewed as a suggestion for doing something, or a suggestion for not doing something, or just information without any suggestion. In this paper, we tried to classify the Bahasa translation of hadith into the three categories using machine learning approach. We tried stemming and stopword removal in preprocessing, and TF-IDF of unigram, bigram, and trigram as the extracted features. As the classifier, we compared between SVM and Neural Network. Since the categories are new, so in order to compare the results of the previous pipelines, we created a baseline classifier using simple rule-based string matching technique. The rule-based algorithm conditions on the occurrence of words such as “janganlah, sholatlah, and so on” to determine the category. The baseline method achieved F1-Score of 0.69, while the best F1-Score from the machine learning approach was 0.88, and it was produced by SVM model with the linear kernel.

[Rapid determination of COD in aquaculture water based on LS-SVM with ultraviolet/visible spectroscopy].

PubMed

Liu, Xue-Mei; Zhang, Hai-Liang

2014-10-01

Ultraviolet/visible (UV/Vis) spectroscopy was studied for the rapid determination of chemical oxygen demand (COD), which was an indicator to measure the concentration of organic matter in aquaculture water. In order to reduce the influence of the absolute noises of the spectra, the extracted 135 absorbance spectra were preprocessed by Savitzky-Golay smoothing (SG), EMD, and wavelet transform (WT) methods. The preprocessed spectra were then used to select latent variables (LVs) by partial least squares (PLS) methods. Partial least squares (PLS) was used to build models with the full spectra, and back- propagation neural network (BPNN) and least square support vector machine (LS-SVM) were applied to build models with the selected LVs. The overall results showed that BPNN and LS-SVM models performed better than PLS models, and the LS-SVM models with LVs based on WT preprocessed spectra obtained the best results with the determination coefficient (r2) and RMSE being 0. 83 and 14. 78 mg · L(-1) for calibration set, and 0.82 and 14.82 mg · L(-1) for the prediction set respectively. The method showed the best performance in LS-SVM model. The results indicated that it was feasible to use UV/Vis with LVs which were obtained by PLS method, combined with LS-SVM calibration could be applied to the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network

PubMed Central

AMINI, Payam; AHMADINIA, Hasan; POOROLAJAL, Jalal; MOQADDASI AMIRI, Mohammad

2016-01-01

Background: We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM). Methods: We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated. Results: Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively. Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN. PMID:27957463

A new automated assessment method for contrast-detail images by applying support vector machine and its robustness to nonlinear image processing.

PubMed

Takei, Takaaki; Ikeda, Mitsuru; Imai, Kuniharu; Yamauchi-Kawaura, Chiyo; Kato, Katsuhiko; Isoda, Haruo

2013-09-01

The automated contrast-detail (C-D) analysis methods developed so-far cannot be expected to work well on images processed with nonlinear methods, such as noise reduction methods. Therefore, we have devised a new automated C-D analysis method by applying support vector machine (SVM), and tested for its robustness to nonlinear image processing. We acquired the CDRAD (a commercially available C-D test object) images at a tube voltage of 120 kV and a milliampere-second product (mAs) of 0.5-5.0. A partial diffusion equation based technique was used as noise reduction method. Three radiologists and three university students participated in the observer performance study. The training data for our SVM method was the classification data scored by the one radiologist for the CDRAD images acquired at 1.6 and 3.2 mAs and their noise-reduced images. We also compared the performance of our SVM method with the CDRAD Analyser algorithm. The mean C-D diagrams (that is a plot of the mean of the smallest visible hole diameter vs. hole depth) obtained from our devised SVM method agreed well with the ones averaged across the six human observers for both original and noise-reduced CDRAD images, whereas the mean C-D diagrams from the CDRAD Analyser algorithm disagreed with the ones from the human observers for both original and noise-reduced CDRAD images. In conclusion, our proposed SVM method for C-D analysis will work well for the images processed with the non-linear noise reduction method as well as for the original radiographic images.

An exploratory study of adolescent female reactions to direct-to-consumer advertising: the case of the Human Papillomavirus (HPV) Vaccine.

PubMed

Leader, Amy E; Cashman, Rebecca; Voytek, Chelsea D; Baker, Jillian L; Brawner, Bridgette M; Frank, Ian

2011-10-01

When the human papillomavirus (HPV) vaccine was approved in 2006, an extensive direct-to-consumer (DTC) advertising campaign raised awareness and promoted vaccination. This study explores adolescents' exposure to and understanding of the messages in these advertisements. Sixty-seven African American females participated in a focus group about DTC advertising for the HPV vaccine. Virtually all adolescents had seen an HPV vaccine DTC advertisement, but most did not understand the health information contained in it. If DTC advertising is to be an effective source of health information for adolescents in the future, it must take into account the unique features of an adolescent audience.

How direct-to-consumer television advertising for osteoarthritis drugs affects physicians' prescribing behavior.

PubMed

Bradford, W David; Kleit, Andrew N; Nietert, Paul J; Steyer, Terrence; McIlwain, Thomas; Ornstein, Steven

2006-01-01

Concern about the potential pernicious effect of direct-to-consumer (DTC) drug advertising on physicians' prescribing patterns was heightened with the 2004 withdrawal of Vioxx, a heavily advertised treatment for osteoarthritis. We examine how DTC advertising has affected physicians' prescribing behavior for osteoarthritis patients. We analyzed monthly clinical information on fifty-seven primary care practices during 2000-2002, matched to monthly brand-specific advertising data for local and network television. DTC advertising of Vioxx and Celebrex increased the number of osteoarthritis patients seen by physicians each month. DTC advertising of Vioxx increased the likelihood that patients received both Vioxx and Celebrex, but Celebrex ads only affected Vioxx use.

Turning point or tipping point: new FDA draft guidances and the future of DTC advertising.

PubMed

Pitts, Peter J

2004-01-01

According to Food and Drug Administration (FDA) research, direct-to-consumer (DTC) drug ads are not as empowering as they were even three years ago. How will the FDA's new draft guidances reverse this trend and affect the future of DTC advertising? Will they be a turning point, resulting in pharmaceutical companies' embracing an educational public health imperative, or a tipping point with politicians and the public zeroing in on aggressively targeted DTC ads as the postimportation pharmaceutical bête noire? The FDA believes that its new guidances strengthen the strategic argument that a better-informed consumer lays the groundwork for a better potential customer.

Message strategies in direct-to-consumer pharmaceutical advertising: a content analysis using Taylor's six-segment message strategy wheel.

PubMed

Tsai, Wan-Hsiu Sunny; Lancaster, Alyse R

2012-01-01

This exploratory study applies Taylor's (1999) six-segment message strategy wheel to direct-to-consumer (DTC) pharmaceutical television commercials to understand message strategies adopted by pharmaceutical advertisers to persuade consumers. A convenience sample of 96 DTC commercial campaigns was analyzed. The results suggest that most DTC drug ads used a combination approach, providing consumers with medical and drug information while simultaneously appealing to the viewer's ego-related needs and desires. In contrast to ration and ego strategies, other approaches including routine, acute need, and social are relatively uncommon while sensory was the least common message strategy. Findings thus recognized the educational value of DTC commercials.

Power line identification of millimeter wave radar based on PCA-GS-SVM

NASA Astrophysics Data System (ADS)

Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

2017-12-01

Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

Direct-to-Consumer Genetic Testing and Personal Genomics Services: A Review of Recent Empirical Studies

PubMed Central

Ostergren, Jenny

2013-01-01

Direct-to-consumer genetic testing (DTC-GT) has sparked much controversy and undergone dramatic changes in its brief history. Debates over appropriate health policies regarding DTC-GT would benefit from empirical research on its benefits, harms, and limitations. We review the recent literature (2011-present) and summarize findings across (1) content analyses of DTC-GT websites, (2) studies of consumer perspectives and experiences, and (3) surveys of relevant health care providers. Findings suggest that neither the health benefits envisioned by DTC-GT proponents (e.g., significant improvements in positive health behaviors) nor the worst fears expressed by its critics (e.g., catastrophic psychological distress and misunderstanding of test results, undue burden on the health care system) have materialized to date. However, research in this area is in its early stages and possesses numerous key limitations. We note needs for future studies to illuminate the impact of DTC-GT and thereby guide practice and policy regarding this rapidly evolving approach to personal genomics. PMID:24058877

Current landscape of direct-to-consumer genetic testing and its role in ophthalmology: a review.

PubMed

Sanfilippo, Paul G; Kearns, Lisa S; Wright, Philip; Mackey, David A; Hewitt, Alex W

2015-08-01

The sequencing of the human genome has seen the emergence of the direct-to-consumer (DTC) genetic-testing market, which allows individuals to obtain information about their genetic profile and its many health and lifestyle implications. Genetics play an important role in the development of many eye diseases, however, little information is available describing the influence of the DTC industry in ophthalmology. In this review, we examined DTC companies providing genetic test products for eye disease. Of all eye conditions, the majority of DTC companies provided susceptibility testing or risk assessment for age-related macular degeneration (AMD). For the 15 companies noted to offer products, we found considerable variation in the cost, scope and clarity of informational content of DTC genetic testing for ophthalmic conditions. The clinical utility of these tests remains in question, and the American Academy of Ophthalmology recommendations against routine testing for many conditions probably still apply. © 2015 Royal Australian and New Zealand College of Ophthalmologists.

Perceived diabetes task competence mediates the relationship of both negative and positive affect with blood glucose in adolescents with type 1 diabetes.

PubMed

Fortenberry, Katherine T; Butler, Jorie M; Butner, Jonathan; Berg, Cynthia A; Upchurch, Renn; Wiebe, Deborah J

2009-02-01

Adolescents dealing with type 1 diabetes experience disruptions in affect and diabetes management that may influence their blood glucose. A daily diary format examined whether daily fluctuations in both negative and positive affect were associated with adolescents' perceived diabetes task competence (DTC) and blood glucose, and whether perceived DTC mediated the relationship between daily affect and blood glucose. Sixty-two adolescents with type 1 diabetes completed a 2-week daily diary, which included daily measures of affect and perceived DTC, then recorded their blood glucose readings at the end of the day. We utilized hierarchical linear modeling to examine whether daily perceived DTC mediated the relationship between daily emotion and blood glucose. Daily perceived DTC mediated the relationship of both negative and positive affect with daily blood glucose. This study suggests that within the ongoing process of self-regulation, daily affect may be associated with blood glucose by influencing adolescents' perception of competence on daily diabetes tasks.

A Support Vector Machine-Based Gender Identification Using Speech Signal

NASA Astrophysics Data System (ADS)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Improving the Representation of Human Factors in Operational Analysis

DTIC Science & Technology

2010-10-01

Defence Equipment and Support (DE&S) via Human Factors Integration Defence Technology Centre ( HFI DTC) activities. In particular this study’s Theme...Framework has been exploited in the HFI DTC Social Organisational Framework study, and the study team has provided a short extract for contribution to...the HFI DTC Handbook. The study has also been explicitly referenced in support to future MOD OA research studies. 8 SUMMARY AND CONCLUSIONS This

Which lessons can we learn from the European Union legal framework of medicines for the regulation of direct-to-consumer genetic tests?

PubMed

van Hellemondt, Rachèl; Hendriks, Aart; Breuning, Martijn

2012-01-01

The legal framework of the European Union (EU) for regulating access to and supply of direct-to-consumer (DTC) genetic tests is very liberal compared to the legal and regulatory framework for (internet) medicines. Nevertheless, both health related products can cause equally serious damage to the well being of individuals. In this contribution we examine whether the legal framework of the EU for the safety and responsible use of (internet) medicines could be an example for regulating access to and supply of DTC genetic tests. The EU laws governing medicines can, notwithstanding their shortcomings, serve as an example for (central) authorising the marketing of DTC genetic tests on the internal market in accordance with strict criteria regarding predictive value and clinical usefulness. Furthermore, a legal framework controlling DTC genetic tests also should introduce system supervision as well as quality criteria with respect to the information to be provided to consumers in order to enhance health protection. However, DTC genetic tests purchased through online ordering are difficult to supervise by any agency. Adequately protecting individuals against questionable testing kits calls for international vigilance and comprehensive measures by the international community. For Europe, it is important to rank the regulation of DTC genetic tests on the European regulatory agenda.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

PubMed Central

Zhang, Daqing; Xiao, Jianfeng; Zhou, Nannan; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2015-01-01

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration. PMID:26504797

[Based on the LS-SVM modeling method determination of soil available N and available K by using near-infrared spectroscopy].

PubMed

Liu, Xue-Mei; Liu, Jian-She

2012-11-01

Visible infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement accuracy of soil properties,namely, available nitrogen(N) and available potassium(K). Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC) and Savitzky-Golay smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares (PLS) and least squares-support vector machine (LS-SVM) models analysis were implemented for calibration models. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including PCA(PCs), latent variables (LVs), and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The performance of the model was evaluated by the correlation coefficient (r2) and RMSEP. The optimal EWs-LS-SVM models were achieved, and the correlation coefficient (r2) and RMSEP were 0.82 and 17.2 for N and 0.72 and 15.0 for K, respectively. The results indicated that visible and short wave-near infrared spectroscopy (Vis/SW-NIRS)(325-1 075 nm) combined with LS-SVM could be utilized as a precision method for the determination of soil properties.

Nonlinear Demodulation and Channel Coding in EBPSK Scheme

PubMed Central

Chen, Xianqing; Wu, Lenan

2012-01-01

The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) performance. However, the conventional threshold decision cannot achieve the optimum performance, and the SIF brings more difficulty in obtaining the posterior probability for LDPC decoding. In this paper, we concentrate not only on reducing the BER of demodulation, but also on providing accurate posterior probability estimates (PPEs). A new approach for the nonlinear demodulation based on the support vector machine (SVM) classifier is introduced. The SVM method which selects only a few sampling points from the filter output was used for getting PPEs. The simulation results show that the accurate posterior probability can be obtained with this method and the BER performance can be improved significantly by applying LDPC codes. Moreover, we analyzed the effect of getting the posterior probability with different methods and different sampling rates. We show that there are more advantages of the SVM method under bad condition and it is less sensitive to the sampling rate than other methods. Thus, SVM is an effective method for EBPSK demodulation and getting posterior probability for LDPC decoding. PMID:23213281

Nonlinear demodulation and channel coding in EBPSK scheme.

PubMed

Chen, Xianqing; Wu, Lenan

2012-01-01

The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) performance. However, the conventional threshold decision cannot achieve the optimum performance, and the SIF brings more difficulty in obtaining the posterior probability for LDPC decoding. In this paper, we concentrate not only on reducing the BER of demodulation, but also on providing accurate posterior probability estimates (PPEs). A new approach for the nonlinear demodulation based on the support vector machine (SVM) classifier is introduced. The SVM method which selects only a few sampling points from the filter output was used for getting PPEs. The simulation results show that the accurate posterior probability can be obtained with this method and the BER performance can be improved significantly by applying LDPC codes. Moreover, we analyzed the effect of getting the posterior probability with different methods and different sampling rates. We show that there are more advantages of the SVM method under bad condition and it is less sensitive to the sampling rate than other methods. Thus, SVM is an effective method for EBPSK demodulation and getting posterior probability for LDPC decoding.

A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

Assessing controversial direct-to-consumer advertising for hereditary breast cancer testing: reactions from women and their physicians in a managed care organization.

PubMed

Mouchawar, Judy; Laurion, Suzanne; Ritzwoller, Debra P; Ellis, Jennifer; Kulchak-Rahm, Alanna; Hensley-Alford, Sharon

2005-10-01

To describe the impact on patients and physicians at a managed care organization (MCO) of a direct-to-consumer advertising (DTC-ad) campaign concerning testing for the BRCA1 and BRCA2 genes. Observational study. In 2003, we mailed a 30-item questionnaire to 750 randomly chosen female members of Kaiser Permanente Colorado (KPCO) aged 25 to 54 years, and 100 female KPCO members with a history of breast cancer genetic referral. We mailed a 7-item questionnaire to 180 randomly chosen KPCO primary care providers. Of 394 patient respondents, 245 (62%) reported exposure to the DTC-ad of whom 63% reported that the DTC-ad caused no anxiety at all. A high level of perceived breast cancer risk and being of Hispanic ethnicity each were independently associated with reported anxiety due to the DTC-ad (adjusted odds ratio [OR] = 3.23, 95% confidence interval [CI] = 1.35, 7.73, and adjusted OR = 4.19, 95% CI = 1.48, 11.83, respectively). Greater knowledge was seen among respondents exposed to the DTC-ad than among those reporting no exposure (P = .015). Of the physician respondents, 84% reported that the DTC-ad caused no strain on the doctor-patient relationship, and nearly 80% reported no effect on daily clinical practice. Genetic referrals soared more than 200% compared with the prior year, when there was no advertising. The DTC-ad had a marked impact on genetic services, but little apparent negative impact on patients or primary care providers at an MCO.

Direct-to-consumer marketing of prescription drugs: a current perspective for neurologists and psychiatrists.

PubMed

Hollon, Matthew F

2004-01-01

In the US and New Zealand, the past decade has seen tremendous growth in the marketing of prescription drugs directly to patients. The pharmaceutical industry has applied pressure in other countries to relax regulations governing such marketing although this has not yet been successful. While we still have much to learn about the potential impact on the public's health of direct-to-consumer (DTC) marketing, some data are available. This article summarises the current literature on the benefits and risks of DTC marketing. This marketing strategy has grown substantially in the US, but only select drugs are advertised. Whether there is net benefit or harm to the public's health as a result of DTC marketing depends critically on which drugs are advertised and the quality of the information provided in promotional material. Critical reviews of this promotional material suggest the information is of poor quality. Notably, 18% of the 50 drugs advertised most intensively in the US were medications used to treat psychiatric and neurological disorders. The impairments in decisional capacity often seen in psychiatric and neurological illness leave patients vunerable to the controlling influence of DTC marketing and, thus, undermine the patient autonomy that is said to be promoted by this marketing strategy. If there is any benefit from DTC marketing it is for significantly undertreated conditions. International restrictions on DTC marketing should remain in place until further evidence of net benefit or harm emerges from the DTC marketing experiment that is taking place in the US and New Zealand.

Obatoclax and LY3009120 Efficiently Overcome Vemurafenib Resistance in Differentiated Thyroid Cancer

PubMed Central

Wei, Wei-Jun; Sun, Zhen-Kui; Shen, Chen-Tian; Song, Hong-Jun; Zhang, Xin-Yun; Qiu, Zhong-Ling; Luo, Quan-Yong

2017-01-01

Although the prognosis of differentiated thyroid cancer (DTC) is relatively good, 30-40% of patients with distant metastases develop resistance to radioactive iodine therapy due to tumor dedifferentiation. For DTC patients harboring BRAFV600E mutation, Vemurafenib, a BRAF kinase inhibitor, has dramatically changed the therapeutic landscape, but side effects and drug resistance often lead to termination of the single agent treatment. In the present study, we showed that either LY3009120 or Obatoclax (GX15-070) efficiently inhibited cell cycle progression and induced massive death of DTC cells. We established that BRAF/CRAF dimerization was an underlying mechanism for Vemurafenib resistance. LY3009120, the newly discovered pan-RAF inhibitor, successfully overcame Vemurafenib resistance and suppressed the growth of DTC cells in vitro and in vivo. We also observed that expression of anti-apoptotic Bcl-2 increased substantially following BRAF inhibitor treatment in Vemurafenib-resistant K1 cells, and both Obatoclax and LY3009120 efficiently induced apoptosis of these resistant cells. Specifically, Obatoclax exerted its anti-cancer activity by inducing loss of mitochondrial membrane potential (ΔΨm), dysfunction of mitochondrial respiration, reduction of cellular glycolysis, autophagy, neutralization of lysosomes, and caspase-related apoptosis. Furthermore, the cancer killing effects of LY3009120 and Obatoclax extended to two more Vemurafenib-resistant DTC cell lines, KTC-1 and BCPAP. Taken together, our results highlighted the potential value of LY3009120 for both Vemurafenib-sensitive and -resistant DTC and provided evidence for the combination therapy using Vemurafenib and Obatoclax for radioiodine-refractory DTC. PMID:28382170

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Medicine, market and communication: ethical considerations in regard to persuasive communication in direct-to-consumer genetic testing services.

PubMed

Schaper, Manuel; Schicktanz, Silke

2018-06-05

Commercial genetic testing offered over the internet, known as direct-to-consumer genetic testing (DTC GT), currently is under ethical attack. A common critique aims at the limited validation of the tests as well as the risk of psycho-social stress or adaption of incorrect behavior by users triggered by misleading health information. Here, we examine in detail the specific role of advertising communication of DTC GT companies from a medical ethical perspective. Our argumentative analysis departs from the starting point that DTC GT operates at the intersection of two different contexts: medicine on the one hand and the market on the other. Both fields differ strongly with regard to their standards of communication practices and the underlying normative assumptions regarding autonomy and responsibility. Following a short review of the ethical contexts of medical and commercial communication, we provide case examples for persuasive messages of DTC GT websites and briefly analyze their design with a multi-modal approach to illustrate some of their problematic implications. We observe three main aspects in DTC GT advertising communication: (1) the use of material suggesting medical professional legitimacy as a trust-establishing tool, (2) the suggestion of empowerment as a benefit of using DTC GT services and (3) the narrative of responsibility as a persuasive appeal to a moral self-conception. While strengthening and respecting the autonomy of a patient is the focus in medical communication, specifically genetic counselling, persuasive communication is the normal mode in marketing of consumer goods, presuming an autonomous, rational, independent consumer. This creates tension in the context of DTC GT regarding the expectation and normative assessment of communication strategies. Our analysis can even the ground for a better understanding of ethical problems associated with intersections of medical and commercial communication and point to perspectives of analysis of DTC GT advertising.

Risky business: risk perception and the use of medical services among customers of DTC personal genetic testing.

PubMed

Kaufman, David J; Bollinger, Juli M; Dvoskin, Rachel L; Scott, Joan A

2012-06-01

Direct-to-consumer genetic testing has generated speculation about how customers will interpret results and how these interpretations will influence healthcare use and behavior; however, few empirical data on these topics exist. We conducted an online survey of DTC customers of 23andMe, deCODEme, and Navigenics to begin to address these questions. Random samples of U.S. DTC customers were invited to participate. Survey topics included demographics, perceptions of two sample DTC results, and health behaviors following DTC testing. Of 3,167 DTC customers invited, 33% (n = 1,048) completed the survey. Forty-three percent of respondents had sought additional information about a health condition tested; 28% had discussed their results with a healthcare professional; and 9% had followed up with additional lab tests. Sixteen percent of respondents had changed a medication or supplement regimen, and one-third said they were being more careful about their diet. Many of these health-related behaviors were significantly associated with responses to a question that asked how participants would perceive their colon cancer risk (as low, moderate, or high) if they received a test result showing an 11% lifetime risk, as compared to 5% risk in the general population. Respondents who would consider themselves to be at high risk for colon cancer were significantly more likely to have sought information about a disease (p = 0.03), discussed results with a physician (p = 0.05), changed their diet (p = 0.02), and started exercising more (p = 0.01). Participants' personal health contexts--including personal and family history of disease and quality of self-perceived health--were also associated with health-related behaviors after testing. Subjective interpretations of genetic risk data and personal context appear to be related to health behaviors among DTC customers. Sharing DTC test results with healthcare professionals may add perceived utility to the tests.

The number of 131I therapy courses needed to achieve complete remission is an indicator of prognosis in patients with differentiated thyroid carcinoma.

PubMed

Thies, Elena-Daphne; Tanase, Karina; Maeder, Uwe; Luster, Markus; Buck, Andreas K; Hänscheid, Heribert; Reiners, Christoph; Verburg, Frederik A

2014-12-01

To assess the risk of differentiated thyroid cancer (DTC) recurrence, DTC-related mortality and life expectancy in relation to the number of courses of (131)I therapy (RIT) and cumulative (131)I activities required to achieve complete remission (CR). The study was a database review of 1,229 patients with DTC, 333 without and 896 with CR (negative TSH-stimulated thyroglobulin and negative (131)I diagnostic whole-body scintigraphy) after one or more courses of RIT. The median follow-up was 9.0 years (range 0.1 - 31.8 years) after CR. Recurrence rates at 5 years, 10 years and the end of follow-up were 1.0 ± 0.3%, 4.0 ± 0.7 % and 6.2 ± 1.1 %, and DTC-related mortality was 0.1 ± 0.1%, 0.5 ± 0.3% and 3.4 ± 1.1%, respectively. Recurrence rates also increased with an increasing number of RIT courses required (p = 0.001). DTC-related mortality increased from four RIT courses. In patients with CR after one RIT course, there were no differences in recurrence or DTC-related mortality rates between low-risk and high-risk patients. In patients requiring two RIT courses these rates remain elevated in high-risk patients. Recurrence and DTC-related mortality rates were only significantly elevated in those requiring a cumulative activity over 22.2 GBq (600 mCi) from multiple RIT courses for CR. Regardless of the number of RIT courses or activity needed, life expectancy was not significantly lowered. If more than one RIT course is needed to achieve CR, higher recurrence and DTC-related mortality rates are observed, especially in high-risk patients. Patients requiring >22.2 GBq (131)I for CR should be followed in the same way as patients in whom CR is never reached as long-term mortality rates are similar.

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

NASA Astrophysics Data System (ADS)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

RECONCEPTUALIZING CONSENT FOR DIRECT-TO-CONSUMER HEALTH SERVICES.

PubMed

Spector-Bagdady, Kayte

2015-01-01

The market for direct-to-consumer (DTC) health services continues to grow rapidly with former patients converting to customers for the opportunity to purchase varied diagnostic tests without the involvement of their clinician. For the first time a DTC genetic testing company is advertising health-related reports "that meet [Food and Drug Administration] standards for being clinically and scientifically valid." Ethicists and regulatory agencies alike have recognized the need for a more informed transaction in the DTC context, but how should we classify a commercial transaction for something normally protected by a duty of care? How can we assure informed agreements in an industry with terms and conditions as varied as the services performed? The doctrine of "informed consent" began as an ethical construct building on the promise of beneficence in the clinical relationship and elevating the principle of autonomy--but in the DTC context should we hold providers to legal standards of informed consent and associated medical malpractice liability, or contractual obligations where consumers would seek remedy for breach? This Article analyzes the fine balance that must be struck in an industry where companies are selling services for entertainment or non-medical purposes that possess the capacity to produce serious and disquieting medical information. It begins by reviewing current standards of consent in the clinical setting from both a legal and ethical perspective and then lays forth current standards for DTC consent using two currently controversial case studies: that of keepsake fetal ultrasound and genetic testing. DTC keepsake ultrasound and genetic testing providers attempt to de-medicalize the devices used for these procedures from their intended medical uses to non-medical uses. But while keepsake ultrasound is marketed as "intended for entertainment purposes only," it can provide medical information as an incidental finding. 23andMe currently purports to be the only DTC genetics service that "includes" reports that meet FDA qualifications, despite disclaimers of intent to "provide medical advice." The attempted de-medicalization of these devices, therefore, has not been fully transformative, and DTC providers should have more robust ethical and legal duties than the average goods and services seller. This Article delineates these responsibilities, beginning with ethical duties surrounding marketing, entering into, and providing DTC services. It then turns to the legal paradigms necessary to enable, or at least allow for, DTC providers to meet these ethical obligations. While it argues that contractual, as opposed to fiduciary, requirements are most appropriate and that waivers of liability will likely be upheld, it also advocates for a heightened expectation of disclosure during contracting.

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Differences in serum thyroglobulin measurements by 3 commercial immunoradiometric assay kits and laboratory standardization using Certified Reference Material 457 (CRM-457).

PubMed

Lee, Ji In; Kim, Ji Young; Choi, Joon Young; Kim, Hee Kyung; Jang, Hye Won; Hur, Kyu Yeon; Kim, Jae Hyeon; Kim, Kwang-Won; Chung, Jae Hoon; Kim, Sun Wook

2010-09-01

Serum thyroglobulin (Tg) is essential in the follow-up of patients with differentiated thyroid carcinoma (DTC). However, interchangeability and standardization between Tg assays have not yet been achieved, even with the development of an international Tg standard (Certified Reference Material 457 [CRM-457]). Serum Tg from 30 DTC patients and serially diluted CRM-457 were measured using 3 different immunoradiometric assays (IRMA-1, IRMA-2, IRMA-3). The intraclass correlation coefficient (ICC) method was used to describe the concordance of each IRMA to CRM-457. The serum Tg measured by 3 different IRMAs correlated well (r > .85, p < .0001), but clinically relevant discrepancies were found in 13.3% of patients. IRMA-3, which claims to be standardized to CRM-457, showed the best ICC (p(1) = .98) for the CRM-457. Hospitals caring for patients with DTC should either set their own cutoffs for IRMAs for Tg based on their patient pools, or adopt IRMAs standardized to CRM-457 and calibrate their laboratory using CRM-457.

Bend it like Beckham! The Ethics of Genetically Testing Children for Athletic Potential

PubMed Central

Camporesi, Silvia

2016-01-01

The recent boom of direct-to-consumer (DTC) genetic tests, aimed at measuring children’s athletic potential, is the latest wave in the ‘pre-professionalization’ of children that has characterized, especially but not exclusively, the USA in the last 15 years or so. In this paper, I analyse the use of DTC genetic tests, sometimes coupled with more traditional methods of ‘talent scouting’, to assess a child’s predisposition to athletic performance. I first discuss the scientific evidence at the basis of these tests, and the parental decision in terms of education, and of investing in the children’s future, taken on the basis of the results of the tests. I then discuss how these parental practices impact on the children’s right to an open future, and on their developing sense of autonomy. I also consider the meaning and role of sports in childhood, and conclude that the use of DTC genetic tests to measure children’s athletic potential should be seen as a ‘wake up’ call for other problematic parental attitudes aimed at scouting and developing children’s talent. PMID:27996058

Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking.

PubMed

Ren, Ji-Xia; Li, Lin-Li; Zheng, Ren-Lin; Xie, Huan-Zhang; Cao, Zhi-Xing; Feng, Shan; Pan, You-Li; Chen, Xin; Wei, Yu-Quan; Yang, Sheng-Yong

2011-06-27

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.

An effective parameter optimization technique for vibration flow field characterization of PP melts via LS-SVM combined with SALS in an electromagnetism dynamic extruder

NASA Astrophysics Data System (ADS)

Xian, Guangming

2018-03-01

A method for predicting the optimal vibration field parameters by least square support vector machine (LS-SVM) is presented in this paper. One convenient and commonly used technique for characterizing the the vibration flow field of polymer melts films is small angle light scattering (SALS) in a visualized slit die of the electromagnetism dynamic extruder. The optimal value of vibration vibration frequency, vibration amplitude, and the maximum light intensity projection area can be obtained by using LS-SVM for prediction. For illustrating this method and show its validity, the flowing material is used with polypropylene (PP) and fifteen samples are tested at the rotation speed of screw at 36rpm. This paper first describes the apparatus of SALS to perform the experiments, then gives the theoretical basis of this new method, and detail the experimental results for parameter prediction of vibration flow field. It is demonstrated that it is possible to use the method of SALS and obtain detailed information on optimal parameter of vibration flow field of PP melts by LS-SVM.

Recognition of medication information from discharge summaries using ensembles of classifiers.

PubMed

Doan, Son; Collier, Nigel; Xu, Hua; Pham, Hoang Duy; Tu, Minh Phuong

2012-05-07

Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting. Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge. Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition.

Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

PubMed Central

Mohammad-Noori, Morteza; Beer, Michael A.

2014-01-01

Abstract Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. PMID:25033408

Enhanced regulatory sequence prediction using gapped k-mer features.

PubMed

Ghandi, Mahmoud; Lee, Dongwon; Mohammad-Noori, Morteza; Beer, Michael A

2014-07-01

Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem.

Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms.

PubMed

Chen, Hongming; Carlsson, Lars; Eriksson, Mats; Varkonyi, Peter; Norinder, Ulf; Nilsson, Ingemar

2013-06-24

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project.

Support vector machines for TEC seismo-ionospheric anomalies detection

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-02-01

Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs) are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC) time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011), Haiti (12 January 2010) and Samoa (29 September 2009). The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU) at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012) obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method to detect the novelty changes of a nonlinear time series such as variations of earthquake precursors.

A ranking method for the concurrent learning of compounds with various activity profiles.

PubMed

Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

2015-01-01

In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

A support vector machine based control application to the experimental three-tank system.

PubMed

Iplikci, Serdar

2010-07-01

This paper presents a support vector machine (SVM) approach to generalized predictive control (GPC) of multiple-input multiple-output (MIMO) nonlinear systems. The possession of higher generalization potential and at the same time avoidance of getting stuck into the local minima have motivated us to employ SVM algorithms for modeling MIMO systems. Based on the SVM model, detailed and compact formulations for calculating predictions and gradient information, which are used in the computation of the optimal control action, are given in the paper. The proposed MIMO SVM-based GPC method has been verified on an experimental three-tank liquid level control system. Experimental results have shown that the proposed method can handle the control task successfully for different reference trajectories. Moreover, a detailed discussion on data gathering, model selection and effects of the control parameters have been given in this paper. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Internet-Based Direct-to-Consumer Genetic Testing: A Systematic Review.

PubMed

Covolo, Loredana; Rubinelli, Sara; Ceretti, Elisabetta; Gelatti, Umberto

2015-12-14

Direct-to-consumer genetic tests (DTC-GT) are easily purchased through the Internet, independent of a physician referral or approval for testing, allowing the retrieval of genetic information outside the clinical context. There is a broad debate about the testing validity, their impact on individuals, and what people know and perceive about them. The aim of this review was to collect evidence on DTC-GT from a comprehensive perspective that unravels the complexity of the phenomenon. A systematic search was carried out through PubMed, Web of Knowledge, and Embase, in addition to Google Scholar according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist with the key term "Direct-to-consumer genetic test." In the final sample, 118 articles were identified. Articles were summarized in five categories according to their focus on (1) knowledge of, attitude toward use of, and perception of DTC-GT (n=37), (2) the impact of genetic risk information on users (n=37), (3) the opinion of health professionals (n=20), (4) the content of websites selling DTC-GT (n=16), and (5) the scientific evidence and clinical utility of the tests (n=14). Most of the articles analyzed the attitude, knowledge, and perception of DTC-GT, highlighting an interest in using DTC-GT, along with the need for a health care professional to help interpret the results. The articles investigating the content analysis of the websites selling these tests are in agreement that the information provided by the companies about genetic testing is not completely comprehensive for the consumer. Given that risk information can modify consumers' health behavior, there are surprisingly few studies carried out on actual consumers and they do not confirm the overall concerns on the possible impact of DTC-GT. Data from studies that investigate the quality of the tests offered confirm that they are not informative, have little predictive power, and do not measure genetic risk appropriately. The impact of DTC-GT on consumers' health perceptions and behaviors is an emerging concern. However, negative effects on consumers or health benefits have yet to be observed. Nevertheless, since the online market of DTC-GT is expected to grow, it is important to remain aware of a possible impact.

Prognostic significance of human pituitary tumor-transforming gene immunohistochemical expression in differentiated thyroid cancer.

PubMed

Sáez, Carmen; Martínez-Brocca, M Asunción; Castilla, Carolina; Soto, Alfonso; Navarro, Elena; Tortolero, María; Pintor-Toro, José A; Japón, Miguel A

2006-04-01

Human securin pituitary tumor-transforming gene (hPTTG) is overexpressed in a variety of primary neoplasias, including differentiated thyroid cancer (DTC). The objective of this study was to examine the immunohistochemical expression of hPTTG in DTC and its association with known prognostic factors. hPTTG expression was analyzed by immunostaining on paraffin-embedded tissues. Clinical data were used to determine any associations between the expression of hPTTG and prognostic variables of DTC. A median follow-up of 43 months allowed us to analyze the persistence of disease and the response to radioiodine therapy. The study was conducted at a tertiary university hospital. Ninety-five patients undergoing surgical resection for DTC (n = 60) or benign nodular thyroid disease (n = 35) were studied. The main outcome measure was the association between hPTTG expression and prognostic factors in DTC. Among DTC cases, 21 (35%) had low and 39 (65%) had high hPTTG immunostaining. Adjacent nonneoplastic thyroid tissue was largely unstained. Among benign nodular thyroid disease cases, immunostaining was detected focally in eight (22.8%). A significant association was found between hPTTG expression and the presence of nodal (P < 0.01) or distant metastases (P < 0.05). A significant association with TNM was also found, because 83.3% of advanced TNM stages showed elevated hPTTG (P < 0.05). The association between hPTTG overexpression and decreased radioiodine uptake during follow-up was also significant (P < 0.05). The expression levels of hPTTG were confirmed as an independent prognostic factor for persistent disease (relative risk, 3.0; 95% confidence interval, 1.1-8.7; P < 0.05). Immunohistochemical analysis of hPTTG is of potential value in the determination of tumor aggressiveness in DTC.

Association of fall history with the Timed Up and Go test score and the dual task cost: A cross-sectional study among independent community-dwelling older adults.

PubMed

Asai, Tsuyoshi; Oshima, Kensuke; Fukumoto, Yoshihiro; Yonezawa, Yuri; Matsuo, Asuka; Misu, Shogo

2018-05-21

To investigate the associations between fall history and the Timed Up and Go (TUG) test (single-TUG test), TUG test while counting aloud backwards from 100 (dual-TUG test) and the dual-task cost (DTC) among independent community-dwelling older adults. This cross-sectional study included 537 older adults who lived independently in the community. Data on fall history in the previous year were obtained by self-administrated questionnaire. The single- and dual-TUG tests were carried out, and the DTC value was computed from these results. Associations between fall history and these TUG-related values were analyzed using multivariate logistic regression models. The participants were divided into fall risk groups using the cut-off values of those significantly associated with falling, and the odds ratios (OR) were computed. Slower single-TUG test scores and lower DTC values were significantly associated with fall history after adjusting for potential confounders (single-TUG test score: OR 1.133, 95% CI 1.029-1.249; DTC value: OR 0.984, 95% CI 0.968-0.998). Older adults with slower single-TUG test scores and lower DTC values reported a fall history more often than those in other categories (OR compared with the lower-risk single-TUG and lower-risk DTC groups: 3.474, 95% CI 1.881-6.570). Slower single-TUG test scores and lower DTC values are associated with fall history among independent community-dwelling older adults. To some extent, dual task performance might provide added value for fall assessment, compared with administering the TUG test alone. Geriatr Gerontol Int 2018; ••: ••-••. © 2018 Japan Geriatrics Society.

Effects of Direct-to-Consumer Advertising and Clinical Guidelines on Appropriate Use of Human Papillomavirus DNA Tests

PubMed Central

2011-01-01

Background Both clinical guidelines and direct-to-consumer (DTC) advertising influence use of new health care technologies, but little is known about their relative effects. The introduction of a cervical cancer screening test in 2000 offered a unique opportunity to assess the two strategies. Objective To evaluate the effects of clinical guidelines and a targeted DTC advertising campaign on overall and appropriate use of human papillomavirus (HPV) DNA tests. Research Design Quasi-experimental study using difference-in-differences analysis. Data were MarketScan private insurance claims for 500,000 women ages 21 to 64 enrolled at least 12 consecutive months from January 2001 through December 2005. Results Both clinical guidelines and DTC advertising were associated with increases in overall HPV DNA test use. DTC advertising was associated with a statistically significant increase in HPV DNA test use in two groups of DTC cities (+5.57 percent, p<0.0001; +2.54 percent, p<0.0001). DTC advertising was associated with comparable increases in the probability of appropriate and inappropriate use of the HPV DNA test in primary screening. Clinical guideline releases from the American College of Obstetricians and Gynecologists, and by a co-sponsored panel, were associated with greater increases in HPV DNA tests for appropriate primary screening than for inappropriate primary screening (β=0.3347, p<0.05 and β=0.4175, p<0.01). Conclusions DTC advertising was associated with increased overall use of a cervical cancer screening test, while clinical guidelines were differentially associated with increased appropriate use. These findings suggest distinct influences of consumer marketing and professional guidelines on the use of health care products and services. PMID:21150798

Effects of direct-to-consumer advertising and clinical guidelines on appropriate use of human papillomavirus DNA tests.

PubMed

Price, Rebecca Anhang; Frank, Richard G; Cleary, Paul D; Goldie, Sue J

2011-02-01

Both clinical guidelines and direct-to-consumer (DTC) advertising influence the use of new health care technologies, but little is known about their relative effects. The introduction of a cervical cancer screening test in 2000 offered a unique opportunity to assess the 2 strategies. To evaluate the effects of clinical guidelines and a targeted DTC advertising campaign on overall and appropriate use of human papillomavirus (HPV) DNA tests. Quasi-experimental study using difference-in-differences analysis. Data were MarketScan private insurance claims for 500,000 women aged 21 to 64 enrolled at least 12 consecutive months from January 2001 through December 2005. Both clinical guidelines and DTC advertising were associated with increases in overall HPV DNA test use. DTC advertising was associated with a statistically significant increase in HPV DNA test use in 2 groups of DTC cities (+5.57%, P < 0.0001; +2.54%, P < 0.0001). DTC advertising was associated with comparable increases in the probability of appropriate and inappropriate use of the HPV DNA test in primary screening. Clinical guideline releases from the American College of Obstetricians and Gynecologists, and by a cosponsored panel, were associated with greater increases in HPV DNA tests for appropriate primary screening than for inappropriate primary screening (β = 0.3347, P < 0.05 and β = 0.4175, P < 0.01). DTC advertising was associated with increased overall use of a cervical cancer screening test, whereas clinical guidelines were differentially associated with increased appropriate use. These findings suggest distinct influences of consumer marketing and professional guidelines on the use of health care products and services.

Thyroid remnant ablation success and disease outcome in stage III or IV differentiated thyroid carcinoma: recombinant human thyrotropin versus thyroid hormone withdrawal.

PubMed

Vallejo Casas, Juan A; Mena Bares, Luisa M; Gálvez Moreno, Maria A; Moreno Ortega, Estefanía; Marlowe, Robert J; Maza Muret, Francisco R; Albalá González, María D

2016-06-01

Most publications to date compare outcomes after post-surgical thyroid remnant ablation stimulated by recombinant human thyrotropin (rhTSH) versus thyroid hormone withholding/withdrawal (THW) in low-recurrence risk differentiated thyroid carcinoma (DTC) patients. We sought to perform this comparison in high-risk patients. We retrospectively analyzed ~9-year single-center experience in 70 consecutive adults with initial UICC (Union for International Cancer Control) stage III/IV, M0 DTC undergoing rhTSH-aided (N.=54) or THW-aided (N.=16) high-activity ablation. Endpoints included ablation success and DTC outcome. Assessed ≥1 year post-ablation, ablation success comprised a) no visible scintigraphic thyroid bed uptake or pathological extra-thyroidal uptake; b) undetectable stimulated serum thyroglobulin (Tg) without interfering autoantibodies; c) both criteria. DTC outcome, determined at the latest visit, comprised either 1) "no evidence of disease" (NED): undetectable Tg, negative Tg autoantibodies, negative most recent whole-body scan, no suspicious findings clinically, on neck ultrasonography, or on other imaging; 2) persistent disease: failure to attain NED; or 3) recurrence: loss of NED. After the first ablative activity, ablation success by scintigraphic plus biochemical criteria was 64.8% in rhTSH patients, 56.3% in THW patients (P=NS). After 3.5-year versus 6.2-year median follow-up (P<0.05), DTC outcomes were NED, 85.2%, persistent disease, 13.0%, recurrence, 1.9%, in the rhTSH group and NED, 87.5%, persistent or recurrent disease, 6.3% each, in the THW group (P=NS). In patients with initial stage III/IV, M0 DTC, rhTSH-aided and THW-assisted ablation were associated with comparable remnant eradication or DTC cure rates.

NORDA’s Pattern Analysis Laboratory: Current Contributions to Naval Mapping, Charting, and Geodesy

DTIC Science & Technology

1989-04-01

magnetic observatories (McLeod, 1988). Using system integrates a suite of sensors and control devices the PAL’s VAX 11/780, spherical harmonic models to...DJAO:[FPS]*.OLB 5. Miscellaneous Utilities CALENDAR (NORDA events) 780 $ CALENDAR (menu-driven) DIALER modem controller 780 $ R AUTO DIAL:DIALER DTC...Utilities CALENDAR (NORDA events) 780 CALENDAR (menu-driven) DIALER modem controller 780 $ R AUTO DIAL:DIALER DTC Desk Top Calendar 780 $ DTC (menu-driven

Experimental Studies in a Reconfigurable C4 Test-bed for Network Enabled Capability

DTIC Science & Technology

2006-06-01

Cross1, Dr R. Houghton1, and Mr R. McMaster1 Defence Technology Centre for Human factors Integration (DTC HFI ) BITlab, School of Engineering and Design...NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Defence Technology Centre for Human factors Integration (DTC HFI ) BITlab, School of...studies into NEC by the Human Factors Integration Defence Technology Centre ( HFI -DTC). DEVELOPMENT OF THE TESTBED In brief, the C4 test-bed

The Low Backscattering Objects Classification in Polsar Image Based on Bag of Words Model Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Yang, L.; Shi, L.; Li, P.; Yang, J.; Zhao, L.; Zhao, B.

2018-04-01

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

State of play in direct-to-consumer genetic testing for lifestyle-related diseases: market, marketing content, user experiences and regulation.

PubMed

Saukko, Paula

2013-02-01

Direct-to-consumer (DTC) genetic tests have aroused controversy. Critics have argued many of the tests are not backed by scientific evidence, misguide their customers and should be regulated more stringently. Proponents suggest that finding out genetic susceptibilities for diseases could encourage healthier behaviours and makes the results of genetics research available to the public. This paper reviews the state of play in DTC genetic testing, focusing on tests identifying susceptibilities for lifestyle-related diseases. It will start with mapping the market for the tests. The paper will review (1) research on the content of the online marketing of DTC tests, (2) studies on the effects of DTC genetic tests on customers and (3) academic and policy proposals on how to regulate the tests. Current studies suggest that the marketing of DTC genetic tests often exaggerates their predictive powers, which could misguide consumers. However, research indicates that the tests do not seem to have major negative effects (worry and confusion) but neither do they engender positive effects (lifestyle change) on current users. Research on regulation of the tests has most commonly suggested regulating the marketing claims of the companies. In conclusion, the risks and benefits of DTC genetic tests are less significant than what has been predicted by critics and proponents, which will be argued reflects broader historical trends transforming health and medicine.

Performance of dithiocarbamate-type flocculant in treating simulated polymer flooding produced water.

PubMed

Gao, Baoyu; Jia, Yuyan; Zhang, Yongqiang; Li, Qian; Yue, Qinyan

2011-01-01

Produced water from polymer flooding is difficult to treat due to its high polymer concentration, high viscosity, and emulsified characteristics. The dithiocarbamate flocculant, DTC (T403), was prepared by the amine-terminated polyoxypropane-ether compound known as Jeffamine-T403. The product was characterized by IR spectra and elemental analysis. The DTC agent chelating with Fe2+ produced a network polymer matrix, which captured and removed oil droplets efficiently. Oil removal by the flocculent on simulated produced water with 0, 200, 500, 900 mg/L of partially hydrolyzed polyacrylamide (HPAM) was investigated for aspects of effectiveness of DTC (T403) dosage and concentrations of HPAM and Fe2+ ions in the wastewater. Results showed that HPAM had a negative influence on oil removal efficiency when DTC (T403) dosage was lower than 20 mg/L. However, residual oil concentrations in tested samples with different concentrations of HPAM all decreased below 10 mg/L when DTC (T403) dosage reached 30 mg/L. The concentration of Fe2+ in the initial wastewater had a slight effect on oil removal at the range of 2-12 mg/L. Results showed that Fe3+ could not be used in place of Fe2+ as Fe3+ could not react with DTC under flocculated conditions. The effects of mineral salts ions were also investigated.

Legislation on direct-to-consumer genetic testing in seven European countries.

PubMed

Borry, Pascal; van Hellemondt, Rachel E; Sprumont, Dominique; Jales, Camilla Fittipaldi Duarte; Rial-Sebbag, Emmanuelle; Spranger, Tade Matthias; Curren, Liam; Kaye, Jane; Nys, Herman; Howard, Heidi

2012-07-01

An increasing number of private companies are now offering direct-to-consumer (DTC) genetic testing services. Although a lot of attention has been devoted to the regulatory framework of DTC genetic testing services in the USA, only limited information about the regulatory framework in Europe is available. We will report on the situation with regard to the national legislation on DTC genetic testing in seven European countries (Belgium, the Netherlands, Switzerland, Portugal, France, Germany, the United Kingdom). The paper will address whether these countries have legislation that specifically address the issue of DTC genetic testing or have relevant laws that is pertinent to the regulatory control of these services in their countries. The findings show that France, Germany, Portugal and Switzerland have specific legislation that defines that genetic tests can only be carried out by a medical doctor after the provision of sufficient information concerning the nature, meaning and consequences of the genetic test and after the consent of the person concerned. In the Netherlands, some DTC genetic tests could fall under legislation that provides the Minister the right to refuse to provide a license to operate if a test is scientifically unsound, not in accordance with the professional medical practice standards or if the expected benefit is not in balance with the (potential) health risks. Belgium and the United Kingdom allow the provision of DTC genetic tests.

Methodological challenges surrounding direct-to-consumer advertising research--the measurement conundrum.

PubMed

Hansen, Richard A; Droege, Marcus

2005-06-01

Numerous studies have focused on the impact of direct-to-consumer (DTC) prescription drug advertising on consumer behavior and health outcomes. These studies have used various approaches to assess exposure to prescription drug advertising and to measure the subsequent effects of such advertisements. The objectives of this article are to (1) discuss measurement challenges involved in DTC advertising research, (2) summarize measurement approaches commonly identified in the literature, and (3) discuss contamination, time to action, and endogeneity as specific problems in measurement design and application. We conducted a review of the professional literature to identify illustrative approaches to advertising measurement. Specifically, our review of the literature focused on measurement of DTC advertising exposure and effect. We used the hierarchy-of-effects model to guide our discussion of processing and communication effects. Other effects were characterized as target audience action, sales, market share, and profit. Overall, existing studies have used a variety of approaches to measure advertising exposure and effect, yet the ability of measures to produce a valid and reliable understanding of the effects of DTC advertising can be improved. Our review provides a framework for conceptualizing DTC measurement, and can be used to identify gaps in the literature not sufficiently addressed by existing measures. Researchers should continue to explore correlations between exposure and effect of DTC advertising, but are obliged to improve and validate measurement in this area.

Lesion symptom map of cognitive-postural interference in multiple sclerosis.

PubMed

Ruggieri, Serena; Fanelli, Fulvia; Castelli, Letizia; Petsas, Nikolaos; De Giglio, Laura; Prosperini, Luca

2018-04-01

To investigate the disease-altered structure-function relationship underlying the cognitive-postural interference (CPI) phenomenon in multiple sclerosis (MS). We measured postural sway of 96 patients and 48 sex-/age-matched healthy controls by force platform in quiet standing (single-task (ST)) while performing the Stroop test (dual-task (DT)) to estimate the dual-task cost (DTC) of balance. In patient group, binary T2 and T1 lesion masks and their corresponding lesion volumes were obtained from magnetic resonance imaging (MRI) of brain. Normalized brain volume (NBV) was also estimated by SIENAX. Correlations between DTC and lesion location were determined by voxel-based lesion symptom mapping (VLSM) analyses. Patients had greater DTC than controls ( p < 0.001). Among whole brain MRI metrics, only T1 lesion volume correlated with DTC ( r = -0.27; p < 0.01). However, VLSM analysis did not reveal any association with DTC using T1 lesion masks. By contrast, we found clusters of T2 lesions in distinct anatomical regions (anterior and superior corona radiata, bilaterally) to be correlated with DTC ( p < 0.01 false discovery rate (FDR)-corrected). A multivariable stepwise regression model confirmed findings from VLSM analysis. NBV did not contribute to fit the model. Our findings suggest that the CPI phenomenon in MS can be explained by disconnection along specific areas implicated in task-switching abilities and divided attention.

Legislation on direct-to-consumer genetic testing in seven European countries

PubMed Central

Borry, Pascal; van Hellemondt, Rachel E; Sprumont, Dominique; Jales, Camilla Fittipaldi Duarte; Rial-Sebbag, Emmanuelle; Spranger, Tade Matthias; Curren, Liam; Kaye, Jane; Nys, Herman; Howard, Heidi

2012-01-01

An increasing number of private companies are now offering direct-to-consumer (DTC) genetic testing services. Although a lot of attention has been devoted to the regulatory framework of DTC genetic testing services in the USA, only limited information about the regulatory framework in Europe is available. We will report on the situation with regard to the national legislation on DTC genetic testing in seven European countries (Belgium, the Netherlands, Switzerland, Portugal, France, Germany, the United Kingdom). The paper will address whether these countries have legislation that specifically address the issue of DTC genetic testing or have relevant laws that is pertinent to the regulatory control of these services in their countries. The findings show that France, Germany, Portugal and Switzerland have specific legislation that defines that genetic tests can only be carried out by a medical doctor after the provision of sufficient information concerning the nature, meaning and consequences of the genetic test and after the consent of the person concerned. In the Netherlands, some DTC genetic tests could fall under legislation that provides the Minister the right to refuse to provide a license to operate if a test is scientifically unsound, not in accordance with the professional medical practice standards or if the expected benefit is not in balance with the (potential) health risks. Belgium and the United Kingdom allow the provision of DTC genetic tests. PMID:22274578

Potential of cancer screening with serum surface-enhanced Raman spectroscopy and a support vector machine

NASA Astrophysics Data System (ADS)

Li, S. X.; Zhang, Y. J.; Zeng, Q. Y.; Li, L. F.; Guo, Z. Y.; Liu, Z. M.; Xiong, H. L.; Liu, S. H.

2014-06-01

Cancer is the most common disease to threaten human health. The ability to screen individuals with malignant tumours with only a blood sample would be greatly advantageous to early diagnosis and intervention. This study explores the possibility of discriminating between cancer patients and normal subjects with serum surface-enhanced Raman spectroscopy (SERS) and a support vector machine (SVM) through a peripheral blood sample. A total of 130 blood samples were obtained from patients with liver cancer, colonic cancer, esophageal cancer, nasopharyngeal cancer, gastric cancer, as well as 113 blood samples from normal volunteers. Several diagnostic models were built with the serum SERS spectra using SVM and principal component analysis (PCA) techniques. The results show that a diagnostic accuracy of 85.5% is acquired with a PCA algorithm, while a diagnostic accuracy of 95.8% is obtained using radial basis function (RBF), PCA-SVM methods. The results prove that a RBF kernel PCA-SVM technique is superior to PCA and conventional SVM (C-SVM) algorithms in classification serum SERS spectra. The study demonstrates that serum SERS, in combination with SVM techniques, has great potential for screening cancerous patients with any solid malignant tumour through a peripheral blood sample.

Improving consensus contact prediction via server correlation reduction.

PubMed

Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

2009-05-06

Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing.

PubMed

Weis, Christian; Hess, Andreas; Budinsky, Lubos; Fabry, Ben

2015-01-01

The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI) in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM) based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm3, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

Signal peptide discrimination and cleavage site identification using SVM and NN.

PubMed

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

Racial minority group interest in direct-to-consumer genetic testing: findings from the PGen study.

PubMed

Landry, Latrice; Nielsen, Daiva Elena; Carere, Deanna Alexis; Roberts, J Scott; Green, Robert C

2017-10-01

There is little information regarding direct-to-consumer (DTC) personal genetic testing (PGT) in non-White racial minorities. Using a web-based survey, we compared the pretest interests and attitudes toward DTC-PGT of racial minority and White DTC-PGT customers of 23andMe and Pathway Genomics using chi-square tests and multinomial regression. Data were available for 1487 participants (1389 White, 44 Black, and 54 Asian). Survey responses were similar across racial groups, although a greater proportion of Blacks compared to Whites reported being "very interested" in genetic information related to traits (91.9 vs. 70.8%, p = 0.009). A greater proportion of Asians compared to Whites reported that a "very important" consideration for pursuing DTC-PGT was limited information about their family health history (58.0 vs. 37.5%, p = 0.002). While a number of significant differences between groups were observed in unadjusted analyses, they did not remain significant after adjustment. This study provides a preliminary view of the interests for purchasing DTC-PGT among customers with racial minority backgrounds.

Direct-to-consumer genetic testing: good, bad or benign?

PubMed

Caulfield, T; Ries, N M; Ray, P N; Shuman, C; Wilson, B

2010-02-01

A wide variety of genetic tests are now being marketed and sold in direct-to-consumer (DTC) commercial transactions. However, risk information revealed through many DTC testing services, especially those based on emerging genome wide-association studies, has limited predictive value for consumers. Some commentators contend that tests are being marketed prematurely, while others support rapid translation of genetic research findings to the marketplace. The potential harms and benefits of DTC access to genetic testing are not yet well understood, but some large-scale studies have recently been launched to examine how consumers understand and use genetic risk information. Greater consumer access to genetic tests creates a need for continuing education for health care professionals so they can respond to patients' inquiries about the benefits, risks and limitations of DTC services. Governmental bodies in many jurisdictions are considering options for regulating practices of DTC genetic testing companies, particularly to govern quality of commercial genetic tests and ensure fair and truthful advertising. Intersectoral initiatives involving government regulators, professional bodies and industry are important to facilitate development of standards to govern this rapidly developing area of personalized genomic commerce.

'Only a click away - DTC genetics for ancestry, health, love…and more: A view of the business and regulatory landscape'.

PubMed

Phillips, Andelka M

2016-03-01

I provide an overview of the current state of the direct-to-consumer (DTC) genetic testing industry and the challenges that different types of testing pose for regulation. I consider the variety of services currently available. These range from health and ancestry tests to those for child talent, paternity, and infidelity. In light of the increasingly blurred lines among different categories of testing, I call for a broader discussion of DTC governance. I stress the importance of shifting our attention from the activities of the most prominent companies to viewing DTC genetics as an industry with a wide spectrum of services and raising a wide variety of issues. These issues go beyond questions of clinical utility and validity to those of data security, personal identity, race, and the nature of the family. Robust DTC testing has the power to provide meaningful clinical, genealogical and even forensic information to those who want it; in unscrupulous hands, however, it also has the power to deceive and exploit. I consider approaches to help ensure the former and minimize the latter.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Current ethical and legal issues in health-related direct-to-consumer genetic testing.

PubMed

Niemiec, Emilia; Kalokairinou, Louiza; Howard, Heidi Carmen

2017-09-01

A variety of health-related genetic testing is currently advertized directly to consumers. This article provides a timely overview of direct-to-consumer genetic testing (DTC GT) and salient ethical issues, as well as an analysis of the impact of the recently adopted regulation on in vitro diagnostic medical devices on DTC GT. DTC GT companies currently employ new testing approaches, report on a wide spectrum of conditions and target new groups of consumers. Such activities raise ethical issues including the questionable analytic and clinical validity of tests, the adequacy of informed consent, potentially misleading advertizing, testing in children, research uses and commercialization of genomic data. The recently adopted regulation on in vitro diagnostic medical devices may limit the offers of predisposition DTC GT in the EU market.

Effects of iodine-131 radiotherapy on Th17/Tc17 and Treg/Th17 cells of patients with differentiated thyroid carcinoma.

PubMed

Zhang, Lixia; Chen, Jinyan; Xu, Caiyun; Qi, Lili; Ren, Yan

2018-03-01

T helper 17 (Th17), T cytotoxic 17 (Tc17) and regulatory T (Treg) cells serve important roles in a number of inflammatory and autoimmune diseases. The aim of the present study was to examine the distribution of Th17, Tc17 and Treg cells in patients with differentiated thyroid cancer (DTC) prior to as well as 7, 30 and 90 days following radioactive iodine-131 ( 131 I) therapy, and to elucidate the probable effects of 131 I therapy on Th17/Tc17 and Treg/Th17 cells in patients with DTC. A total of 40 patients with DTC (26 female; 14 male) between the ages of 24 and 72 years, as well as 13 age- and sex-matched healthy subjects were included in this study. The number of Th17, Tc17 and Treg cells in the peripheral blood of patients with DTC and of healthy Controls were assessed by flow cytometry. Th17 and Tc17 cells were counted as percentages of the number of CD3 + T cells; Treg cells were counted as a percentage of the number of CD4 + T cells. In addition, the serum levels of interleukin (IL)-17, IL-23, IL-10 and transforming growth factor (TGF)-β1 were examined by ELISA. The frequencies of Th17, Tc17 and Treg cells, as well as the serum levels of IL-17, IL-23, IL-10 and TGF-β1 were significantly elevated in patients with DTC compared with healthy Controls, whereas 131 I therapy significantly decreased them. In addition, elevated Th17/Tc17 ratio and reduced Treg/Th17 ratio were observed in patients with DTC at day 0, however, these ratios returned to normal levels following 131 I therapy for 90 days as compared with healthy Controls. Notably, Th17/Tc17 and Treg/Th17 ratios varied following 131 I therapy for 7 and 30 days. In addition, a strong positive correlation between Th17 and Tc17 cells was observed in the healthy Controls and patients with DTC that received 131 I treatment for 90 days, whereas a weak positive correlation between Th17 and Treg cell levels was identified in the healthy Controls and no obvious correlation between Th17 and Treg cells was observed in all patients with DTC pre- and post- 131 I therapy during the entire treatment period. These data suggested a significant involvement of Th17, Tc17 and Treg cells in the pathology of DTC. Restoring the balance of these cells may contribute to the recovery of patients with DTC following 131 I therapy.

Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields.

PubMed

Artan, Yusuf; Haider, Masoom A; Langer, Deanna L; van der Kwast, Theodorus H; Evans, Andrew J; Yang, Yongyi; Wernick, Miles N; Trachtenberg, John; Yetik, Imam Samil

2010-09-01

Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.

Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

NASA Astrophysics Data System (ADS)

Wang, Danshi; Zhang, Min; Cai, Zhongle; Cui, Yue; Li, Ze; Han, Huanhuan; Fu, Meixia; Luo, Bin

2016-06-01

An effective machine learning algorithm, the support vector machine (SVM), is presented in the context of a coherent optical transmission system. As a classifier, the SVM can create nonlinear decision boundaries to mitigate the distortions caused by nonlinear phase noise (NLPN). Without any prior information or heuristic assumptions, the SVM can learn and capture the link properties from only a few training data. Compared with the maximum likelihood estimation (MLE) algorithm, a lower bit-error rate (BER) is achieved by the SVM for a given launch power; moreover, the launch power dynamic range (LPDR) is increased by 3.3 dBm for 8 phase-shift keying (8 PSK), 1.2 dBm for QPSK, and 0.3 dBm for BPSK. The maximum transmission distance corresponding to a BER of 1 ×10-3 is increased by 480 km for the case of 8 PSK. The larger launch power range and longer transmission distance improve the tolerance to amplitude and phase noise, which demonstrates the feasibility of the SVM in digital signal processing for M-PSK formats. Meanwhile, in order to apply the SVM method to 16 quadratic amplitude modulation (16 QAM) detection, we propose a parameter optimization scheme. By utilizing a cross-validation and grid-search techniques, the optimal parameters of SVM can be selected, thus leading to the LPDR improvement by 2.8 dBm. Additionally, we demonstrate that the SVM is also effective in combating the laser phase noise combined with the inphase and quadrature (I/Q) modulator imperfections, but the improvement is insignificant for the linear noise and separate I/Q imbalance. The computational complexity of SVM is also discussed. The relatively low complexity makes it possible for SVM to implement the real-time processing.

NOTE: Fluoroscopic gating without implanted fiducial markers for lung cancer radiotherapy based on support vector machines

NASA Astrophysics Data System (ADS)

Cui, Ying; Dy, Jennifer G.; Alexander, Brian; Jiang, Steve B.

2008-08-01

Various problems with the current state-of-the-art techniques for gated radiotherapy have prevented this new treatment modality from being widely implemented in clinical routine. These problems are caused mainly by applying various external respiratory surrogates. There might be large uncertainties in deriving the tumor position from external respiratory surrogates. While tracking implanted fiducial markers has sufficient accuracy, this procedure may not be widely accepted due to the risk of pneumothorax. Previously, we have developed a technique to generate gating signals from fluoroscopic images without implanted fiducial markers using template matching methods (Berbeco et al 2005 Phys. Med. Biol. 50 4481-90, Cui et al 2007b Phys. Med. Biol. 52 741-55). In this note, our main contribution is to provide a totally different new view of the gating problem by recasting it as a classification problem. Then, we solve this classification problem by a well-studied powerful classification method called a support vector machine (SVM). Note that the goal of an automated gating tool is to decide when to turn the beam ON or OFF. We treat ON and OFF as the two classes in our classification problem. We create our labeled training data during the patient setup session by utilizing the reference gating signal, manually determined by a radiation oncologist. We then pre-process these labeled training images and build our SVM prediction model. During treatment delivery, fluoroscopic images are continuously acquired, pre-processed and sent as an input to the SVM. Finally, our SVM model will output the predicted labels as gating signals. We test the proposed technique on five sequences of fluoroscopic images from five lung cancer patients against the reference gating signal as ground truth. We compare the performance of the SVM to our previous template matching method (Cui et al 2007b Phys. Med. Biol. 52 741-55). We find that the SVM is slightly more accurate on average (1-3%) than the template matching method, when delivering the target dose. And the average duty cycle is 4-6% longer. Given the very limited patient dataset, we cannot conclude that the SVM is more accurate and efficient than the template matching method. However, our preliminary results show that the SVM is a potentially precise and efficient algorithm for generating gating signals for radiotherapy. This work demonstrates that the gating problem can be considered as a classification problem and solved accordingly.

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

NASA Astrophysics Data System (ADS)

Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

NASA Astrophysics Data System (ADS)

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

2014-05-01

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

PubMed

Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

2014-10-01

Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

Comparison of ANN and SVM for classification of eye movements in EOG signals

NASA Astrophysics Data System (ADS)

Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

Classification of burst and suppression in the neonatal electroencephalogram

NASA Astrophysics Data System (ADS)

Löfhede, J.; Löfgren, N.; Thordstein, M.; Flisberg, A.; Kjellmer, I.; Lindecrantz, K.

2008-12-01

Fisher's linear discriminant (FLD), a feed-forward artificial neural network (ANN) and a support vector machine (SVM) were compared with respect to their ability to distinguish bursts from suppressions in electroencephalograms (EEG) displaying a burst-suppression pattern. Five features extracted from the EEG were used as inputs. The study was based on EEG signals from six full-term infants who had suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as the area under the curve (AUC), derived from receiver operating characteristic (ROC) curves for the three methods. Based on this, the SVM performs slightly better than the others. Testing the three methods with combinations of increasing numbers of the five features shows that the SVM handles the increasing amount of information better than the other methods.

Extracting physicochemical features to predict protein secondary structure.

PubMed

Huang, Yin-Fu; Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.

Extracting Physicochemical Features to Predict Protein Secondary Structure

PubMed Central

Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances. PMID:23766688

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression

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

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

2013-01-01

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

The dynamic financial distress prediction method of EBW-VSTW-SVM

NASA Astrophysics Data System (ADS)

Sun, Jie; Li, Hui; Chang, Pei-Chann; He, Kai-Yu

2016-07-01

Financial distress prediction (FDP) takes important role in corporate financial risk management. Most of former researches in this field tried to construct effective static FDP (SFDP) models that are difficult to be embedded into enterprise information systems, because they are based on horizontal data-sets collected outside the modelling enterprise by defining the financial distress as the absolute conditions such as bankruptcy or insolvency. This paper attempts to propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise's vertical sliding time window (VSTW). The dynamic FDP (DFDP) method is named EBW-VSTW-SVM, which keeps updating the FDP model dynamically with time goes on and only needs the historic financial data of the modelling enterprise itself and thus is easier to be embedded into enterprise information systems. The DFDP method of EBW-VSTW-SVM consists of four steps, namely evaluation of vertical relative financial distress (VRFD) based on EBW, construction of training data-set for DFDP modelling according to VSTW, training of DFDP model based on SVM and DFDP for the future time point. We carry out case studies for two listed pharmaceutical companies and experimental analysis for some other companies to simulate the sliding of enterprise vertical time window. The results indicated that the proposed approach was feasible and efficient to help managers improve corporate financial management.

Effects of direct-to-consumer advertising of hydroxymethylglutaryl coenzyme a reductase inhibitors on attainment of LDL-C goals.

PubMed

Bradford, W David; Kleit, Andrew N; Nietert, Paul J; Ornstein, Steven

2006-12-01

Although highly controversial, directto-consumer (DTC) television advertising for prescription drugs is an established practice in the US health care industry. While the US Food and Drug Administration is currently reexamining its regulatory stance, little evidence exists regarding the impact of DTC advertising on patient health outcomes. The objective of this research was to study the relationship between heavy television promotion of 3 major hydroxymethylglutaryl coenzyme A reductase inhibitors ("statins") and the frequency with which patients are able to attain low-density lipoprotein cholesterol (LDL-C) blood-level goals after treatment with any statin. We used logistic regression to determine achievement of LDL-C goals at 6 months after statin treatment, using electronic medical record extract data from patients from geographically dispersed primary care practices in the United States. We identified LDL-C blood levels as being at or less than goal, as defined by risk-adjusted guidelines published by the National Heart, Lung, and Blood Institute from the Adult Treatment Panel III (ATP III) data. A total of 50,741 patients, identified from 88 practices, were diagnosed with hyperlipidemia and had begun therapy with any statin medication during the 1998-2004 time period. In addition, total dollars spent each month on television advertising at the national and local levels for atorvastatin, pravastatin, and simvastatin were obtained. DTC advertising data were merged by local media market where the physician practice was located and by the month in which the patient was first prescribed a statin. The models were run for all patients who initiated therapy, and also on a subsample of patients who continued to receive prescriptions for the drugs for at least 6 months. Logistic regressions were used to predict the likelihood that each patient attained the ATP III LDL-C blood-level goals as a function of DTC advertising and other factors. High levels of national DTC advertising when therapy was initiated were found to increase the likelihood that patients attained LDL-C goals at 6 months by 6% (P < 0.001)-although the effect was concentrated among patients with the least-restrictive ATP III LDL-C goals (

75 FR 10539 - Self-Regulatory Organizations; The Depository Trust Company; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-08

... SECURITIES AND EXCHANGE COMMISSION [Release No. 34-61620; File No. SR-DTC-2010-04] Self-Regulatory... Change To Modify Its Registered Transfer Agent Notification Methods for Assumption or Termination of... transfer agent notification methods for assumption or termination of services. II. Self-Regulatory...

Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

PubMed Central

Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

2013-01-01

The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method. PMID:24351629

Online least squares one-class support vector machines-based abnormal visual event detection.

PubMed

Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

2013-12-12

The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Markerless gating for lung cancer radiotherapy based on machine learning techniques

NASA Astrophysics Data System (ADS)

Lin, Tong; Li, Ruijiang; Tang, Xiaoli; Dy, Jennifer G.; Jiang, Steve B.

2009-03-01

In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks—ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

Age differences in how consumers behave following exposure to DTC advertising.

PubMed

DeLorme, Denise E; Huh, Jisu; Reid, Leonard N

2006-01-01

This study was conducted to provide additional evidence on how consumers behave following direct-to-consumer (DTC) advertising exposure and to determine if there are differences in ad-prompted acts (drug inquiry and drug requests) between different age groups (i.e., older, mature, and younger adults). The results suggest that younger, mature, and older consumers are all moved to act by DTC drug ads, but that each age group behaves in different ways. Somewhat surprisingly, age was not predictive of ad-prompted behavior. DTC advertising was no more effective at moving older consumers to behave than their younger counterparts. These results suggest that age does not matter that much when it comes to the "moving power" of prescription drug advertising, even though research indicates that older consumers are more vulnerable to the persuasive effects of communication.

FDA direct-to-consumer advertising for prescription drugs: what are consumer preferences and response tendencies?

PubMed

Khanfar, Nile; Loudon, David; Sircar-Ramsewak, Feroza

2007-01-01

The effect of direct-to-consumer (DTC) television advertising of prescription medications is a growing concern of the United States (U.S.) Congress, state legislatures, and the Food and Drug Administration (FDA). This research study was conducted in order to examine consumers' perceived preferences of DTC television advertisement in relation to "reminder" "help-seeking," and "product-claim" FDA-approved advertisement categories. An additional objective was to examine the influence of DTC television advertising of prescription drugs on consumers' tendency to seek more information about the medication and/or the medical condition. The research indicates that DTC television drug ads appear to be insufficient for consumers to make informed decisions. Their mixed perception and acceptance of the advertisements seem to influence them to seek more information from a variety of medical sources.

Attitudes of cystic fibrosis patients and their parents towards direct-to-consumer genetic testing for carrier status.

PubMed

Janssens, Sandra; Kalokairinou, Louiza; Chokoshvilli, Davit; Binst, Carmen; Mahieu, Inge; Henneman, Lidewij; De Paepe, Anne; Borry, Pascal

2015-03-01

An increasing number of direct-to-consumer (DTC) genetic testing companies have started offering tests for carrier status of autosomal recessive disorders. A written questionnaire was administered to 47 patients and 65 parents of children with Cystic Fibrosis (CF), a common severe autosomal recessive disorder, to assess their views about the offer of DTC carrier tests. All participants were recruited from a CF patient registry in Belgium. We found that very few patients and parents were aware of the offer of DTC genetic testing for carrier status, and were generally skeptical. A strong preference for the healthcare system over commercial companies as the provider of the test was observed. However, many participants believe people should have a right to access DTC genetic tests provided by commercial companies.

Direct-to-consumer genetic testing in Slovenia: availability, ethical dilemmas and legislation.

PubMed

Vrecar, Irena; Peterlin, Borut; Teran, Natasa; Lovrecic, Luca

2015-01-01

Over the last few years, many private companies are advertising direct-to-consumer genetic testing (DTC GT), mostly with no or only minor clinical utility and validity of tests and without genetic counselling. International professional community does not approve provision of DTC GT and situation in some EU countries has been analysed already. The aim of our study was to analyse current situation in the field of DTC GT in Slovenia and related legal and ethical issues. Information was retrieved through internet search, performed independently by two authors, structured according to individual private company and the types of offered genetic testing. Five private companies and three Health Insurance Companies offer DTC GT and it is provided without genetic counselling. Available tests include testing for breast cancer, tests with other health-related information (complex diseases, drug responses) and other tests (nutrigenetic, ancestry, paternity). National legislation is currently being developed and Council of Experts in Medical Genetics has issued an opinion about Genetic Testing and Commercialization of Genetic Tests in Slovenia. Despite the fact that Slovenia has signed the Additional protocol to the convention on human rights and biomedicine, concerning genetic testing for health purposes, DTC GT in Slovenia is present and against all international recommendations. There is lack of or no medical supervision, clinical validity and utility of tests and inappropriate genetic testing of minors is available. There is urgent need for regulation of ethical, legal, and social aspects. National legislation on DTC GT is being prepared.

Steganography anomaly detection using simple one-class classification

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M.; Peterson, Gilbert L.; Agaian, Sos S.

2007-04-01

There are several security issues tied to multimedia when implementing the various applications in the cellular phone and wireless industry. One primary concern is the potential ease of implementing a steganography system. Traditionally, the only mechanism to embed information into a media file has been with a desktop computer. However, as the cellular phone and wireless industry matures, it becomes much simpler for the same techniques to be performed using a cell phone. In this paper, two methods are compared that classify cell phone images as either an anomaly or clean, where a clean image is one in which no alterations have been made and an anomalous image is one in which information has been hidden within the image. An image in which information has been hidden is known as a stego image. The main concern in detecting steganographic content with machine learning using cell phone images is in training specific embedding procedures to determine if the method has been used to generate a stego image. This leads to a possible flaw in the system when the learned model of stego is faced with a new stego method which doesn't match the existing model. The proposed solution to this problem is to develop systems that detect steganography as anomalies, making the embedding method irrelevant in detection. Two applicable classification methods for solving the anomaly detection of steganographic content problem are single class support vector machines (SVM) and Parzen-window. Empirical comparison of the two approaches shows that Parzen-window outperforms the single class SVM most likely due to the fact that Parzen-window generalizes less.

Prediction on sunspot activity based on fuzzy information granulation and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

PubMed

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

Diagnostic value of Tg and TgAb for metastasis following ablation in patients with differentiated thyroid carcinoma coexistent with Hashimoto thyroiditis.

PubMed

Chai, Hong; Zhu, Zhao-Jin; Chen, Ze-Quan; Yu, Yong-Li

2016-08-01

This study was designed to investigate the clinical value of serum thyroglobulin (Tg) and antithyroglobulin antibody (TgAb) measurements and the cutoff value after ablation in differentiated thyroid carcinoma (DTC) complicated by Hashimoto thyroiditis (HT) with metastasis. We measured serum Tg and TgAb levels and evaluated the disease status in 164 cases of DTC coexistent with HT in pathologically confirmed patients after surgery and post-remnant ablation during a 3-year follow-up. All Tg and TgAb levels were assessed by chemiluminescent immunoassay (IMA). Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic value of Tg and TgAb for disease metastasis. The relationship between Tg and TgAb was analyzed using the scatter diagram distribution method. We found that the cutoff values of Tg and TgAb were 1.48 µg/L and 45 kIU/L, respectively. The area under the ROC curve (AUC) of Tg and TgAb was 0.907 and 0.650, respectively. In DTC coexistent with HT patients, the optimal cutoff value correlated with metastasis in Tg and TgAb was 1.48 µg/L and 45 kIU/L, respectively.

Support vector regression methodology for estimating global solar radiation in Algeria

NASA Astrophysics Data System (ADS)

Guermoui, Mawloud; Rabehi, Abdelaziz; Gairaa, Kacem; Benkaciali, Said

2018-01-01

Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaïa city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (r2), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

‘Only a click away — DTC genetics for ancestry, health, love…and more: A view of the business and regulatory landscape’

PubMed Central

Phillips, Andelka M.

2016-01-01

I provide an overview of the current state of the direct-to-consumer (DTC) genetic testing industry and the challenges that different types of testing pose for regulation. I consider the variety of services currently available. These range from health and ancestry tests to those for child talent, paternity, and infidelity. In light of the increasingly blurred lines among different categories of testing, I call for a broader discussion of DTC governance. I stress the importance of shifting our attention from the activities of the most prominent companies to viewing DTC genetics as an industry with a wide spectrum of services and raising a wide variety of issues. These issues go beyond questions of clinical utility and validity to those of data security, personal identity, race, and the nature of the family. Robust DTC testing has the power to provide meaningful clinical, genealogical and even forensic information to those who want it; in unscrupulous hands, however, it also has the power to deceive and exploit. I consider approaches to help ensure the former and minimize the latter. PMID:27047755

Is there a doctor in the house? : The presence of physicians in the direct-to-consumer genetic testing context.

PubMed

Howard, Heidi Carmen; Borry, Pascal

2012-04-01

Over the last couple of years, many commercial companies, the majority of which are based in the USA, have been advertising and offering direct-to-consumer (DTC) genetic testing services outside of the established health care system, and often without any involvement from a health care professional. In the last year, however, a number of DTC genetic testing companies have changed their provision model such that consumers must now contact a health care professional before being able to order the genetic testing service. In discussing the advent of this new model of service provision, this article also reviews the ethical and social issues surrounding DTC genetic testing and addresses the potential motivations for change, some barriers to achieving truly appropriate medical supervision and the present reality of DTC genetic testing for some psychiatric and neurological disorders. Since the advent of these commercial activities, critics have pointed a finger at the lack of medical supervision surrounding these services. The discussion herein, however, reveals how difficult it may be, despite the addition of a physician, to actually achieve adequate medical supervision within the present context of DTC genetic testing.

Direct-to-consumer personalized genomic testing

PubMed Central

Bloss, Cinnamon S.; Darst, Burcu F.; Topol, Eric J.; Schork, Nicholas J.

2011-01-01

Over the past 18 months, there have been notable developments in the direct-to-consumer (DTC) genomic testing arena, in particular with regard to issues surrounding governmental regulation in the USA. While commentaries continue to proliferate on this topic, actual empirical research remains relatively scant. In terms of DTC genomic testing for disease susceptibility, most of the research has centered on uptake, perceptions and attitudes toward testing among health care professionals and consumers. Only a few available studies have examined actual behavioral response among consumers, and we are not aware of any studies that have examined response to DTC genetic testing for ancestry or for drug response. We propose that further research in this area is desperately needed, despite challenges in designing appropriate studies given the rapid pace at which the field is evolving. Ultimately, DTC genomic testing for common markers and conditions is only a precursor to the eventual cost-effectiveness and wide availability of whole genome sequencing of individuals, although it remains unclear whether DTC genomic information will still be attainable. Either way, however, current knowledge needs to be extended and enhanced with respect to the delivery, impact and use of increasingly accurate and comprehensive individualized genomic data. PMID:21828075

Calculation of Blood Dose in Patients Treated With 131I Using MIRD, Imaging, and Blood Sampling Methods

PubMed Central

Piruzan, Elham; Haghighatafshar, Mahdi; Faghihi, Reza; Entezarmahdi, Seyed Mohammad

2016-01-01

Abstract Radioiodine therapy is known as the most effective treatment of differentiated thyroid carcinoma (DTC) to ablate remnant thyroid tissue after surgery. In patients with DTC treated with radioiodine, internal radiation dosimetry of radioiodine is useful for radiation risk assessment. The aim of this study is to describe a method to estimate the absorbed dose to the blood using medical internal radiation dosimetry methods. In this study, 23 patients with DTC with different administrated activities, 3.7, 4.62, and 5.55 GBq after thyroidectomy, were randomly selected. Blood dosimetry of treated patients was performed with external whole body counting using a dual-head gamma camera imaging device and also with blood sample activity measurements using a dose calibrator. Absorbed dose to the blood was measured at 2, 6, 12, 24, 48, and 96 hours after the administration of radioiodine with the 2 methods. Based on the results of whole body counting and blood sample activity dose rate measurements, 96 hours after administration of 3.7, 4.62, and 5.55 GBq of radioiodine, absorbed doses to patients’ blood were 0.65 ± 0.20, 0.67 ± 0.18, 0.79 ± 0.51 Gy, respectively. Increasing radioiodine activity from 3.7 to 5.55 GBq increased blood dose significantly, while there was no significant difference in blood dose between radioiodine dosages of 3.7 and 4.62 GBq. Our results revealed a significant correlation between the blood absorbed dose and blood sample activity and between the blood absorbed dose and whole body counts 24 to 48 hours after the administration of radioiodine. PMID:26986171

Calculation of Blood Dose in Patients Treated With 131I Using MIRD, Imaging, and Blood Sampling Methods.

PubMed

Piruzan, Elham; Haghighatafshar, Mahdi; Faghihi, Reza; Entezarmahdi, Seyed Mohammad

2016-03-01

Radioiodine therapy is known as the most effective treatment of differentiated thyroid carcinoma (DTC) to ablate remnant thyroid tissue after surgery. In patients with DTC treated with radioiodine, internal radiation dosimetry of radioiodine is useful for radiation risk assessment. The aim of this study is to describe a method to estimate the absorbed dose to the blood using medical internal radiation dosimetry methods. In this study, 23 patients with DTC with different administrated activities, 3.7, 4.62, and 5.55 GBq after thyroidectomy, were randomly selected. Blood dosimetry of treated patients was performed with external whole body counting using a dual-head gamma camera imaging device and also with blood sample activity measurements using a dose calibrator. Absorbed dose to the blood was measured at 2, 6, 12, 24, 48, and 96 hours after the administration of radioiodine with the 2 methods. Based on the results of whole body counting and blood sample activity dose rate measurements, 96 hours after administration of 3.7, 4.62, and 5.55 GBq of radioiodine, absorbed doses to patients' blood were 0.65 ± 0.20, 0.67 ± 0.18, 0.79 ± 0.51 Gy, respectively. Increasing radioiodine activity from 3.7 to 5.55 GBq increased blood dose significantly, while there was no significant difference in blood dose between radioiodine dosages of 3.7 and 4.62 GBq. Our results revealed a significant correlation between the blood absorbed dose and blood sample activity and between the blood absorbed dose and whole body counts 24 to 48 hours after the administration of radioiodine.

Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran

NASA Astrophysics Data System (ADS)

Mahvash Mohammadi, Neda; Hezarkhani, Ardeshir

2018-07-01

Classification of mineralised zones is an important factor for the analysis of economic deposits. In this paper, the support vector machine (SVM), a supervised learning algorithm, based on subsurface data is proposed for classification of mineralised zones in the Takht-e-Gonbad porphyry Cu-deposit (SE Iran). The effects of the input features are evaluated via calculating the accuracy rates on the SVM performance. Ultimately, the SVM model, is developed based on input features namely lithology, alteration, mineralisation, the level and, radial basis function (RBF) as a kernel function. Moreover, the optimal amount of parameters λ and C, using n-fold cross-validation method, are calculated at level 0.001 and 0.01 respectively. The accuracy of this model is 0.931 for classification of mineralised zones in the Takht-e-Gonbad porphyry deposit. The results of the study confirm the efficiency of SVM method for classification the mineralised zones.

Distributed support vector machine in master-slave mode.

PubMed

Chen, Qingguo; Cao, Feilong

2018-05-01

It is well known that the support vector machine (SVM) is an effective learning algorithm. The alternating direction method of multipliers (ADMM) algorithm has emerged as a powerful technique for solving distributed optimisation models. This paper proposes a distributed SVM algorithm in a master-slave mode (MS-DSVM), which integrates a distributed SVM and ADMM acting in a master-slave configuration where the master node and slave nodes are connected, meaning the results can be broadcasted. The distributed SVM is regarded as a regularised optimisation problem and modelled as a series of convex optimisation sub-problems that are solved by ADMM. Additionally, the over-relaxation technique is utilised to accelerate the convergence rate of the proposed MS-DSVM. Our theoretical analysis demonstrates that the proposed MS-DSVM has linear convergence, meaning it possesses the fastest convergence rate among existing standard distributed ADMM algorithms. Numerical examples demonstrate that the convergence and accuracy of the proposed MS-DSVM are superior to those of existing methods under the ADMM framework. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method

NASA Astrophysics Data System (ADS)

Khandelwal, Manoj; Monjezi, M.

2013-03-01

Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

Inline Measurement of Particle Concentrations in Multicomponent Suspensions using Ultrasonic Sensor and Least Squares Support Vector Machines.

PubMed

Zhan, Xiaobin; Jiang, Shulan; Yang, Yili; Liang, Jian; Shi, Tielin; Li, Xiwen

2015-09-18

This paper proposes an ultrasonic measurement system based on least squares support vector machines (LS-SVM) for inline measurement of particle concentrations in multicomponent suspensions. Firstly, the ultrasonic signals are analyzed and processed, and the optimal feature subset that contributes to the best model performance is selected based on the importance of features. Secondly, the LS-SVM model is tuned, trained and tested with different feature subsets to obtain the optimal model. In addition, a comparison is made between the partial least square (PLS) model and the LS-SVM model. Finally, the optimal LS-SVM model with the optimal feature subset is applied to inline measurement of particle concentrations in the mixing process. The results show that the proposed method is reliable and accurate for inline measuring the particle concentrations in multicomponent suspensions and the measurement accuracy is sufficiently high for industrial application. Furthermore, the proposed method is applicable to the modeling of the nonlinear system dynamically and provides a feasible way to monitor industrial processes.

Scaling Support Vector Machines On Modern HPC Platforms

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

You, Yang; Fu, Haohuan; Song, Shuaiwen

2015-02-01

We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.

What do we know about direct-to-consumer advertising of prescription drugs?

PubMed

Calfee, John E

2003-01-01

Two papers, by Joel Weissman and colleagues and by Robert Dubois, add to our limited knowledge of the effects of direct-to-consumer (DTC) advertising of prescription drugs. Their results reinforce the largely positive findings from consumer surveys, while adding valuable new data and insights. These suggest that DTC ads probably improve patients' health outcomes and do not tend to lead to inappropriate prescribing. DTC advertising is emerging as a positive force in health care markets, consistent with what is known about the effects of advertising in many other markets.

Fault detection of Tennessee Eastman process based on topological features and SVM

NASA Astrophysics Data System (ADS)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

PubMed

Liu, Xiaoming; Guo, Shuxu; Yang, Bingtao; Ma, Shuzhi; Zhang, Huimao; Li, Jing; Sun, Changjian; Jin, Lanyi; Li, Xueyan; Yang, Qi; Fu, Yu

2018-04-20

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Effects of comparative claims in prescription drug direct-to-consumer advertising on consumer perceptions and recall.

PubMed

O'Donoghue, Amie C; Williams, Pamela A; Sullivan, Helen W; Boudewyns, Vanessa; Squire, Claudia; Willoughby, Jessica Fitts

2014-11-01

Although pharmaceutical companies cannot make comparative claims in direct-to-consumer (DTC) ads for prescription drugs without substantial evidence, the U.S. Food and Drug Administration permits some comparisons based on labeled attributes of the drug, such as dosing. Researchers have examined comparative advertising for packaged goods; however, scant research has examined comparative DTC advertising. We conducted two studies to determine if comparative claims in DTC ads influence consumers' perceptions and recall of drug information. In Experiment 1, participants with osteoarthritis (n=1934) viewed a fictitious print or video DTC ad that had no comparative claim or made an efficacy comparison to a named or unnamed competitor. Participants who viewed print (but not video) ads with named competitors had greater efficacy and lower risk perceptions than participants who viewed unnamed competitor and noncomparative ads. In Experiment 2, participants with high cholesterol or high body mass index (n=5317) viewed a fictitious print or video DTC ad that had no comparative claim or made a comparison to a named or unnamed competitor. We varied the type of comparison (of indication, dosing, or mechanism of action) and whether the comparison was accompanied by a visual depiction. Participants who viewed print and video ads with named competitors had greater efficacy perceptions than participants who viewed unnamed competitor and noncomparative ads. Unlike Experiment 1, named competitors in print ads resulted in higher risk perceptions than unnamed competitors. In video ads, participants who saw an indication comparison had greater benefit recall than participants who saw dosing or mechanism of action comparisons. In addition, visual depictions of the comparison decreased risk recall for video ads. Overall, the results suggest that comparative claims in DTC ads could mislead consumers about a drug's efficacy and risk; therefore, caution should be used when presenting comparative claims in DTC ads. Published by Elsevier Ltd.

The association of consumer cost-sharing and direct-to-consumer advertising with prescription drug use.

PubMed

Hansen, Richard A; Schommer, Jon C; Cline, Richard R; Hadsall, Ronald S; Schondelmeyer, Stephen W; Nyman, John A

2005-06-01

Previous research on the impact of various cost-sharing strategies on prescription drug use has not considered the impact of direct-to-consumer (DTC) advertising. To explore the association of cost-containment strategies with prescription drug use and to determine if the association is moderated by DTC prescription drug advertising. The study population included 288 280 employees and dependents aged 18 to 65 years with employer-sponsored health insurance contributing to the MEDSTAT MarketScan administrative data set. Person-level enrollment and claims data were obtained for beneficiaries enrolled continuously during July 1997 through December 1998. Direct-to-consumer advertising data were obtained from Competitive Media Reporting and linked to the MEDSTAT enrollment files. Localized DTC advertising expenditures for one class of medication were evaluated and matched with prescription claims for eligible MEDSTAT contributors. The association of various types and levels of cost-sharing incentives with incident product use was evaluated, controlling for the level of DTC advertising, health status, and other demographic covariates. The relationship of cost-sharing amounts with drug use was modified by the level of DTC advertising in a geographic market. This relationship was dependent on the type of cost-sharing, distinguishing between co-payments for provider visits and co-payments for prescription drugs. Compared with low-advertising markets, individuals residing in markets with high levels of advertising and paying provider co-payments of $10.00 or more were more likely to use the advertised product. In the same markets, higher prescription drug co-payments were associated with a decreased likelihood of using the advertised product. A similar relationship was not observed for the nonadvertised competitor. Among insured individuals, response to cost-sharing strategies is moderated by DTC prescription drug advertising. The relative ability of cost-sharing strategies to influence drug use should be interpreted with caution in the presence of DTC advertising.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data

NASA Astrophysics Data System (ADS)

Dementev, A. O.; Dmitriev, E. V.; Kozoderov, V. V.; Egorov, V. D.

2017-10-01

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

Detection of Alzheimer's disease using group lasso SVM-based region selection

NASA Astrophysics Data System (ADS)

Sun, Zhuo; Fan, Yong; Lelieveldt, Boudewijn P. F.; van de Giessen, Martijn

2015-03-01

Alzheimer's disease (AD) is one of the most frequent forms of dementia and an increasing challenging public health problem. In the last two decades, structural magnetic resonance imaging (MRI) has shown potential in distinguishing patients with Alzheimer's disease and elderly controls (CN). To obtain AD-specific biomarkers, previous research used either statistical testing to find statistically significant different regions between the two clinical groups, or l1 sparse learning to select isolated features in the image domain. In this paper, we propose a new framework that uses structural MRI to simultaneously distinguish the two clinical groups and find the bio-markers of AD, using a group lasso support vector machine (SVM). The group lasso term (mixed l1- l2 norm) introduces anatomical information from the image domain into the feature domain, such that the resulting set of selected voxels are more meaningful than the l1 sparse SVM. Because of large inter-structure size variation, we introduce a group specific normalization factor to deal with the structure size bias. Experiments have been performed on a well-designed AD vs. CN dataset1 to validate our method. Comparing to the l1 sparse SVM approach, our method achieved better classification performance and a more meaningful biomarker selection. When we vary the training set, the selected regions by our method were more stable than the l1 sparse SVM. Classification experiments showed that our group normalization lead to higher classification accuracy with fewer selected regions than the non-normalized method. Comparing to the state-of-art AD vs. CN classification methods, our approach not only obtains a high accuracy with the same dataset, but more importantly, we simultaneously find the brain anatomies that are closely related to the disease.

Reducing the bioavailability of cadmium in contaminated soil by dithiocarbamate chitosan as a new remediation.

PubMed

Yin, Zheng; Cao, Jingjing; Li, Zhen; Qiu, Dong

2015-07-01

Dithiocarbamate chitosan (DTC-CTS) was used as a new amendment for remediation of cadmium (Cd)-contaminated soils to reduce the Cd bioavailability. Arabidopsis thaliana was chosen as a model plant to evaluate its efficiency. It was found that DTC-CTS could effectively improve the growth of A. thaliana. The amount of Cd up-taken by A. thaliana could be decreased by as much as 50% compared with that grown in untreated Cd-contaminated soil samples. The chlorophyll content and the aerial biomass of Arabidopsis also increased substantially and eventually returned to a level comparable to plants grown in non-contaminated soils, with the addition of DTC-CTS. These findings suggested that DTC-CTS amendment could be effective in immobilizing Cd and mitigating its accumulation in plants grown in Cd-contaminated soils, with potential application as an in situ remediation of Cd-polluted soils.

Consumer friendly or reader hostile? An evaluation of the readability of DTC print ads.

PubMed

Sheehan, Kim

2008-01-01

The Food and Drug Administration requires advertisements promoting prescription drugs to be written in "consumer friendly" language. The purpose of this study is to examine the language of Direct-to-Consumer prescription drug advertisements to determine if such language is easy for consumers to read and understand. A series of advertisements for a variety of products, appearing in popular consumer magazines, were analyzed using the Flesch and Gunning-Fogg formulas to determine if DTC advertisements are more or less complex than other advertisements that consumers read today. Results indicate that DTC ads are among the most difficult print ads to read. Additionally, certain types of information contained in these print ads (such as information discussing a drug's risks and contraindications) are significantly more difficult to read than information in any other type of ad copy in magazines today. Implications for DTC marketers and the FDA are included.

Direct-to-consumer advertising: its effects on stakeholders.

PubMed

Montoya, Isaac D; Lee-Dukes, Gwen; Shah, Dhvani

2008-01-01

The escalating growth in the development of pharmaceutical drugs has caused the pharmaceutical industry to market drugs directly to consumers. Direct-to-consumer (DTC) advertising has increased immensely in the past 15 years and continues to grow each year. The advantages of DTC advertising include an increase in consumer knowledge, patient autonomy, and possibly providing physicians and pharmacists with up-to-date information about the recent trends in the marketplace. However, there is also an equally notable list of disadvantages, which include concerns about the quality of information provided, loss in physician productivity due to time spent convincing patients that what they want is not in their best interest, and increases in the reimbursement expenditure of the insurers. Because of these conflicting outcomes, the issue of DTC advertising has become controversial. This report offers an overview of DTC advertising and focuses on its effects on physicians, pharmacists, consumers, insurers, the government, and pharmaceutical manufacturers.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

Image quality classification for DR screening using deep learning.

PubMed

FengLi Yu; Jing Sun; Annan Li; Jun Cheng; Cheng Wan; Jiang Liu

2017-07-01

The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.

Direct-to-consumer pharmaceutical advertising: physician and public opinion and potential effects on the physician-patient relationship.

PubMed

Robinson, Andrew R; Hohmann, Kirsten B; Rifkin, Julie I; Topp, Daniel; Gilroy, Christine M; Pickard, Jeffrey A; Anderson, Robert J

2004-02-23

Previous studies have shown that direct-to-consumer (DTC) pharmaceutical advertising can influence consumer behavior and that many physicians have negative views of these advertisements. Physician and public opinions about these advertisements and how they may affect the physician-patient relationship are not well established. Mail survey of 523 Colorado physicians and 261 national physicians and telephone survey of 500 Colorado households asking respondents to rate their agreement with statements about DTC advertising. Most physicians tended to view DTC advertisements negatively, indicating that such advertisements rarely provide enough information on cost (98.7%), alternative treatment options (94.9%), or adverse effects (54.8%). Most also believed that DTC advertisements affected interactions with patients by lengthening clinical encounters (55.9%), leading to patient requests for specific medications (80.7%), and changing patient expectations of physicians' prescribing practices (67.0%). Only 29.0% of public respondents agreed that DTC advertising is a positive trend in health care and 28.6% indicated that advertisements make them better informed about medical problems; fewer indicated that advertisements motivated them to seek care (10.5%) or led them to request specific medications from their physicians (13.3%). Most physicians have negative views of DTC pharmaceutical advertising and see several potential effects of these advertisements on the physician-patient relationship. Many public respondents have similarly negative views, and only a few agree that they change their expectations of or interactions with physicians. While these advertisements may be influencing only a few consumers, it seems that the impact on physicians and their interactions with patients may be significant.

Direct to consumer genetic testing-law and policy concerns in Ireland.

PubMed

de Paor, Aisling

2017-11-25

With rapid scientific and technological advances, the past few years has witnessed the emergence of a new genetic era and a growing understanding of the genetic make-up of human beings. These advances have propelled the introduction of companies offering direct to consumer (DTC) genetic testing, which facilitates the direct provision of such tests to consumers, (for example, via the internet). Although DTC genetic testing offers benefits by enhancing consumer accessibility to such technology, promoting proactive healthcare and increasing genetic awareness, it presents a myriad of challenges, from an ethical, legal and regulatory perspective. As DTC genetic testing usually eliminates the need for a medical professional in accessing genetic tests, this lack of professional guidance and counselling may result in misinterpretation and confusion regarding results. In addition, an evident concern relates to the scientific validity and quality of these tests. A further problem arising is the lack or inadequacy of regulation in this field. Despite the increasing accessibility of DTC genetic testing, this legislative vacuum is apparent in Ireland, where there is no concrete legislation. This article explores the main ethical, legal and regulatory issues arising with the advent of rapid advances in DTC genetic testing in Ireland. Further, with inevitable future advances in genetic science, as well as increasing internet accessibility, the challenges presented are likely to become more amplified. In consideration of the ethical and legal challenges, this paper highlights the regulation of DTC genetic testing as a growing concern in Ireland, recognising its importance to both the scientific community as well as in respect of enhancing consumer confidence in such technologies.

Episode-specific drinking-to-cope motivation, daily mood, and fatigue-related symptoms among college students.

PubMed

Armeli, Stephen; O'Hara, Ross E; Ehrenberg, Ethan; Sullivan, Tami P; Tennen, Howard

2014-09-01

The goal of the present study was to examine whether within-person, episode-specific changes in drinking-to-cope (DTC) motivation from the previous evening were associated with concurrent daily mood and fatigue-related symptoms among college student drinkers (N = 1,421; 54% female). We conducted an Internet-based daily diary study in which students reported over 30 days on their previous night's drinking level and motivation and their current mood (i.e., sadness, anxiety, anger/hostility, and positive mood) and fatigue-related symptoms. Hypotheses were tested using hierarchical linear models in which the current day's outcome was predicted by last night's levels of DTC motivation and drinking, controlling for drinking to enhance motivation, sex, current day's physical symptoms and drinking, and yesterday's level of the outcome. Subsequent models also predicted outcomes 2 days following the drinking event. Relative increases in previous night's DTC motivation were associated with higher levels of current day negative mood and fatigue-related symptoms and lower levels of positive mood. Also, the association between episode-specific DTC motivation and negative mood was stronger in the positive direction when individuals reported higher levels of nonsocial drinking from the previous night. Last, episode-specific DTC showed similar associations with sadness and anger/hostility 2 days after the drinking event. The results are generally consistent with the posited attention allocation and ego-depletion mechanisms. Findings suggest that the deleterious effects of repeated episodes of DTC, over time, could help to explain the increased likelihood of alcohol-related problems seen in prior studies.

Intergenerational effects of parental substance-related convictions and adult drug treatment court participation on children's school performance.

PubMed

Gifford, Elizabeth J; Sloan, Frank A; Eldred, Lindsey M; Evans, Kelly E

2015-09-01

This study examined the intergenerational effects of parental conviction of a substance-related charge on children's academic performance and, conditional on a conviction, whether completion of an adult drug treatment court (DTC) program was associated with improved school performance. State administrative data from North Carolina courts, birth records, and school records were linked for 2005-2012. Math and reading end-of-grade test scores and absenteeism were examined for 5 groups of children, those with parents who: were not convicted on any criminal charge, were convicted on a substance-related charge and not referred by a court to a DTC, were referred to a DTC but did not enroll, enrolled in a DTC but did not complete, and completed a DTC program. Accounting for demographic and socioeconomic factors, the school performance of children whose parents were convicted of a substance-related offense was worse than that of children whose parents were not convicted on any charge. These differences were statistically significant but substantially reduced after controlling for socioeconomic characteristics; for example, mother's educational attainment. We found no evidence that parent participation in an adult DTC program led to improved school performance of their children. While the children of convicted parents fared worse on average, much--but not all--of this difference was attributed to socioeconomic factors, with the result that parental conviction remained a risk factor for poorer school performance. Even though adult DTCs have been shown to have other benefits, we could detect no intergenerational benefit in improved school performance of their children. (c) 2015 APA, all rights reserved).

Patterns of differentiated thyroid cancer in Baluchistan Province of Pakistan: some initial observations.

PubMed

Iftikhar, A; Naseeb, A Khush; Khwaja, A; Mati, H; Karim, K; Hameeda, N

2011-10-01

The incidence of thyroid cancer is increasing in several countries. The main objective of this retrospective study was to find and describe province-specific estimates of incidence in males and females by age groups for differentiated thyroid cancer (DTC). This study reports on 87 cases of DTC from Baluchistan province of Pakistan treated with post operative radioiodine at the Center for Nuclear Medicine and Radiotherapy (CENAR) Quetta from January 2003 to December 2009. The patient data has been collected from CENAR Quetta. Patients with DTC were confirmed by clinical examination, thyroid scintigraphy (Thyroid scan), blood tests (T3, T4, TSH) and histopathalogy tests and then treated with radioiodine. The Median age of the patients was 35.5 years (Range 12-70 years). The final histological diagnosis was papillary carcinoma in 71 (81.6 %) cases, follicular carcinoma in 6 (6.9%) cases while 10 (11.5%) cases presented with mixed papillary and follicular carcinoma. About 53 % cases were found in females with age 21-40 years. No strike predominance was observed in any age group for males. Four patients presented with recurrence while six patients showed metastasis in cervical lymph nodes. The small annual incidence did not follow any definite pattern. DTC has a small incidence in Baluchistan due to lack of education and health care facilities. The incidence of DTC is higher in females when compared with males as per this study. This preliminary study will provide an insight to incidence of DTC, its treatment facilities and future planning strategies in Baluchistan, Pakistan.

75 FR 6752 - Self-Regulatory Organizations; The Depository Trust Company; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-10

... submission received by DTC and would also create an hourly Extraordinary Processing/Research Fee of $100 per... revisions are consistent with DTC's overall pricing philosophy of aligning service fees with underlying...

d-Tubocurarine and Berbamine: Alkaloids That Are Permeant Blockers of the Hair Cell's Mechano-Electrical Transducer Channel and Protect from Aminoglycoside Toxicity

PubMed Central

Kirkwood, Nerissa K.; O'Reilly, Molly; Derudas, Marco; Kenyon, Emma J.; Huckvale, Rosemary; van Netten, Sietse M.; Ward, Simon E.; Richardson, Guy P.; Kros, Corné J.

2017-01-01

Aminoglycoside antibiotics are widely used for the treatment of life-threatening bacterial infections, but cause permanent hearing loss in a substantial proportion of treated patients. The sensory hair cells of the inner ear are damaged following entry of these antibiotics via the mechano-electrical transducer (MET) channels located at the tips of the hair cell's stereocilia. d-Tubocurarine (dTC) is a MET channel blocker that reduces the loading of gentamicin-Texas Red (GTTR) into rat cochlear hair cells and protects them from gentamicin treatment. Berbamine is a structurally related alkaloid that reduces GTTR labeling of zebrafish lateral-line hair cells and protects them from aminoglycoside-induced cell death. Both compounds are thought to reduce aminoglycoside entry into hair cells through the MET channels. Here we show that dTC (≥6.25 μM) or berbamine (≥1.55 μM) protect zebrafish hair cells in vivo from neomycin (6.25 μM, 1 h). Protection of zebrafish hair cells against gentamicin (10 μM, 6 h) was provided by ≥25 μM dTC or ≥12.5 μM berbamine. Hair cells in mouse cochlear cultures are protected from longer-term exposure to gentamicin (5 μM, 48 h) by 20 μM berbamine or 25 μM dTC. Berbamine is, however, highly toxic to mouse cochlear hair cells at higher concentrations (≥30 μM) whilst dTC is not. The absence of toxicity in the zebrafish assays prompts caution in extrapolating results from zebrafish neuromasts to mammalian cochlear hair cells. MET current recordings from mouse outer hair cells (OHCs) show that both compounds are permeant open-channel blockers, rapidly and reversibly blocking the MET channel with half-blocking concentrations of 2.2 μM (dTC) and 2.8 μM (berbamine) in the presence of 1.3 mM Ca2+ at −104 mV. Berbamine, but not dTC, also blocks the hair cell's basolateral K+ current, IK,neo, and modeling studies indicate that berbamine permeates the MET channel more readily than dTC. These studies reveal key properties of MET-channel blockers required for the future design of successful otoprotectants. PMID:28928635

Prediction and analysis of beta-turns in proteins by support vector machine.

PubMed

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2003-01-01

Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

Classification of edible oils and modeling of their physico-chemical properties by chemometric methods using mid-IR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Ferré, Joan; Boqué, Ricard

This research work describes two studies for the classification and characterization of edible oils and its quality parameters through Fourier transform mid infrared spectroscopy (FT-mid-IR) together with chemometric methods. The discrimination of canola, sunflower, corn and soybean oils was investigated using SVM-DA, SIMCA and PLS-DA. Using FT-mid-IR, DPLS was able to classify 100% of the samples from the validation set, but SIMCA and SVM-DA were not. The quality parameters: refraction index and relative density of edible oils were obtained from reference methods. Prediction models for FT-mid-IR spectra were calculated for these quality parameters using partial least squares (PLS) and support vector machines (SVM). Several preprocessing alternatives (first derivative, multiplicative scatter correction, mean centering, and standard normal variate) were investigated. The best result for the refraction index was achieved with SVM as well as for the relative density except when the preprocessing combination of mean centering and first derivative was used. For both of quality parameters, the best results obtained for the figures of merit expressed by the root mean square error of cross validation (RMSECV) and prediction (RMSEP) were equal to 0.0001.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Improving near-infrared prediction model robustness with support vector machine regression: a pharmaceutical tablet assay example.

PubMed

Igne, Benoît; Drennen, James K; Anderson, Carl A

2014-01-01

Changes in raw materials and process wear and tear can have significant effects on the prediction error of near-infrared calibration models. When the variability that is present during routine manufacturing is not included in the calibration, test, and validation sets, the long-term performance and robustness of the model will be limited. Nonlinearity is a major source of interference. In near-infrared spectroscopy, nonlinearity can arise from light path-length differences that can come from differences in particle size or density. The usefulness of support vector machine (SVM) regression to handle nonlinearity and improve the robustness of calibration models in scenarios where the calibration set did not include all the variability present in test was evaluated. Compared to partial least squares (PLS) regression, SVM regression was less affected by physical (particle size) and chemical (moisture) differences. The linearity of the SVM predicted values was also improved. Nevertheless, although visualization and interpretation tools have been developed to enhance the usability of SVM-based methods, work is yet to be done to provide chemometricians in the pharmaceutical industry with a regression method that can supplement PLS-based methods.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

[Measurement of soil organic matter and available K based on SPA-LS-SVM].

PubMed

Zhang, Hai-Liang; Liu, Xue-Mei; He, Yong

2014-05-01

Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LSSVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0. 860 2 and 2. 98 for OM and 0. 730 5 and 15. 78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325 approximately 1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.

SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

USDA-ARS?s Scientific Manuscript database

Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PubMed

Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

2014-03-01

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Header: Do adult DTC programs prevent child maltreatment? Parental criminal justice involvement and children’s involvement with child protective services: Do adult drug treatment courts prevent child maltreatment?

PubMed Central

Eldred, Lindsey M.; Sloan, Frank A.; Evans, Kelly E.

2016-01-01

Background In light of evidence showing reduced criminal recidivism and cost savings, adult drug treatment courts have grown in popularity. However, the potential spillover benefits to family members are understudied. Objectives To examine: 1) the overlap between parents who were convicted of a substance-related offense and their children’s involvement with child protective services (CPS); and 2) whether parental participation in an adult drug treatment court program reduces children’s risk for CPS involvement. Methods Administrative data from North Carolina courts, birth records, and social services were linked at the child level. First, children of parents convicted of a substance-related offense were matched to (a) children of parents convicted of a non-substance-related offense and (b) those not convicted of any offense. Second, we compared children of parents who completed a DTC program with children of parents who were referred but did not enroll, who enrolled for <90 days but did not complete, and who enrolled for 90+ days but did not complete. Multivariate logistic regression was used to model group differences in the odds of being reported to CPS in the one to three years following parental criminal conviction or, alternatively, being referred to a DTC program. Results Children of parents convicted of a substance-related offense were at greater risk of CPS involvement than children whose parents were not convicted of any charge, but DTC participation did not mitigate this risk. Conclusion/Importance The role of specialty courts as a strategy for reducing children’s risk of maltreatment should be further explored. PMID:26789656

Sociodemographic Disparities in Differentiated Thyroid Cancer Survival Among Adolescents and Young Adults in California

PubMed Central

Grogan, Raymon H.; Parsons, Helen M.; Tao, Li; White, Michael G.; Onel, Kenan; Horn-Ross, Pamela L.

2015-01-01

Background: Few studies have focused on prognostic factors among adolescents and young adults (AYAs) 15 to 39 years of age when diagnosed with differentiated thyroid cancer (DTC). Our study expands upon prior work by including an evaluation of survival among AYA men and by neighborhood socioeconomic status, health insurance, and clinical factors to identify subgroups of young DTC patients at higher risk of mortality. Methods: Data for 16,827 AYA DTC patients diagnosed between 1988 and 2010 were obtained from the California Cancer Registry. Survival, through 2010, by sociodemographic and clinical factors was analyzed using Cox proportional hazards regression. Results: Of the 2.1% of AYAs who died, 16.7% died from thyroid cancer and 21.4% died from a subsequent cancer. In multivariate analyses, older AYAs 35 to 39 year of age (versus 15- to 29-year-olds), men (hazard ratio [HR] 2.77, 95% confidence interval [CI] 1.62–4.72), and AYAs of African American or Hispanic race/ethnicity (versus non-Hispanic whites) had worse thyroid cancer specific survival. In addition, residing in low socioeconomic status neighborhoods (HR 3.11 [CI 1.28–7.56]) and nonmetropolitan areas (HR 5.53 [CI 2.07–14.78]) was associated with worse thyroid cancer–specific survival among AYA men, but not AYA women. Conclusions: Despite the generally good prognosis among AYAs with DTC, we identified subgroups of AYA patients at risk for poor outcomes. Further study of the factors underlying these associations, including possible barriers to receiving high-quality treatment and follow-up care, as well as lifestyle factors, are critical to reducing these disparities. PMID:25778795

Clinical factors related to the efficacy of tyrosine kinase inhibitor therapy in radioactive iodine refractory recurrent differentiated thyroid cancer patients.

PubMed

Sugino, Kiminori; Nagahama, Mitsuji; Kitagawa, Wataru; Ohkuwa, Keiko; Uruno, Takashi; Matsuzu, Kenichi; Suzuki, Akifumi; Masaki, Chie; Akaishi, Junko; Hames, Kiyomi Y; Tomoda, Chisato; Ogimi, Yuna; Ito, Koichi

2018-03-28

New insights in thyroid cancer biology propelled the development of targeted therapies as salvage treatment for radioiodine-refractory differentiated thyroid cancer (RR-DTC), and the tyrosine kinase inhibitor (TKI) lenvatinib has recently become available as a new line of therapy for RR-DTC. The aim of this study is to investigate clinical factors related to the efficacy of TKI therapy in recurrent RR-DTC patients and identify the optimal timing for the start of TKI therapy. The subjects consisted of 29 patients with progressive RR-DTC, 9 males and 20 females, median age 66 years. A univariate analysis was conducted in relation to progression free survival (PFS) and overall survival (OS) by the Kaplan-Meier method for the following variables: age, sex, histology of the primary tumor, thyroglobulin doubling time before the start of lenvatinib therapy, site of the target lesions, presence of a tumor-mediated symptom at the start of lenvatinib therapy, and baseline tumor size of the target lesions. Median duration of lenvatinib therapy was 14.7 months and median drug intensity was 9.5 mg. At the time of the data cut-off for the analysis, 9 patients (31.0%) have died of their disease (DOD), and a PR (partial response), SD (stable disease), and PD (progressive disease) were observed in 20 patients (69%), 6 patients (20.7%), 3 patients (10.3%), respectively. Univariate analyses showed that the presence of a symptom was the only factor significantly related to poorer PFS and OS. Clinical benefit of TKI therapy will be possibly limited when the therapy starts after tumor-mediated symptoms appear.

Trends in Imaging after Thyroid Cancer Diagnosis

PubMed Central

Banerjee, Mousumi; Muenz, Daniel G.; Worden, Francis P.; Haymart, Megan R.

2015-01-01

Background The largest growth in differentiated thyroid cancer (DTC) diagnosis is in low-risk cancers. Trends in imaging after DTC diagnosis are understudied. Hypothesizing a reduction in imaging utilization due to rising low-risk disease, we evaluated post-diagnosis imaging patterns over time and patient characteristics that are associated with likelihood of imaging. Methods Using the Surveillance Epidemiology and End Results-Medicare database, we identified patients diagnosed with localized, regional or distant DTC between 1991 and 2009. We reviewed Medicare claims for neck ultrasound, I-131 scan, or PET scan within 3 years post-diagnosis. Using regression analyses we evaluated trends of imaging utilization. Multivariable logistic regression was used to estimate the likelihood of imaging based on patient characteristics. Results 23,669 patients were included. Patients diagnosed during 2001-2009, compared to 1991-2000, were more likely to have localized disease (p<0.001) and tumors less than 1cm (p<0.001). Use of neck ultrasound and I-131 scan increased in patients with localized disease (p=<0.001 and p=0.003, respectively), regional disease (p<0.001 and p<0.001), and distant metastasis (p=0.001 and p=0.015). Patients diagnosed after 2000 were more likely to undergo neck ultrasound (OR 2.15, 95% CI 2.02-2.28) and I-131 scan (OR 1.44, 95% CI 1.35-1.54). PET scan use from 2005-2009, compared to 1996-2004, increased 32.4-fold (p=<0.001) in localized patients, 13.1-fold (p<0.001) in regional disease patients, and 33.4-fold (p<0.001) in patients with distant DTC. Conclusion Despite a rise in low-risk disease, the use of post-diagnosis imaging increased in all stages of disease. The largest growth was in use of PET scan after 2004. PMID:25565063

Lenvatinib in Advanced Radioiodine-Refractory Thyroid Cancer - A Retrospective Analysis of the Swiss Lenvatinib Named Patient Program.

PubMed

Balmelli, Catharina; Railic, Nikola; Siano, Marco; Feuerlein, Kristin; Cathomas, Richard; Cristina, Valerie; Güthner, Christiane; Zimmermann, Stefan; Weidner, Sabine; Pless, Miklos; Stenner, Frank; Rothschild, Sacha I

2018-01-01

Purpose: Differentiated thyroid cancer (DTC) accounts for approximately 95% of thyroid carcinomas. In the metastatic RAI-refractory disease, chemotherapy has very limited efficacy and is associated with substantial toxicity. With increasing knowledge of the molecular pathogenesis of DTC, novel targeted therapies have been developed. Lenvatinib is a tyrosine kinase inhibitor (TKI) with promising clinical activity based on the randomized phase III SELECT trial. In Switzerland, a Named Patient Program (NPP) was installed to bridge the time gap to Swissmedic approval. Here, we report the results from the Swiss Lenvatinib NPP including patients with metastatic RAI-refractory DTC. Methods: Main inclusion criteria for the Swiss NPP were RAI-refractory DTC, documented disease progression, Eastern Cooperative Oncology Group (ECOG) performance status 0-3. The number of previous therapies was not limited. The Swiss Lenvatinib NPP was initiated in June 2014 and was closed in October 2015 with the approval of the drug. Results: Between June 2014 and October 2015, 13 patients with a median age of 72 years have been enrolled. Most patients (69%) had at least one prior systemic therapy, mainly sorafenib. 31% of patients showed a PR and 31% SD. Median progression free survival was 7.2 months and the median overall survival was 22.7 months. Dose reduction due to adverse events was necessary in 7 patients (53%). At the time of analysis 6 patients (47%) were still on treatment with a median time on treatment of 9.98 months. Conclusions: Our results show that lenvatinib has reasonable clinical activity in unselected patients with RAI-refractory thyroid cancer with nearly two-third of patients showing clinical benefit. The toxicity profile of lenvatinib is manageable.

Lenvatinib in Advanced Radioiodine-Refractory Thyroid Cancer - A Retrospective Analysis of the Swiss Lenvatinib Named Patient Program

PubMed Central

Balmelli, Catharina; Railic, Nikola; Siano, Marco; Feuerlein, Kristin; Cathomas, Richard; Cristina, Valerie; Güthner, Christiane; Zimmermann, Stefan; Weidner, Sabine; Pless, Miklos; Stenner, Frank; Rothschild, Sacha I.

2018-01-01

Purpose:Differentiated thyroid cancer (DTC) accounts for approximately 95% of thyroid carcinomas. In the metastatic RAI-refractory disease, chemotherapy has very limited efficacy and is associated with substantial toxicity. With increasing knowledge of the molecular pathogenesis of DTC, novel targeted therapies have been developed. Lenvatinib is a tyrosine kinase inhibitor (TKI) with promising clinical activity based on the randomized phase III SELECT trial. In Switzerland, a Named Patient Program (NPP) was installed to bridge the time gap to Swissmedic approval. Here, we report the results from the Swiss Lenvatinib NPP including patients with metastatic RAI-refractory DTC. Methods:Main inclusion criteria for the Swiss NPP were RAI-refractory DTC, documented disease progression, Eastern Cooperative Oncology Group (ECOG) performance status 0-3. The number of previous therapies was not limited. The Swiss Lenvatinib NPP was initiated in June 2014 and was closed in October 2015 with the approval of the drug. Results:Between June 2014 and October 2015, 13 patients with a median age of 72 years have been enrolled. Most patients (69%) had at least one prior systemic therapy, mainly sorafenib. 31% of patients showed a PR and 31% SD. Median progression free survival was 7.2 months and the median overall survival was 22.7 months. Dose reduction due to adverse events was necessary in 7 patients (53%). At the time of analysis 6 patients (47%) were still on treatment with a median time on treatment of 9.98 months. Conclusions:Our results show that lenvatinib has reasonable clinical activity in unselected patients with RAI-refractory thyroid cancer with nearly two-third of patients showing clinical benefit. The toxicity profile of lenvatinib is manageable. PMID:29344270

Epithelial–mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer

PubMed Central

Fan, X-J; Wan, X-B; Huang, Y; Cai, H-M; Fu, X-H; Yang, Z-L; Chen, D-K; Song, S-X; Wu, P-H; Liu, Q; Wang, L; Wang, J-P

2012-01-01

Background: Current imaging modalities are inadequate in preoperatively predicting regional lymph node metastasis (RLNM) status in rectal cancer (RC). Here, we designed support vector machine (SVM) model to address this issue by integrating epithelial–mesenchymal-transition (EMT)-related biomarkers along with clinicopathological variables. Methods: Using tissue microarrays and immunohistochemistry, the EMT-related biomarkers expression was measured in 193 RC patients. Of which, 74 patients were assigned to the training set to select the robust variables for designing SVM model. The SVM model predictive value was validated in the testing set (119 patients). Results: In training set, eight variables, including six EMT-related biomarkers and two clinicopathological variables, were selected to devise SVM model. In testing set, we identified 63 patients with high risk to RLNM and 56 patients with low risk. The sensitivity, specificity and overall accuracy of SVM in predicting RLNM were 68.3%, 81.1% and 72.3%, respectively. Importantly, multivariate logistic regression analysis showed that SVM model was indeed an independent predictor of RLNM status (odds ratio, 11.536; 95% confidence interval, 4.113–32.361; P<0.0001). Conclusion: Our SVM-based model displayed moderately strong predictive power in defining the RLNM status in RC patients, providing an important approach to select RLNM high-risk subgroup for neoadjuvant chemoradiotherapy. PMID:22538975

Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

NASA Astrophysics Data System (ADS)

Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

2017-07-01

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

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

2014-12-01

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

75 FR 2570 - Self-Regulatory Organizations; The Depository Trust Company; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-15

... intraday by Fedwire to DTC when a DTC participant (``Participant'') has insufficient collateral \\5\\ or at.... The current early P&I withdrawal process allows Participants to withdraw intraday P&I payments for non...

Direct to confusion: lessons learned from marketing BRCA testing.

PubMed

Matloff, Ellen; Caplan, Arthur

2008-06-01

Myriad Genetics holds a patent on testing for the hereditary breast and ovarian cancer genes, BRCA1 and BRCA2, and therefore has a forced monopoly on this critical genetic test. Myriad launched a Direct-to-Consumer (DTC) marketing campaign in the Northeast United States in September 2007 and plans to expand that campaign to Florida and Texas in 2008. The ethics of Myriad's patent, forced monopoly and DTC campaign will be reviewed, as well as the impact of this situation on patient access and care, physician liability, and the future of DTC campaigns for genetic testing.

Ethical Issues for Direct-to-Consumer Digital Psychotherapy Apps: Addressing Accountability, Data Protection, and Consent

PubMed Central

Kreitmair, Karola

2018-01-01

This paper focuses on the ethical challenges presented by direct-to-consumer (DTC) digital psychotherapy services that do not involve oversight by a professional mental health provider. DTC digital psychotherapy services can potentially assist in improving access to mental health care for the many people who would otherwise not have the resources or ability to connect with a therapist. However, the lack of adequate regulation in this area exacerbates concerns over how safety, privacy, accountability, and other ethical obligations to protect an individual in therapy are addressed within these services. In the traditional therapeutic relationship, there are ethical obligations that serve to protect the interests of the client and provide warnings. In contrast, in a DTC therapy app, there are no clear lines of accountability or associated ethical obligations to protect the user seeking mental health services. The types of DTC services that present ethical challenges include apps that use a digital platform to connect users to minimally trained nonprofessional counselors, as well as services that provide counseling steered by artificial intelligence and conversational agents. There is a need for adequate oversight of DTC nonprofessional psychotherapy services and additional empirical research to inform policy that will provide protection to the consumer. PMID:29685865

External Beam Radiation in Differentiated Thyroid Carcinoma

PubMed Central

Billan, Salem; Charas, Tomer

2016-01-01

The treatment of differentiated thyroid carcinoma (DTC) is surgery followed in some cases by adjuvant treatment, mostly with radioactive iodine (RAI). External beam radiotherapy (EBRT) is less common and not a well-established treatment modality in DTC. The risk of recurrence depends on three major prognostic factors: extra-thyroid extension, patient’s age, and tumor with reduced iodine uptake. Increased risk for recurrence is a major factor in the decision whether to treat the patient with EBRT. Data about the use of EBRT in DTC are limited to small retrospective studies. Most series have demonstrated an increase in loco-regional control. The risk/benefit from giving EBRT requires careful patient selection. Different scoring systems have been proposed by different investigators and centers. The authors encourage clinicians treating DTC to become familiarized with those scoring systems and to use them in the management of different cases. The irradiated volume should include areas of risk for microscopic disease. Determining those areas in each case can be difficult and requires detailed knowledge of the surgery and pathological results, and also understanding of the disease-spreading pattern. Treatment with EBRT in DTC can be beneficial, and data support the use of EBRT in high-risk patients. Randomized controlled trials are needed for better confirmation of the role of EBRT. PMID:26886956

SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

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

Cardenas, C; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Wong, A

Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results inmore » non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate high-dose CTV for H&N patients. Both methods showed promising results that hint to a solution to the standardization of the contouring process of clinical target volumes. Varian Medical Systems grant.« less

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

PubMed Central

Mourao-Miranda, J.; Reinders, A. A. T. S.; Rocha-Rego, V.; Lappin, J.; Rondina, J.; Morgan, C.; Morgan, K. D.; Fearon, P.; Jones, P. B.; Doody, G. A.; Murray, R. M.; Kapur, S.; Dazzan, P.

2012-01-01

Background To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. Method One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. Results At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). Conclusions We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data. PMID:22059690

A New Method of Facial Expression Recognition Based on SPE Plus SVM

NASA Astrophysics Data System (ADS)

Ying, Zilu; Huang, Mingwei; Wang, Zhen; Wang, Zhewei

A novel method of facial expression recognition (FER) is presented, which uses stochastic proximity embedding (SPE) for data dimension reduction, and support vector machine (SVM) for expression classification. The proposed algorithm is applied to Japanese Female Facial Expression (JAFFE) database for FER, better performance is obtained compared with some traditional algorithms, such as PCA and LDA etc.. The result have further proved the effectiveness of the proposed algorithm.

Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier

NASA Astrophysics Data System (ADS)

Pablo Fernández, Juan; Shubitidze, Fridon; Shamatava, Irma; Barrowes, Benjamin E.; O'Neill, Kevin

2010-12-01

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries.

PubMed

Han, Bucong; Ma, Xiaohua; Zhao, Ruiying; Zhang, Jingxian; Wei, Xiaona; Liu, Xianghui; Liu, Xin; Zhang, Cunlong; Tan, Chunyan; Jiang, Yuyang; Chen, Yuzong

2012-11-23

Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.

Clinicopathological correlations of Bcl-xL and Bax expression in differentiated thyroid carcinoma.

PubMed

Martínez-Brocca, M Asunción; Castilla, Carolina; Navarro, Elena; Amaya, M José; Travado, Paulino; Japón, Miguel A; Sáez, Carmen

2008-02-01

The Bcl-2 family proteins are essential mediators in the apoptotic process. Our aim was to investigate whether anti-apoptotic Bcl-xL and pro-apoptotic Bax were over-expressed in a large series of differentiated thyroid carcinomas (DTC) and to study their association with tumour presentation at diagnosis and prognosis. We examined the immunohistochemical expression of Bcl-xL and Bax in benign nodular thyroid disease (BNTD) and DTC and their association with clinicopathological parameters. Thyroid tissue samples were collected from an unselected series of patients undergoing surgical resection for DTC (n = 74) or BNTD (n = 15). Among DTC cases, expression of Bcl-xL was found to be high in 43.2% and low or absent in 56.8%. Expression of Bax was high in 75.7% and low or absent in 24.3%. Non-neoplastic thyroid tissue was largely unstained for both proteins. Among BNTD cases, expression of Bcl-xL was high in 13.3% and low or absent in 86.6%. Expression of Bax was high in 14.3% and low or absent in 86.6%. A significant association was found between Bcl-xL expression and the presence of high-risk histological subtype (P < 0.05), and regional lymph node (P < 0.01) and distant metastases (P < 0.01). The association between high Bcl-xL expression levels and a longer time of persistent disease after radioiodine ablation was also significant (P < 0.01). Bcl-xL expression was confirmed as an independent prognostic factor for persistent disease in DTC (relative risk, 2.5; 95% confidence interval, 1.1-5.9; P < 0.05). Immunohistochemical expression of Bcl-xL might be a valuable tool in the prediction of tumour aggressiveness in DTC.

A Structural and Mutagenic Blueprint for Molecular Recognition of Strychnine and d-Tubocurarine by Different Cys-Loop Receptors

PubMed Central

Kuzmin, Dmitry; van Elk, René; Krijnen, Liz; Yakel, Jerrel L.; Tsetlin, Victor; Smit, August B.; Ulens, Chris

2011-01-01

Cys-loop receptors (CLR) are pentameric ligand-gated ion channels that mediate fast excitatory or inhibitory transmission in the nervous system. Strychnine and d-tubocurarine (d-TC) are neurotoxins that have been highly instrumental in decades of research on glycine receptors (GlyR) and nicotinic acetylcholine receptors (nAChR), respectively. In this study we addressed the question how the molecular recognition of strychnine and d-TC occurs with high affinity and yet low specificity towards diverse CLR family members. X-ray crystal structures of the complexes with AChBP, a well-described structural homolog of the extracellular domain of the nAChRs, revealed that strychnine and d-TC adopt multiple occupancies and different ligand orientations, stabilizing the homopentameric protein in an asymmetric state. This introduces a new level of structural diversity in CLRs. Unlike protein and peptide neurotoxins, strychnine and d-TC form a limited number of contacts in the binding pocket of AChBP, offering an explanation for their low selectivity. Based on the ligand interactions observed in strychnine- and d-TC-AChBP complexes we performed alanine-scanning mutagenesis in the binding pocket of the human α1 GlyR and α7 nAChR and showed the functional relevance of these residues in conferring high potency of strychnine and d-TC, respectively. Our results demonstrate that a limited number of ligand interactions in the binding pocket together with an energetic stabilization of the extracellular domain are key to the poor selective recognition of strychnine and d-TC by CLRs as diverse as the GlyR, nAChR, and 5-HT3R. PMID:21468359

A structural and mutagenic blueprint for molecular recognition of strychnine and d-tubocurarine by different cys-loop receptors.

PubMed

Brams, Marijke; Pandya, Anshul; Kuzmin, Dmitry; van Elk, René; Krijnen, Liz; Yakel, Jerrel L; Tsetlin, Victor; Smit, August B; Ulens, Chris

2011-03-01

Cys-loop receptors (CLR) are pentameric ligand-gated ion channels that mediate fast excitatory or inhibitory transmission in the nervous system. Strychnine and d-tubocurarine (d-TC) are neurotoxins that have been highly instrumental in decades of research on glycine receptors (GlyR) and nicotinic acetylcholine receptors (nAChR), respectively. In this study we addressed the question how the molecular recognition of strychnine and d-TC occurs with high affinity and yet low specificity towards diverse CLR family members. X-ray crystal structures of the complexes with AChBP, a well-described structural homolog of the extracellular domain of the nAChRs, revealed that strychnine and d-TC adopt multiple occupancies and different ligand orientations, stabilizing the homopentameric protein in an asymmetric state. This introduces a new level of structural diversity in CLRs. Unlike protein and peptide neurotoxins, strychnine and d-TC form a limited number of contacts in the binding pocket of AChBP, offering an explanation for their low selectivity. Based on the ligand interactions observed in strychnine- and d-TC-AChBP complexes we performed alanine-scanning mutagenesis in the binding pocket of the human α1 GlyR and α7 nAChR and showed the functional relevance of these residues in conferring high potency of strychnine and d-TC, respectively. Our results demonstrate that a limited number of ligand interactions in the binding pocket together with an energetic stabilization of the extracellular domain are key to the poor selective recognition of strychnine and d-TC by CLRs as diverse as the GlyR, nAChR, and 5-HT(3)R.

Family history of cancer and risk of sporadic differentiated thyroid carcinoma.

PubMed

Xu, Li; Li, Guojun; Wei, Qingyi; El-Naggar, Adel K; Sturgis, Erich M

2012-03-01

Thyroid cancer incidence in the United States, particularly in women, has increased dramatically since the 1980s. Although the causes of thyroid cancer in most patients remain largely unknown, evidence suggests the existence of an inherited predisposition to development of differentiated thyroid carcinoma (DTC). Therefore, the authors explored the association between sporadic DTC and family history of cancer. In a retrospective hospital-based case-control study of prospectively recruited subjects who completed the study questionnaire upon enrollment, unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) as estimates of the DTC risk associated with first-degree family history of cancer. The study included 288 patients with sporadic DTC and 591 cancer-free controls. Family history of thyroid cancer in first-degree relatives was associated with increased DTC risk (adjusted OR, 4.1; 95% CI, 1.7-9.9). All DTC cases in patients with a first-degree family history of thyroid cancer were cases of papillary thyroid carcinoma (PTC) (adjusted OR, 4.6; 95% CI, 1.9-11.1). Notably, the risk of PTC was highest in subjects with a family history of thyroid cancer in siblings (OR, 7.4; 95% CI, 1.8-30.4). In addition, multifocal primary tumor was more common among PTC patients with first-degree family history of thyroid cancer than among PTC patients with no first-degree family history of thyroid cancer (68.8% vs 35.5%, P = .01). The study suggests that family history of thyroid cancer in first-degree relatives, particularly in siblings, is associated with an increased risk of sporadic PTC. Copyright © 2011 American Cancer Society.

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

PubMed

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

Implementation of support vector machine for classification of speech marked hijaiyah letters based on Mel frequency cepstrum coefficient feature extraction

NASA Astrophysics Data System (ADS)

Adhi Pradana, Wisnu; Adiwijaya; Novia Wisesty, Untari

2018-03-01

Support Vector Machine or commonly called SVM is one method that can be used to process the classification of a data. SVM classifies data from 2 different classes with hyperplane. In this study, the system was built using SVM to develop Arabic Speech Recognition. In the development of the system, there are 2 kinds of speakers that have been tested that is dependent speakers and independent speakers. The results from this system is an accuracy of 85.32% for speaker dependent and 61.16% for independent speakers.

A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

NASA Astrophysics Data System (ADS)

He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

2014-02-01

Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

76 FR 44057 - Sunshine Act Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-22

... SECURITIES AND EXCHANGE COMMISSION Sunshine Act Meeting Notice is hereby given, pursuant to the provisions of the Government in the Sunshine Act, Public Law 94-409, that the Securities and Exchange... (DTC). DTC operates an automated, centralized system for book-entry movement of securities positions in...

Health care knowledge and consumer learning: the case of direct-to-consumer drug advertising.

PubMed

Delbaere, Marjorie; Smith, Malcolm C

2006-01-01

This research develops a framework for understanding how consumers process health-related information and interact with their caregivers. The context is direct-to-consumer (DTC) advertising by pharmaceutical companies in North America. This theoretical research presents a research framework and focuses on the presentation of information in advertisements, consumer-learning processes, consumer utilization of health care knowledge, and bias in perceived risk. The paper proposes that consumers who lack expertise with prescription drugs learn from DTC ads differently than those with expertise. Further, it is proposed that consumers also process the information in DTC ads differently depending on the perceived effectiveness of the drug being advertised, and ultimately utilize the knowledge taken from the ads in many different ways, some of which may appear irrational to health care providers. By understanding how consumers interpret and learn from DTC ads, health care organizations and providers may be able to improve health care delivery and consumer outcomes.

Direct-to-consumer advertising and its utility in health care decision making: a consumer perspective.

PubMed

Deshpande, Aparna; Menon, Ajit; Perri, Matthew; Zinkhan, George

2004-01-01

The growth in direct-to-consumer advertising(DTCA)over the past two decades has facilitated the communication of prescription drug information directly to consumers. Data from a 1999 national survey are employed to determine the factors influencing consumers' opinions of the utility of DTC ads for health care decision making. We also analyze whether consumers use DTC ad information in health care decision making and who are the key drivers of such information utilization. The study results suggest that consumers have positive opinions of DTCA utility, varying across demographics and perceptions of certain advertisement features. Specifically, consumers value information about both risks and benefits, but the perception of risk information is more important in shaping opinions of ad utility than the perception of benefit information. Consumers still perceive, however that the quality of benefit information in DTC ads is better than that of risk information. Opinions about ad utility significantly influence whether information from DTC ads is used in health care decision making.

The effect of disease risk probability and disease type on interest in clinic-based versus direct-to-consumer genetic testing services.

PubMed

Sherman, Kerry; Shaw, Laura-Kate; Champion, Katrina; Caldeira, Fernanda; McCaskill, Margaret

2015-10-01

The effect of disease-specific cognitions on interest in clinic-based and direct-to-consumer (DTC) genetic testing was assessed. Participants (N = 309) responded to an online hypothetical scenario and received genetic testing-related messages that varied by risk probability (25, 50, 75 %) and disease type (Alzheimer's disease vs. Type 2 Diabetes). Post-manipulation interest increased for both testing types, but was greater for clinic-based testing. Interest was greater for Type 2 Diabetes than for Alzheimer's disease, the latter perceived as more severe and likely, and less treatable and preventable. For DTC testing only, participants allocated to the high risk condition (75 %) had greater testing interest than those in the low (25 %) category. DTC testing is perceived as a viable, but less preferred, option compared with clinic-based testing. Particularly when considering DTC genetic testing, there is a need to emphasize subjective disease-related perceptions, including risk probability.

Direct-to-consumer communication on prescription only medicines via the internet in the Netherlands, a pilot study. Opinion of the pharmaceutical industry, patient associations and support groups.

PubMed

Fabius, A Mariette; Cheung, Ka-Chun; Rijcken, Cristianne J F; Vinkers, Christiaan H; Talsma, Herre

2004-06-01

Investigation of the current application of direct-to-consumer (DTC) communication on prescription only medicines via the Intemet in the Netherlands. Questionnaires were sent by e-mail to 43 Dutch innovative pharmaceutical industries and 130 Patient Association and Support Groups (PASGs). In this pilot study, the response of the pharmaceutical industry was rather low but the impression is that they were willing to invest in DTC communication. The majority of the websites of PASGs did not link to websites of pharmaceutical companies. The PASGs had no opinion whether patients can make a good distinction between DTC advertising and information on websites of the pharmaceutical industry nor about the quality. PASGs did not think unambiguously about the impact on the patient-doctor relationship. The impact of DTC communication on prescription only medicines via the internet is not yet clear in the Netherlands.

Direct-to-consumer marketing of psychological treatments for anxiety disorders.

PubMed

Gallo, Kaitlin P; Comer, Jonathan S; Barlow, David H

2013-12-01

Progress disseminating and implementing evidence-based psychological treatments (EBPTs) for the anxiety disorders has been gradual. To date, the dominant approach for promoting the uptake of EBPTs in clinical settings has been to target the education and training of mental health providers, with many consumers remaining unaware of the potential benefits of EBPTs for anxiety disorders. Direct-to-consumer (DTC) marketing may be a promising vehicle for increasing EBPT utilization rates in the treatment of anxiety disorders. This paper provides an overview of the rationale and important considerations for applying DTC efforts to promote evidence-based care in the treatment of anxiety disorders, and reviews current DTC efforts in this area, including resources on the Internet and other media and in-person events. We conclude with recommendations for future efforts in the DTC marketing of EBPTs for the anxiety disorders, including the need for increased funding and grassroots efforts to inform consumers about anxiety disorders and their most effective treatments. Copyright © 2013 Elsevier Ltd. All rights reserved.

The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants

NASA Astrophysics Data System (ADS)

Sohrabi, Mahmoud Reza; Darabi, Golnaz

2016-01-01

Flavonoids are γ-benzopyrone derivatives, which are highly regarded in these researchers for their antioxidant property. In this study, two new signals processing methods been coupled with UV spectroscopy for spectral resolution and simultaneous quantitative determination of Myricetin, Kaempferol and Quercetin as flavonoids in Laurel, St. John's Wort and Green Tea without the need for any previous separation procedure. The developed methods are continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM) methods integrated with UV spectroscopy individually. Different wavelet families were tested by CWT method and finally the Daubechies wavelet family (Db4) for Myricetin and the Gaussian wavelet families for Kaempferol (Gaus3) and Quercetin (Gaus7) were selected and applied for simultaneous analysis under the optimal conditions. The LS-SVM was applied to build the flavonoids prediction model based on absorption spectra. The root mean square errors for prediction (RMSEP) of Myricetin, Kaempferol and Quercetin were 0.0552, 0.0275 and 0.0374, respectively. The developed methods were validated by the analysis of the various synthetic mixtures associated with a well- known flavonoid contents. Mean recovery values of Myricetin, Kaempferol and Quercetin, in CWT method were 100.123, 100.253, 100.439 and in LS-SVM method were 99.94, 99.81 and 99.682, respectively. The results achieved by analyzing the real samples from the CWT and LS-SVM methods were compared to the HPLC reference method and the results were very close to the reference method. Meanwhile, the obtained results of the one-way ANOVA (analysis of variance) test revealed that there was no significant difference between the suggested methods.

The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants.

PubMed

Sohrabi, Mahmoud Reza; Darabi, Golnaz

2016-01-05

Flavonoids are γ-benzopyrone derivatives, which are highly regarded in these researchers for their antioxidant property. In this study, two new signals processing methods been coupled with UV spectroscopy for spectral resolution and simultaneous quantitative determination of Myricetin, Kaempferol and Quercetin as flavonoids in Laurel, St. John's Wort and Green Tea without the need for any previous separation procedure. The developed methods are continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM) methods integrated with UV spectroscopy individually. Different wavelet families were tested by CWT method and finally the Daubechies wavelet family (Db4) for Myricetin and the Gaussian wavelet families for Kaempferol (Gaus3) and Quercetin (Gaus7) were selected and applied for simultaneous analysis under the optimal conditions. The LS-SVM was applied to build the flavonoids prediction model based on absorption spectra. The root mean square errors for prediction (RMSEP) of Myricetin, Kaempferol and Quercetin were 0.0552, 0.0275 and 0.0374, respectively. The developed methods were validated by the analysis of the various synthetic mixtures associated with a well- known flavonoid contents. Mean recovery values of Myricetin, Kaempferol and Quercetin, in CWT method were 100.123, 100.253, 100.439 and in LS-SVM method were 99.94, 99.81 and 99.682, respectively. The results achieved by analyzing the real samples from the CWT and LS-SVM methods were compared to the HPLC reference method and the results were very close to the reference method. Meanwhile, the obtained results of the one-way ANOVA (analysis of variance) test revealed that there was no significant difference between the suggested methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction of toxic metals concentration using artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Gholami, R.; Kamkar-Rouhani, A.; Doulati Ardejani, F.; Maleki, Sh.

2011-12-01

Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN.

Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

PubMed

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

PubMed Central

Yao, Yiqing; Xu, Xiaosu

2017-01-01

In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. PMID:28245549

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages.

PubMed

Yao, Yiqing; Xu, Xiaosu

2017-02-24

In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends.

PubMed

Shao, Xiaolong; Li, Hui; Wang, Nan; Zhang, Qiang

2015-10-21

An electronic nose (e-nose) was used to characterize sesame oils processed by three different methods (hot-pressed, cold-pressed, and refined), as well as blends of the sesame oils and soybean oil. Seven classification and prediction methods, namely PCA, LDA, PLS, KNN, SVM, LASSO and RF, were used to analyze the e-nose data. The classification accuracy and MAUC were employed to evaluate the performance of these methods. The results indicated that sesame oils processed with different methods resulted in different sensor responses, with cold-pressed sesame oil producing the strongest sensor signals, followed by the hot-pressed sesame oil. The blends of pressed sesame oils with refined sesame oil were more difficult to be distinguished than the blends of pressed sesame oils and refined soybean oil. LDA, KNN, and SVM outperformed the other classification methods in distinguishing sesame oil blends. KNN, LASSO, PLS, and SVM (with linear kernel), and RF models could adequately predict the adulteration level (% of added soybean oil) in the sesame oil blends. Among the prediction models, KNN with k = 1 and 2 yielded the best prediction results.

Improved direct torque control of an induction generator used in a wind conversion system connected to the grid.

PubMed

Abdelli, Radia; Rekioua, Djamila; Rekioua, Toufik; Tounzi, Abdelmounaïm

2013-07-01

This paper presents a modulated hysteresis direct torque control (MHDTC) applied to an induction generator (IG) used in wind energy conversion systems (WECs) connected to the electrical grid through a back-to-back converter. The principle of this strategy consists in superposing to the torque reference a triangular signal, as in the PWM strategy, with the desired switching frequency. This new modulated reference is compared to the estimated torque by using a hysteresis controller as in the classical direct torque control (DTC). The aim of this new approach is to lead to a constant frequency and low THD in grid current with a unit power factor and a minimum voltage variation despite the wind variation. To highlight the effectiveness of the proposed method, a comparison was made with classical DTC and field oriented control method (FOC). The obtained simulation results, with a variable wind profile, show an adequate dynamic of the conversion system using the proposed method compared to the classical approaches. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Thyroid Patient Salivary Radioiodine Transit and Dysfunction Assessment Using Chewing Gums.

PubMed

Okkalides, Demetrios

2016-11-01

Radiation-induced salivary gland dysfunction is the most frequent side-effect of I-131 thyroid therapy. Here, a novel saliva sampling method with ordinary chewing gums administered to the patients at appropriate time intervals post-treatment (TIPT) was used to relate this effect to chewing gum saliva activity (CGSA) content. Saliva samples were acquired after the oral administration of prescribed I-131 activity (radioactivity administered [RA]) to 19 differentiated thyroid cancer (DTC) and 16 hyperthyroidism patients of the radioisotope unit (RIU) during 2014 and 2015. The error of this saliva collecting process was found to be 1.2%-2.05%, and so, the method was considered satisfactory. For each patient, the CGSA was plotted against the TIPT producing a curve, R(t). On this, two functions were fitted: a linear on the first few rising data points and a gamma variate over the peak of the R(t). From these, several parameters related to the radioactivity oral transit were calculated and the total radioactivity administered (TRA) during all past treatments of each patient was obtained from RIU records. The patients were asked to report any swelling, dry mouth, taste-smell change, or pain and were graded as a morbidity score (MS) describing the quality of life of each. The peak radioactivity in the saliva samples, R max , was found to be proportional to RA and was plotted against the CGSA extrapolated at 24 and 36 hours. The linear fits produced were used to estimate the salivary glands' activity average effective half-life (16.3 hours). The MS of DTC patients was found to depend linearly both on R max and TRA (MS = 0.0032 × R max - 0.7107 and MS = 0.1862 × TRA +0.66, respectively). Both lines were used to extrapolate symptom thresholds. The measurement of R max in DTC patients proved very useful for individualized radiation protection, and the dependence of MS on TRA should be used when additional treatments are considered for repeat DTC patients.

Effects of image congruency on persuasiveness and recall in direct-to-consumer prescription drug advertising.

PubMed

Kiernicki, Kristen; Helme, Donald W

2017-01-01

Although direct-to-consumer (DTC) prescription drug advertising is regulated by the U.S. Food and Drug Administration, content analyses suggest advertisers may not disclose drug risks in the same way they describe drug benefits. This study tests the relationship between image congruency in televised DTC advertisements, recall of risks/benefits, and perceived persuasiveness. Advertisements for Nasonex, Advair, and Lunesta were shown to college students in either their original (image incongruent) or modified (image neutral) form. Risks were easier to recall with image-neutral advertisements. Gender also had a significant interaction effect, suggesting that males and females process DTC advertisement differently.

Novel EO/IR sensor technologies

NASA Astrophysics Data System (ADS)

Lewis, Keith

2011-10-01

The requirements for advanced EO/IR sensor technologies are discussed in the context of evolving military operations, with significant emphasis on the development of new sensing technologies to meet the challenges posed by asymmetric threats. The Electro-Magnetic Remote Sensing (EMRS DTC) was established in 2003 to provide a centre of excellence in sensor research and development, supporting new capabilities in key military areas such as precision attack, battlespace manoeuvre and information superiority. In the area of advanced electro-optic technology, the DTC has supported work on discriminative imaging, advanced detectors, laser components/technologies, and novel optical techniques. This paper provides a summary of some of the EO/IR technologies explored by the DTC.

Online, direct-to-consumer access to insulin: patient safety considerations and reform.

PubMed

Lovett, Kimberly M; Liang, Bryan A; Mackey, Timothy K

2012-11-01

The online, direct-to-consumer (DTC) medical marketplace is proliferating more rapidly than regulation is evolving to ensure proper patient safety and public health controls. Along with this growing body of unrestrained medical testing and pharmaceuticals offered DTC online, most types of insulin and insulin administration products may now be purchased without prescriptions or physician guidance. Given the relatively significant risks of insulin use, the abuse potential, the high prevalence of diabetes mellitus, and the rising population of uninsured and underinsured, it is imperative to reform the online DTC medical marketplace to ensure that patient safety and public health are protected. © 2012 Diabetes Technology Society.

Photolithography of Dithiocarbamate-Anchored Monolayers and Polymers on Gold

PubMed Central

Leonov, Alexei P.; Wei, Alexander

2011-01-01

Dithiocarbamate (DTC)-anchored monolayers and polymers were investigated as positive resists for UV photolithography on planar and roughened Au surfaces. DTCs were formed in situ by the condensation of CS2 with monovalent or polyvalent amines such as linear polyethyleneimine (PEI) under mildly basic aqueous conditions, just prior to surface passivation. The robust adsorption of the polyvalent PEI-DTC to Au surfaces supported high levels of resistance to photoablation, providing opportunities to generate thin films with gradient functionality. Treatment of photopatterned substrates with alkanethiols produced binary coatings, enabling a direct visual comparison of DTC- and thiol-passivated surfaces against chemically induced corrosion using confocal microscopy. PMID:21894240

Visual elements in direct-to-consumer advertising: Messages communicated to patients with arthritis.

PubMed

Willis, Erin

2017-01-01

Direct-to-consumer (DTC) advertising saturates popular health magazines, communicating persuasive messages to readers that may influence attitudes and behaviors. This research used a two-prong approach to investigate the visual elements used in DTC advertising and their influence on consumers' understanding of a disease and its treatment options. An analysis was conducted of DTC advertisements (N = 62) from a population sample of Arthritis Today magazine, 2000-2010. Three panels of people with arthritis were used to validate the findings and discuss implications for health literacy. Pharmaceutical companies have an opportunity to communicate tailored messages to readers of niche publications and improve disease management.

Online, Direct-to-Consumer Access to Insulin: Patient Safety Considerations and Reform

PubMed Central

Lovett, Kimberly M.; Liang, Bryan A.; Mackey, Timothy K.

2012-01-01

The online, direct-to-consumer (DTC) medical marketplace is proliferating more rapidly than regulation is evolving to ensure proper patient safety and public health controls. Along with this growing body of unrestrained medical testing and pharmaceuticals offered DTC online, most types of insulin and insulin administration products may now be purchased without prescriptions or physician guidance. Given the relatively significant risks of insulin use, the abuse potential, the high prevalence of diabetes mellitus, and the rising population of uninsured and underinsured, it is imperative to reform the online DTC medical marketplace to ensure that patient safety and public health are protected. PMID:23294798

Direct-to-consumer drug advertising: you get what you pay for.

PubMed

Jeffords, James M

2004-01-01

Between 1997 and 2001 spending on direct-to-consumer (DTC) drug advertising more than doubled. Opinions differ as to whether and to what extent DTC advertising benefits the doctor-patient relationship. Some analysts argue that the current regulatory regime is sufficient, others advocate a stricter enforcement, and still others promote an outright ban. An alternative may be to use the purchasing power of the federal government to require the inclusion of comparative quality data, thus creating a basis for more informed consumer choice. This approach could create incentives for the pharmaceutical industry to adjust spending on DTC advertising while avoiding "big government" interference with commercial free speech.

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

NASA Astrophysics Data System (ADS)

Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

2017-04-01

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM).

PubMed

Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

2017-04-15

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms. Copyright © 2017. Published by Elsevier B.V.

Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

PubMed Central

Shahlaei, M.; Saghaie, L.

2014-01-01

A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

Working set selection using functional gain for LS-SVM.

PubMed

Bo, Liefeng; Jiao, Licheng; Wang, Ling

2007-09-01

The efficiency of sequential minimal optimization (SMO) depends strongly on the working set selection. This letter shows how the improvement of SMO in each iteration, named the functional gain (FG), is used to select the working set for least squares support vector machine (LS-SVM). We prove the convergence of the proposed method and give some theoretical support for its performance. Empirical comparisons demonstrate that our method is superior to the maximum violating pair (MVP) working set selection.

A method of distributed avionics data processing based on SVM classifier

NASA Astrophysics Data System (ADS)

Guo, Hangyu; Wang, Jinyan; Kang, Minyang; Xu, Guojing

2018-03-01

Under the environment of system combat, in order to solve the problem on management and analysis of the massive heterogeneous data on multi-platform avionics system, this paper proposes a management solution which called avionics "resource cloud" based on big data technology, and designs an aided decision classifier based on SVM algorithm. We design an experiment with STK simulation, the result shows that this method has a high accuracy and a broad application prospect.

A Simple Deep Learning Method for Neuronal Spike Sorting

NASA Astrophysics Data System (ADS)

Yang, Kai; Wu, Haifeng; Zeng, Yu

2017-10-01

Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Wire connector classification with machine vision and a novel hybrid SVM

NASA Astrophysics Data System (ADS)

Chauhan, Vedang; Joshi, Keyur D.; Surgenor, Brian W.

2018-04-01

A machine vision-based system has been developed and tested that uses a novel hybrid Support Vector Machine (SVM) in a part inspection application with clear plastic wire connectors. The application required the system to differentiate between 4 different known styles of connectors plus one unknown style, for a total of 5 classes. The requirement to handle an unknown class is what necessitated the hybrid approach. The system was trained with the 4 known classes and tested with 5 classes (the 4 known plus the 1 unknown). The hybrid classification approach used two layers of SVMs: one layer was semi-supervised and the other layer was supervised. The semi-supervised SVM was a special case of unsupervised machine learning that classified test images as one of the 4 known classes (to accept) or as the unknown class (to reject). The supervised SVM classified test images as one of the 4 known classes and consequently would give false positives (FPs). Two methods were tested. The difference between the methods was that the order of the layers was switched. The method with the semi-supervised layer first gave an accuracy of 80% with 20% FPs. The method with the supervised layer first gave an accuracy of 98% with 0% FPs. Further work is being conducted to see if the hybrid approach works with other applications that have an unknown class requirement.

A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

NASA Astrophysics Data System (ADS)

Shamshirband, Shahaboddin; Mohammadi, Kasra; Tong, Chong Wen; Zamani, Mazdak; Motamedi, Shervin; Ch, Sudheer

2016-07-01

In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination ( R 2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

Waterbodies Extraction from LANDSAT8-OLI Imagery Using Awater Indexs-Guied Stochastic Fully-Connected Conditional Random Field Model and the Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, X.; Xu, L.

2018-04-01

One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method's performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.

Third-person effects and direct-to-consumer advertisements for antidepressants.

PubMed

Taylor, Laramie D; Bell, Robert A; Kravitz, Richard L

2011-02-01

This study examines the evidence for a third- person effect (TPE) in the reactions of individuals affected by depression to direct-to-consumer (DTC) advertisements for antidepressants. TPE predicts that people will perceive the self to be less vulnerable to such advertisements than others. Previous research has identified such an effect, but did so in general population surveys. Past Previous research has also found a link between depression and diminished self-serving biases; whether this would be the case for TPE is unknown. An online questionnaire was administered to 148 participants in an Internet depression support group to investigate their perceptions of the influence of direct-to-consumer (DTC) advertisements for antidepressants. Consistent with expectations derived from third-person effect TPE research, participants, although relatively neutral in their attitudes toward such advertisements, nevertheless perceived other individuals with depression as more influenced than themselves. Positive attitudes towards DTC advertisements and depressive symptoms at the time of the survey were each negatively associated with this third-person perception (TPE). Individuals who have been diagnosed with depression and who participated in an online depression support group believe that they are less vulnerable to the influence of DTC advertisements than the typical person with a history of depression. This is moderated by attitudes towards DTC advertisements as well as by depressive symptoms, each of which is associated with a weakened TPE. © 2010 Wiley-Liss, Inc.

Ethical Issues for Direct-to-Consumer Digital Psychotherapy Apps: Addressing Accountability, Data Protection, and Consent.

PubMed

Martinez-Martin, Nicole; Kreitmair, Karola

2018-04-23

This paper focuses on the ethical challenges presented by direct-to-consumer (DTC) digital psychotherapy services that do not involve oversight by a professional mental health provider. DTC digital psychotherapy services can potentially assist in improving access to mental health care for the many people who would otherwise not have the resources or ability to connect with a therapist. However, the lack of adequate regulation in this area exacerbates concerns over how safety, privacy, accountability, and other ethical obligations to protect an individual in therapy are addressed within these services. In the traditional therapeutic relationship, there are ethical obligations that serve to protect the interests of the client and provide warnings. In contrast, in a DTC therapy app, there are no clear lines of accountability or associated ethical obligations to protect the user seeking mental health services. The types of DTC services that present ethical challenges include apps that use a digital platform to connect users to minimally trained nonprofessional counselors, as well as services that provide counseling steered by artificial intelligence and conversational agents. There is a need for adequate oversight of DTC nonprofessional psychotherapy services and additional empirical research to inform policy that will provide protection to the consumer. ©Nicole Martinez-Martin, Karola Kreitmair. Originally published in JMIR Mental Health (http://mental.jmir.org), 23.04.2018.

Direct-to-Consumer Genetic Testing: User Motivations, Decision Making, and Perceived Utility of Results.

PubMed

Roberts, J Scott; Gornick, Michele C; Carere, Deanna Alexis; Uhlmann, Wendy R; Ruffin, Mack T; Green, Robert C

2017-01-01

To describe the interests, decision making, and responses of consumers of direct-to-consumer personal genomic testing (DTC-PGT) services. Prior to 2013 regulatory restrictions on DTC-PGT services, 1,648 consumers from 2 leading companies completed Web surveys before and after receiving test results. Prior to testing, DTC-PGT consumers were as interested in ancestry (74% very interested) and trait information (72%) as they were in disease risks (72%). Among disease risks, heart disease (68% very interested), breast cancer (67%), and Alzheimer disease (66%) were of greatest interest prior to testing. Interest in disease risks was associated with female gender and poorer self-reported health (p < 0.01). Many consumers (38%) did not consider the possibility of unwanted information before purchasing services; this group was more likely to be older, male, and less educated (p < 0.05). After receiving results, 59% of respondents said test information would influence management of their health; 2% reported regret about seeking testing and 1% reported harm from results. DTC-PGT has attracted controversy because of the health-related information it provides, but nonmedical information is of equal or greater interest to consumers. Although many consumers did not fully consider potential risks prior to testing, DTC-PGT was generally perceived as useful in informing future health decisions. © 2017 S. Karger AG, Basel.

Prescription medication changes following direct-to-consumer personal genomic testing: findings from the Impact of Personal Genomics (PGen) Study.

PubMed

Carere, Deanna Alexis; VanderWeele, Tyler J; Vassy, Jason L; van der Wouden, Cathelijne H; Roberts, J Scott; Kraft, Peter; Green, Robert C

2017-05-01

To measure the frequency of prescription medication changes following direct-to-consumer personal genomic testing (DTC-PGT) and their association with the pharmacogenomic results received. New DTC-PGT customers were enrolled in 2012 and completed surveys prior to the return of results and 6 months after results; DTC-PGT results were linked to survey data. "Atypical response" pharmacogenomic results were defined as those indicating an increase or decrease in risk of an adverse drug event or likelihood of therapeutic benefit. At follow-up, participants reported prescription medication changes and health-care provider consultation. Follow-up data were available from 961 participants, of whom 54 (5.6%) reported changing a medication they were taking or starting a new medication due to their DTC-PGT results. Of these, 45 (83.3%) reported consulting with a health-care provider regarding the change. Pharmacogenomic results were available for 961 participants, of which 875 (91.2%) received one or more atypical response results. For each such result received, the odds of reporting a prescription medication change increased 1.57 times (95% confidence interval = 1.17, 2.11). Receipt of pharmacogenomic results indicating an atypical drug response is common with DTC-PGT and is associated with prescription medication changes; however, fewer than 1% of consumers report unsupervised changes at 6 months after testing.Genet Med advance online publication 22 September 2016.

The Impact of Risk Information Exposure on Women’s Beliefs about Direct-to-Consumer Genetic Testing for BRCA Mutations

PubMed Central

Gray, Stacy W.; Hornik, Robert C.; Schwartz, J. Sanford; Armstrong, Katrina

2011-01-01

Despite an increase in direct-to-consumer (DTC) genetic testing, little is known about how variations in website content might alter consumer behavior. We evaluated the impact of risk information provision on women’s attitudes about DTC BRCA testing. We conducted a randomized experiment; women viewed a “mock” BRCA testing website without (control group: CG) or with information on the potential risks of DTC testing (RG; framed two ways: unattributed information [UR] and information presented by experts [ER]). 767 women participated; mean age was 37 years, mean education was 15 years, and 79% of subjects were white. Women in the RG had less positive beliefs about DTC testing (mean RG=23.8, CG=25.2; p=0.001), lower intentions to get tested (RG= 2.8, CG= 3.1; p=0.03), were more likely to prefer clinic-based testing (RG=5.1, CG=4.8; p=0.03) and to report that they had seen enough risk information (RG=5.3, CG= 4.7; p<0.001). UR and ER exposure produced similar effects. Effects did not differ for women with or without a personal/family history of breast/ovarian cancer. Exposing women to the potential risks of DTC BRCA testing altered their beliefs, preferences, and intentions. Risk messages appear to be salient to women irrespective of their chance of having a BRCA mutation. PMID:21992449

[Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

PubMed

Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

2013-12-01

The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

Rapid authentication of edible bird's nest by FTIR spectroscopy combined with chemometrics.

PubMed

Guo, Lili; Wu, Yajun; Liu, Mingchang; Ge, Yiqiang; Chen, Ying

2018-06-01

Edible bird's nests (EBNs) have been traditionally regarded as a kind of medicinal and healthy food in China. For economic reasons, they are frequently subjected to adulteration with some cheaper substitutes, such as Tremella fungus, agar, fried pigskin, and egg white. As a kind of precious and functional product, it is necessary to establish a robust method for the rapid authentication of EBNs with small amounts of samples by simple processes. In this study, the Fourier transform infrared spectroscopy (FTIR) system was utilized and its feasibility for identification of EBNs was verified. FTIR spectra data of authentic and adulterated EBNs were analyzed by chemometrics analyses including principal component analysis, linear discriminant analysis (LDA), support vector machine (SVM) and one-class partial least squares (OCPLS). The results showed that the established LDA and SVM models performed well and had satisfactory classification ability, with the former 94.12% and the latter 100%. The OCPLS model was developed with prediction sensitivity of 0.937 and specificity of 0.886. Further detection of commercial EBN samples confirmed these results. FTIR is applicable in the scene of rapid authentication of EBNs, especially for quality supervision departments, entry-exit inspection and quarantine, and customs administration. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Daily River Flow Forecasting with Hybrid Support Vector Machine – Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Zaini, N.; Malek, M. A.; Yusoff, M.; Mardi, N. H.; Norhisham, S.

2018-04-01

The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

78 FR 33876 - Self-Regulatory Organizations; The Depository Trust Company; Notice of Filing of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-05

... functionalities allow receiving DTC participants (``Participants'') to exercise control over which transactions to... without regard to risk management controls, the Participant that initiated the original delivery versus... Participant and to the Corporation.\\5\\ \\5\\ DTC's risk management controls, including Collateral Monitor and...

Topsoil thickness effects on phosphorus and potassium dynamics on claypan soils

USDA-ARS?s Scientific Manuscript database

Due to variable depth to claypan (DTC) across landscapes, nutrient supply from subsoils, and crop removal, precise P and K fertilizer management on claypan soil fields can be difficult. Therefore, a study was performed to determine if DTC derived from soil apparent electrical conductivity (ECa) coul...

78 FR 49271 - Agency Information Collection Activities; Announcement of Office of Management and Budget...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-13

...; Communicating Composite Scores in Direct-to-Consumer Advertising AGENCY: Food and Drug Administration, HHS... information entitled ``Communicating Composite Scores in Direct-to-Consumer (DTC) Advertising'' has been... ``Communicating Composite Scores in Direct-to-Consumer (DTC) Advertising'' to OMB for review and clearance under...

How Drug Treatment Courts Work: An Analysis of Mediators

ERIC Educational Resources Information Center

Gottfredson, Denise C.; Kearley, Brook W.; Najaka, Stacy S.; Rocha, Carlos M.

2007-01-01

This study examines program elements related to reductions in drug use and crime among Drug Treatment Courts (DTC) participants as well as theoretical mechanisms--increased social controls and improved perceptions of procedural justice--expected to mediate the effects of DTC on these outcomes. Data are from 157 research participants interviewed…

A sensitive, support-vector-machine method for the detection of horizontal gene transfers in viral, archaeal and bacterial genomes.

PubMed

Tsirigos, Aristotelis; Rigoutsos, Isidore

2005-01-01

In earlier work, we introduced and discussed a generalized computational framework for identifying horizontal transfers. This framework relied on a gene's nucleotide composition, obviated the need for knowledge of codon boundaries and database searches, and was shown to perform very well across a wide range of archaeal and bacterial genomes when compared with previously published approaches, such as Codon Adaptation Index and C + G content. Nonetheless, two considerations remained outstanding: we wanted to further increase the sensitivity of detecting horizontal transfers and also to be able to apply the method to increasingly smaller genomes. In the discussion that follows, we present such a method, Wn-SVM, and show that it exhibits a very significant improvement in sensitivity compared with earlier approaches. Wn-SVM uses a one-class support-vector machine and can learn using rather small training sets. This property makes Wn-SVM particularly suitable for studying small-size genomes, similar to those of viruses, as well as the typically larger archaeal and bacterial genomes. We show experimentally that the new method results in a superior performance across a wide range of organisms and that it improves even upon our own earlier method by an average of 10% across all examined genomes. As a small-genome case study, we analyze the genome of the human cytomegalovirus and demonstrate that Wn-SVM correctly identifies regions that are known to be conserved and prototypical of all beta-herpesvirinae, regions that are known to have been acquired horizontally from the human host and, finally, regions that had not up to now been suspected to be horizontally transferred. Atypical region predictions for many eukaryotic viruses, including the alpha-, beta- and gamma-herpesvirinae, and 123 archaeal and bacterial genomes, have been made available online at http://cbcsrv.watson.ibm.com/HGT_SVM/.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

PubMed Central

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens

2018-01-01

Background EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Discussion Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings. PMID:29492336

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

PubMed

Zhang, Jiang; Wang, James Z; Yuan, Zhen; Sobel, Eric S; Jiang, Huabei

2011-01-01

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

PubMed Central

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

Support vector machine-based facial-expression recognition method combining shape and appearance

NASA Astrophysics Data System (ADS)

Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

2010-11-01

Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

Multisource image fusion method using support value transform.

PubMed

Zheng, Sheng; Shi, Wen-Zhong; Liu, Jian; Zhu, Guang-Xi; Tian, Jin-Wen

2007-07-01

With the development of numerous imaging sensors, many images can be simultaneously pictured by various sensors. However, there are many scenarios where no one sensor can give the complete picture. Image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors. In this paper, we proposed a new image fusion method using the support value transform, which uses the support value to represent the salient features of image. This is based on the fact that, in support vector machines (SVMs), the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. The mapped least squares SVM (mapped LS-SVM) is used to efficiently compute the support values of image. The support value analysis is developed by using a series of multiscale support value filters, which are obtained by filling zeros in the basic support value filter deduced from the mapped LS-SVM to match the resolution of the desired level. Compared with the widely used image fusion methods, such as the Laplacian pyramid, discrete wavelet transform methods, the proposed method is an undecimated transform-based approach. The fusion experiments are undertaken on multisource images. The results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (Q(AB/F)), the mutual information, etc.

Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Zhang, Qigui; Deng, Kai

2016-12-01

As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Binning in Gaussian Kernel Regularization

DTIC Science & Technology

2005-04-01

OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, but reduces the...71.40%) on 966 randomly sampled data. Using the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the...the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, and reduces

Direct-to-consumer genetic testing: perceptions, problems, and policy responses.

PubMed

Caulfield, Timothy; McGuire, Amy L

2012-01-01

Direct-to-consumer (DTC) genetic testing has attracted a great amount of attention from policy makers, the scientific community, professional groups, and the media. Although it is unclear what the public demand is for these services, there does appear to be public interest in personal genetic risk information. As a result, many commentators have raised a variety of social, ethical, and regulatory issues associated with this emerging industry, including privacy issues, ensuring that DTC companies provide accurate information about the risks and limitations of their services, the possible adverse impact of DTC genetic testing on healthcare systems, and concern about how individuals may interpret and react to genetic risk information.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

PubMed

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens; Comani, Silvia

2018-01-01

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity ( p ). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

[Identification of varieties of textile fibers by using Vis/NIR infrared spectroscopy technique].

PubMed

Wu, Gui-Fang; He, Yong

2010-02-01

The aim of the present paper was to provide new insight into Vis/NIR spectroscopic analysis of textile fibers. In order to achieve rapid identification of the varieties of fibers, the authors selected 5 kinds of fibers of cotton, flax, wool, silk and tencel to do a study with Vis/NIR spectroscopy. Firstly, the spectra of each kind of fiber were scanned by spectrometer, and principal component analysis (PCA) method was used to analyze the characteristics of the pattern of Vis/NIR spectra. Principal component scores scatter plot (PC1 x PC2 x PC3) of fiber indicated the classification effect of five varieties of fibers. The former 6 principal components (PCs) were selected according to the quantity and size of PCs. The PCA classification model was optimized by using the least-squares support vector machines (LS-SVM) method. The authors used the 6 PCs extracted by PCA as the inputs of LS-SVM, and PCA-LS-SVM model was built to achieve varieties validation as well as mathematical model building and optimization analysis. Two hundred samples (40 samples for each variety of fibers) of five varieties of fibers were used for calibration of PCA-LS-SVM model, and the other 50 samples (10 samples for each variety of fibers) were used for validation. The result of validation showed that Vis/NIR spectroscopy technique based on PCA-LS-SVM had a powerful classification capability. It provides a new method for identifying varieties of fibers rapidly and real time, so it has important significance for protecting the rights of consumers, ensuring the quality of textiles, and implementing rationalization production and transaction of textile materials and its production.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; García-Gonzalo, Esperanza; Vilán, José Antonio Vilán; Robleda, Abraham Segade

2015-12-01

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-SVM-based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, this hybrid PSO-SVM-based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

SVM Pixel Classification on Colour Image Segmentation

NASA Astrophysics Data System (ADS)

Barui, Subhrajit; Latha, S.; Samiappan, Dhanalakshmi; Muthu, P.

2018-04-01

The aim of image segmentation is to simplify the representation of an image with the help of cluster pixels into something meaningful to analyze. Segmentation is typically used to locate boundaries and curves in an image, precisely to label every pixel in an image to give each pixel an independent identity. SVM pixel classification on colour image segmentation is the topic highlighted in this paper. It holds useful application in the field of concept based image retrieval, machine vision, medical imaging and object detection. The process is accomplished step by step. At first we need to recognize the type of colour and the texture used as an input to the SVM classifier. These inputs are extracted via local spatial similarity measure model and Steerable filter also known as Gabon Filter. It is then trained by using FCM (Fuzzy C-Means). Both the pixel level information of the image and the ability of the SVM Classifier undergoes some sophisticated algorithm to form the final image. The method has a well developed segmented image and efficiency with respect to increased quality and faster processing of the segmented image compared with the other segmentation methods proposed earlier. One of the latest application result is the Light L16 camera.

Age and gender estimation using Region-SIFT and multi-layered SVM

NASA Astrophysics Data System (ADS)

Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Hwang, Byunghun

2018-04-01

In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

PubMed

Mustaqeem, Anam; Anwar, Syed Muhammad; Majid, Muahammad

2018-01-01

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure☆

PubMed Central

Frick, Andreas; Gingnell, Malin; Marquand, Andre F.; Howner, Katarina; Fischer, Håkan; Kristiansson, Marianne; Williams, Steven C.R.; Fredrikson, Mats; Furmark, Tomas

2014-01-01

Functional neuroimaging of social anxiety disorder (SAD) support altered neural activation to threat-provoking stimuli focally in the fear network, while structural differences are distributed over the temporal and frontal cortices as well as limbic structures. Previous neuroimaging studies have investigated the brain at the voxel level using mass-univariate methods which do not enable detection of more complex patterns of activity and structural alterations that may separate SAD from healthy individuals. Support vector machine (SVM) is a supervised machine learning method that capitalizes on brain activation and structural patterns to classify individuals. The aim of this study was to investigate if it is possible to discriminate SAD patients (n = 14) from healthy controls (n = 12) using SVM based on (1) functional magnetic resonance imaging during fearful face processing and (2) regional gray matter volume. Whole brain and region of interest (fear network) SVM analyses were performed for both modalities. For functional scans, significant classifications were obtained both at whole brain level and when restricting the analysis to the fear network while gray matter SVM analyses correctly classified participants only when using the whole brain search volume. These results support that SAD is characterized by aberrant neural activation to affective stimuli in the fear network, while disorder-related alterations in regional gray matter volume are more diffusely distributed over the whole brain. SVM may thus be useful for identifying imaging biomarkers of SAD. PMID:24239689

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Nonlinear Classification of AVO Attributes Using SVM

NASA Astrophysics Data System (ADS)

Zhao, B.; Zhou, H.

2005-05-01

A key research topic in reservoir characterization is the detection of the presence of fluids using seismic and well-log data. In particular, partial gas discrimination is very challenging because low and high gas saturation can result in similar anomalies in terms of Amplitude Variation with Offset (AVO), bright spot, and velocity sag. Hence, a successful fluid detection will require a good understanding of the seismic signatures of the fluids, high-quality data, and good detection methodology. Traditional attempts of partial gas discrimination employ the Neural Network algorithm. A new approach is to use the Support Vector Machine (SVM) (Vapnik, 1995; Liu and Sacchi, 2003). While the potential of the SVM has not been fully explored for reservoir fluid detection, the current nonlinear methods classify seismic attributes without the use of rock physics constraints. The objective of this study is to improve the capability of distinguishing a fizz-water reservoir from a commercial gas reservoir by developing a new detection method using AVO attributes and rock physics constraints. This study will first test the SVM classification with synthetic data, and then apply the algorithm to field data from the King-Kong and Lisa-Anne fields in Gulf of Mexico. While both field areas have high amplitude seismic anomalies, King-Kong field produces commercial gas but Lisa-Anne field does not. We expect that the new SVM-based nonlinear classification of AVO attributes may be able to separate commercial gas from fizz-water in these two fields.

78 FR 65326 - Agency Information Collection Activities; Proposed Collection; Comment Request; Experimental...

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2013-10-31

... Directed at Adolescents.'' This study is designed to examine how adolescents interpret DTC advertising... understanding of benefits and risks in DTC ads differ across this part of the lifespan. Design Overview Within... (benefit onset: immediate, delayed) x 2 (risk severity: high, low) factorial design, based on the rationale...

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Federal Register 2010, 2011, 2012, 2013, 2014

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[Personal genomics: are we debating the right Issues?].

PubMed

Vayena, E; Mauch, F

2012-07-25

The debate about personal genomics and their role in personalized medicine has been, to some extent, hijacked by the controversy about commercially available genomic tests sold directly to consumers. The clinical validity and utility of such tests are currently limited and most medical associations recommend that consumers refrain from testing. Conversely, DTC genomics proponents and particularly the DTC industry argue that there is personal utility in acquiring genomic information. While it is necessary to debate risks and benefits of DTC genomics, we should not lose sight of the increasingly important role that genomics will play in medical practice and public health. Therefore, and in anticipation of this shift we also need to focus on important implications from the use of genomics information such as genetic discrimination, privacy protection and equitable access to health care. Undoubtedly, personal genomics will challenge our social norms maybe more than our medicine. Sticking to the polarization of «to have or not to have DTC genomics» risks to takes us away from the critical issues we need to be debating.