Adrenal hormones and the anorectic response and adaptation of rats to amino acid imbalance.

PubMed

Hammer, V A; Gietzen, D W; Sworts, V D; Beverly, J L; Rogers, Q R

1990-12-01

The role of adrenal function in the anorectic response and adaptation of rats to a diet with an isoleucine (Ile) imbalance was investigated. In the first of four experiments, rats were fed a mildly Ile-imbalanced diet after treatment with metyrapone, and inhibitor of glucocorticoid synthesis. In two separate experiments, rats were presented with either a mildly or severely Ile-imbalanced diet (4.93 and 9.86% imbalanced amino acid mixture, respectively) after bilateral adrenalectomy. Finally, the effects of ICS 205-930, a serotonin-3 receptor antagonist, on the intake of mildly Ile-imbalanced diet were tested in adrenalectomized animals. In each experiment a 2 X 2 factorial design was used. Neither metyrapone nor adrenalectomy altered the initial depression in the intake of an imbalanced diet. The adaptation phase in the response of adrenalectomized rats fed a mildly Ile-imbalanced diet was not different from that of controls, but adrenalectomized rats fed severely Ile-imbalanced diets were unable to adapt. Adrenalectomy did not alter the anti-anoretic activity of ICS 205-930 in this model. These results suggest that adrenal hormones are not necessary for the initial anoretic response or adaptation of rats to an Ile-imbalanced diet, nor are they implicated in the anti-anorectic effect of serotonin-3 blockade.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

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

2017-01-01

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

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification

PubMed Central

Huang, Lingkang; Zhang, Hao Helen; Zeng, Zhao-Bang; Bushel, Pierre R.

2013-01-01

Background Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity. Results The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes. Conclusions High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention. Availability: The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html. PMID:23966761

Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

PubMed

Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

2010-09-17

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

On the role of cost-sensitive learning in multi-class brain-computer interfaces.

PubMed

Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

2010-06-01

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

Spin Imbalanced Quasi-Two-Dimensional Fermi Gases

NASA Astrophysics Data System (ADS)

Ong, Willie C.

Spin-imbalanced Fermi gases serve as a testbed for fundamental notions and are efficient table-top emulators of a variety of quantum matter ranging from neutron stars, the quark-gluon plasma, to high critical temperature superconductors. A macroscopic quantum phenomenon which occurs in spin-imbalanced Fermi gases is that of phase separation; in three dimensions, a spin-balanced, fully-paired superfluid core is surrounded by an imbalanced normal-fluid shell, followed by a fully polarized shell. In one dimension, the behavior is reversed; a balanced phase appears outside a spin-imbalanced core. This thesis details the first density profile measurements and studies on spin-imbalanced quasi-2D Fermi gases, accomplished with high-resolution, rapid sequential spin-imaging. The measured cloud radii and central densities are in disagreement with mean-field Bardeen-Cooper-Schrieffer theory for a 2D system. Data for normal-fluid mixtures are well fit by a simple 2D polaron model of the free energy. Not predicted by the model is an observed phase transition to a spin-balanced central core above a critical polarisation.

Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

PubMed

Wang, Ying; Coiera, Enrico; Runciman, William; Magrabi, Farah

2017-06-12

Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals. Text based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with "balanced" datasets (n_ Type  = 2860, n_ SeverityLevel  = 1160) from a state-wide incident reporting system. Testing was also undertaken with imbalanced "stratified" datasets (n_ Type  = 6000, n_ SeverityLevel =5950) from the state-wide system and an independent hospital reporting system. Classifier performance was evaluated using a confusion matrix, as well as F-score, precision and recall. The most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. "Documentation" was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8-84%) but precision was poor (6.8-11.2%). High risk incidents (SAC2) were confused with medium risk incidents (SAC3). Binary classifier ensembles appear to be a feasible method for identifying incidents by type and severity level. Automated identification should enable safety problems to be detected and addressed in a more timely manner. Multi-label classifiers may be necessary for reports that relate to more than one incident type.

Combining multiple decisions: applications to bioinformatics

NASA Astrophysics Data System (ADS)

Yukinawa, N.; Takenouchi, T.; Oba, S.; Ishii, S.

2008-01-01

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.

Detection of circuit-board components with an adaptive multiclass correlation filter

NASA Astrophysics Data System (ADS)

Diaz-Ramirez, Victor H.; Kober, Vitaly

2008-08-01

A new method for reliable detection of circuit-board components is proposed. The method is based on an adaptive multiclass composite correlation filter. The filter is designed with the help of an iterative algorithm using complex synthetic discriminant functions. The impulse response of the filter contains information needed to localize and classify geometrically distorted circuit-board components belonging to different classes. Computer simulation results obtained with the proposed method are provided and compared with those of known multiclass correlation based techniques in terms of performance criteria for recognition and classification of objects.

System and method for memory allocation in a multiclass memory system

DOEpatents

Loh, Gabriel; Meswani, Mitesh; Ignatowski, Michael; Nutter, Mark

2016-06-28

A system for memory allocation in a multiclass memory system includes a processor coupleable to a plurality of memories sharing a unified memory address space, and a library store to store a library of software functions. The processor identifies a type of a data structure in response to a memory allocation function call to the library for allocating memory to the data structure. Using the library, the processor allocates portions of the data structure among multiple memories of the multiclass memory system based on the type of the data structure.

An essential role for IGF2 in cartilage development and glucose metabolism during postnatal long bone growth.

PubMed

Uchimura, Tomoya; Hollander, Judith M; Nakamura, Daisy S; Liu, Zhiyi; Rosen, Clifford J; Georgakoudi, Irene; Zeng, Li

2017-10-01

Postnatal bone growth involves a dramatic increase in length and girth. Intriguingly, this period of growth is independent of growth hormone and the underlying mechanism is poorly understood. Recently, an IGF2 mutation was identified in humans with early postnatal growth restriction. Here, we show that IGF2 is essential for longitudinal and appositional murine postnatal bone development, which involves proper timing of chondrocyte maturation and perichondrial cell differentiation and survival. Importantly, the Igf2 null mouse model does not represent a simple delay of growth but instead uncoordinated growth plate development. Furthermore, biochemical and two-photon imaging analyses identified elevated and imbalanced glucose metabolism in the Igf2 null mouse. Attenuation of glycolysis rescued the mutant phenotype of premature cartilage maturation, thereby indicating that IGF2 controls bone growth by regulating glucose metabolism in chondrocytes. This work links glucose metabolism with cartilage development and provides insight into the fundamental understanding of human growth abnormalities. © 2017. Published by The Company of Biologists Ltd.

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

PubMed

Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I

2017-01-01

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.

Multiclass classification of microarray data samples with a reduced number of genes

PubMed Central

2011-01-01

Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. Results A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. Conclusions A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples. PMID:21342522

Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles.

PubMed

Yukinawa, Naoto; Oba, Shigeyuki; Kato, Kikuya; Ishii, Shin

2009-01-01

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation. Therefore, a new problem, which we call the "optimal coding problem," has arisen: how can we determine which coding is the optimal one in each situation? To approach this optimal coding problem, we propose a novel framework for constructing a multiclass classifier, in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. Although there is no a priori answer to the optimal coding problem, our weight tuning method can be a consistent answer to the problem. We apply this method to various classification problems including a synthesized data set and some cancer diagnosis data sets from gene expression profiling. The results demonstrate that, in most situations, our method can improve classification accuracy over simple voting heuristics and is better than or comparable to state-of-the-art multiclass predictors.

Imbalanced Learning for Functional State Assessment

NASA Technical Reports Server (NTRS)

Li, Feng; McKenzie, Frederick; Li, Jiang; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classes and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random undersampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving lest dataset show thai accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes,

Imbalanced class learning in epigenetics.

PubMed

Haque, M Muksitul; Skinner, Michael K; Holder, Lawrence B

2014-07-01

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

Describing three-class task performance: three-class linear discriminant analysis and three-class ROC analysis

NASA Astrophysics Data System (ADS)

He, Xin; Frey, Eric C.

2007-03-01

Binary ROC analysis has solid decision-theoretic foundations and a close relationship to linear discriminant analysis (LDA). In particular, for the case of Gaussian equal covariance input data, the area under the ROC curve (AUC) value has a direct relationship to the Hotelling trace. Many attempts have been made to extend binary classification methods to multi-class. For example, Fukunaga extended binary LDA to obtain multi-class LDA, which uses the multi-class Hotelling trace as a figure-of-merit, and we have previously developed a three-class ROC analysis method. This work explores the relationship between conventional multi-class LDA and three-class ROC analysis. First, we developed a linear observer, the three-class Hotelling observer (3-HO). For Gaussian equal covariance data, the 3- HO provides equivalent performance to the three-class ideal observer and, under less strict conditions, maximizes the signal to noise ratio for classification of all pairs of the three classes simultaneously. The 3-HO templates are not the eigenvectors obtained from multi-class LDA. Second, we show that the three-class Hotelling trace, which is the figureof- merit in the conventional three-class extension of LDA, has significant limitations. Third, we demonstrate that, under certain conditions, there is a linear relationship between the eigenvectors obtained from multi-class LDA and 3-HO templates. We conclude that the 3-HO based on decision theory has advantages both in its decision theoretic background and in the usefulness of its figure-of-merit. Additionally, there exists the possibility of interpreting the two linear features extracted by the conventional extension of LDA from a decision theoretic point of view.

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.

Discriminative least squares regression for multiclass classification and feature selection.

PubMed

Xiang, Shiming; Nie, Feiping; Meng, Gaofeng; Pan, Chunhong; Zhang, Changshui

2012-11-01

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

A Multi-Class, Interdisciplinary Project Using Elementary Statistics

ERIC Educational Resources Information Center

Reese, Margaret

2012-01-01

This article describes a multi-class project that employs statistical computing and writing in a statistics class. Three courses, General Ecology, Meteorology, and Introductory Statistics, cooperated on a project for the EPA's Student Design Competition. The continuing investigation has also spawned several undergraduate research projects in…

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Renormalization Group Theory for the Imbalanced Fermi Gas

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

Gubbels, K. B.; Stoof, H. T. C.

2008-04-11

We formulate a Wilsonian renormalization group theory for the imbalanced Fermi gas. The theory is able to recover quantitatively well-established results in both the weak-coupling and the strong-coupling (unitarity) limits. We determine for the latter case the line of second-order phase transitions of the imbalanced Fermi gas and, in particular, the location of the tricritical point. We obtain good agreement with the recent experiments of Y. Shin et al. [Nature (London) 451, 689 (2008)].

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

Diffuse Interface Methods for Multiclass Segmentation of High-Dimensional Data

DTIC Science & Technology

2014-03-04

handwritten digits , 1998. http://yann.lecun.com/exdb/mnist/. [19] S. Nene, S. Nayar, H. Murase, Columbia Object Image Library (COIL-100), Technical Report... recognition on smartphones using a multiclass hardware-friendly support vector machine, in: Ambient Assisted Living and Home Care, Springer, 2012, pp. 216–223.

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

PubMed

Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Evaluation of a multiclass, multiresidue liquid chromatography-tandem mass spectrometry method for analysis of 120 veterinary drugs in bovine kidney

USDA-ARS?s Scientific Manuscript database

Traditionally, regulatory monitoring of veterinary drug residues in food animal tissues involves the use of several single-class methods to cover a wide analytical scope. Multiclass, multiresidue methods of analysis tend to provide greater overall laboratory efficiency than the use of multiple meth...

Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants....mass spectrometry

USDA-ARS?s Scientific Manuscript database

A multi-class, multi-residue method for the analysis of 13 novel flame retardants, 18 representative pesticides, 14 polychlorinated biphenyl (PCB) congeners, 16 polycyclic aromatic hydrocarbons (PAHs), and 7 polybrominated diphenyl ether (PBDE) congeners in catfish muscle was developed and evaluated...

National Trends in Child and Adolescent Psychotropic Polypharmacy in Office-Based Practice, 1996-2007

ERIC Educational Resources Information Center

Comer, Jonathan S.; Olfson, Mark; Mojtabai, Ramin

2010-01-01

Objective: To examine patterns and recent trends in multiclass psychotropic treatment among youth visits to office-based physicians in the United States. Method: Annual data from the 1996-2007 National Ambulatory Medical Care Surveys were analyzed to examine patterns and trends in multiclass psychotropic treatment within a nationally…

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

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

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.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

PubMed

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Electrical neurostimulation with imbalanced waveform mitigates dissolution of platinum electrodes

PubMed Central

Kumsa, Doe; Hudak, Eric M; Montague, Fred W; Kelley, Shawn C; Untereker, Darrel F; Hahn, Benjamin P; Condit, Chris; Cholette, Martin; Lee, Hyowon; Bardot, Dawn; Takmakov, Pavel

2017-01-01

Objective Electrical neurostimulation has traditionally been limited to the use of charge-balanced waveforms. Charge-imbalanced and monophasic waveforms are not used to deliver clinical therapy, because it is believed that these stimulation paradigms may generate noxious electrochemical species that cause tissue damage. Approach In this study, we investigated the dissolution of platinum as one of such irreversible reactions over a range of charge densities up to 160 µC cm−2 with current-controlled first phase, capacitive discharge second phase waveforms of both cathodic-first and anodic-first polarity. We monitored the concentration of platinum in solution under different stimulation delivery conditions including charge-balanced, charge-imbalanced, and monophasic pulses. Main results We observed that platinum dissolution decreased during charge-imbalanced and monophasic stimulation when compared to charge-balanced waveforms. Significance This observation provides an opportunity to re-evaluate the charge-balanced waveform as the primary option for sustainable neural stimulation. PMID:27650936

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

Multi-class texture analysis in colorectal cancer histology

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Weis, Cleo-Aron; Bianconi, Francesco; Melchers, Susanne M.; Schad, Lothar R.; Gaiser, Timo; Marx, Alexander; Zöllner, Frank Gerrit

2016-06-01

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

2016-08-01

Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

Multiclass methods for the analysis of antibiotic residues in milk by liquid chromatography coupled to mass spectrometry: A review.

PubMed

Rossi, Rosanna; Saluti, Giorgio; Moretti, Simone; Diamanti, Irene; Giusepponi, Danilo; Galarini, Roberta

2018-02-01

Milk is an important and beneficial food from a nutritional point of view, being an indispensable source of high quality proteins. Furthermore, it is a raw material for many dairy products, such as yoghurt, cheese, cream etc. Before reaching consumers, milk goes through production, processing and circulation. Each step involves potentially unsafe factors, such as chemical contamination that can affect milk quality. Antibiotics are widely used in veterinary medicine for dry cow therapy and mastitis treatment in lactating cows, which can cause the presence of antimicrobial residues in milk. In order to ensure consumers' safety, milk is analyzed to make sure that the fixed Maximum Residue Limits (MRLs) for antibiotics are not exceeded. Multiclass methods can monitor more drug classes through a single analysis, so they are faster, less time-consuming and cheaper than traditional methods (single-class); this aspect is particularly important for milk, which is a highly perishable food. Nevertheless, multiclass methods for veterinary drug residues in foodstuffs are real analytical challenges. This article reviews the major multiclass methods published for the determination of antibiotic residues in milk by liquid chromatography coupled to mass spectrometry, with a special focus on sample preparation approaches.

COSMIC-RAY PITCH-ANGLE SCATTERING IN IMBALANCED MHD TURBULENCE SIMULATIONS

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

Weidl, Martin S.; Jenko, Frank; Teaca, Bogdan

2015-09-20

Pitch-angle scattering rates for cosmic-ray particles in MHD simulations with imbalanced turbulence are calculated for fully evolving electromagnetic turbulence. We compare with theoretical predictions derived from the quasilinear theory of cosmic-ray diffusion for an idealized slab spectrum and demonstrate how cross helicity affects the shape of the pitch-angle diffusion coefficient. Additional simulations in evolving magnetic fields or static field configurations provide evidence that the scattering anisotropy in imbalanced turbulence is not primarily due to coherence with propagating Alfvén waves, but an effect of the spatial structure of electric fields in cross-helical MHD turbulence.

Surmounting the sign problem in nonrelativistic calculations: A case study with mass-imbalanced fermions

NASA Astrophysics Data System (ADS)

Rammelmüller, Lukas; Porter, William J.; Drut, Joaquín E.; Braun, Jens

2017-11-01

The calculation of the ground state and thermodynamics of mass-imbalanced Fermi systems is a challenging many-body problem. Even in one spatial dimension, analytic solutions are limited to special configurations and numerical progress with standard Monte Carlo approaches is hindered by the sign problem. The focus of the present work is on the further development of methods to study imbalanced systems in a fully nonperturbative fashion. We report our calculations of the ground-state energy of mass-imbalanced fermions using two different approaches which are also very popular in the context of the theory of the strong interaction (quantum chromodynamics, QCD): (a) the hybrid Monte Carlo algorithm with imaginary mass imbalance, followed by an analytic continuation to the real axis; and (b) the complex Langevin algorithm. We cover a range of on-site interaction strengths that includes strongly attractive as well as strongly repulsive cases which we verify with nonperturbative renormalization group methods and perturbation theory. Our findings indicate that, for strong repulsive couplings, the energy starts to flatten out, implying interesting consequences for short-range and high-frequency correlation functions. Overall, our results clearly indicate that the complex Langevin approach is very versatile and works very well for imbalanced Fermi gases with both attractive and repulsive interactions.

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

PubMed

Saito, Takaya; Rehmsmeier, Marc

2015-01-01

Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.

Mexican Hat Wavelet Kernel ELM for Multiclass Classification.

PubMed

Wang, Jie; Song, Yi-Fan; Ma, Tian-Lei

2017-01-01

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.

Effects of imbalanced currents on large-format LiFePO4/graphite batteries systems connected in parallel

NASA Astrophysics Data System (ADS)

Shi, Wei; Hu, Xiaosong; Jin, Chao; Jiang, Jiuchun; Zhang, Yanru; Yip, Tony

2016-05-01

With the development and popularization of electric vehicles, it is urgent and necessary to develop effective management and diagnosis technology for battery systems. In this work, we design a parallel battery model, according to equivalent circuits of parallel voltage and branch current, to study effects of imbalanced currents on parallel large-format LiFePO4/graphite battery systems. Taking a 60 Ah LiFePO4/graphite battery system manufactured by ATL (Amperex Technology Limited, China) as an example, causes of imbalanced currents in the parallel connection are analyzed using our model, and the associated effect mechanisms on long-term stability of each single battery are examined. Theoretical and experimental results show that continuously increasing imbalanced currents during cycling are mainly responsible for the capacity fade of LiFePO4/graphite parallel batteries. It is thus a good way to avoid fast performance fade of parallel battery systems by suppressing variations of branch currents.

Spectral Indices in Simulations of Imbalanced Magnetohydrodynamic Turbulence

NASA Astrophysics Data System (ADS)

Ng, C. S.; Dennis, T. J.

2017-12-01

Three-dimensional (3D) simulations of imbalanced magnetohydrodynamic (MHD) turbulence based on reduced MHD equations have been performed. Alfven waves are launched from both ends of a long tube along the background uniform magnetic field so that turbulence develops due to collision between counter propagating Alfven waves in the interior region. Waves are launched randomly with specified correlation time Tc such that the length of the tube, L, is greater than (but of the same order of) VA Tc such that turbulence can fill most of the tube. While waves at both ends are launched with equal power, turbulence generated is imbalanced in general, with normalized cross-helicity gets close to -1 at one end and 1 at the other end. One fundamental unresolved problem in the theory of imbalanced turbulence is how turbulence spectral indices depend on the normalized cross-helicity. We will present turbulence spectral indices found in our latest simulations and discuss theoretical implications. This work is supported by a NASA grant NNX15AU61G.

Sex Ratio at Birth in Vietnam: Results From Data in CHILILAB HDSS, 2004 to 2013.

PubMed

Le, Vui Thi; Duong, Duc Minh; Nguyen, Anh Duy; Nguyen, Chuong Canh; Bui, Ha Thi Thu; Pham, Cuong Viet; Le, Thi Minh; Tran, Bich Huu

2017-07-01

This study aimed to explore the association of demographic and socioeconomic characteristics and imbalanced sex ratio at birth (SRB) in Chi Linh district, Hai Duong. The data were collected from a longitudinal study using a community-based periodic, referred as Chi Linh Health and Demographic Surveillance System (CHILILAB HDSS) during 2004 to 2013. A total of 7568 children were analyzed. Results showed that SRB in Chi Linh dramatically increased to the imbalanced sex ratio (114.6 boys to 100 girls) by 2013. SRB was associated with birth order and sex of preceding siblings. SRB was extremely high among families without any sons (136/100). SRB was highest among families having third or more children (175/100). Imbalanced SRB was more likely to occur among women working in small business/homemakers and others, women who attained high education level, and women in wealthy households. We suggested further efforts to tackle imbalanced SRB in periurban areas in Vietnam.

Automatic classification and detection of clinically relevant images for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets.

PubMed

García-Pedrajas, Nicolás; Perez-Rodríguez, Javier; de Haro-García, Aida

2013-02-01

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

PubMed Central

Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

2009-01-01

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

Improving Predictions of Multiple Binary Models in ILP

PubMed Central

2014-01-01

Despite the success of ILP systems in learning first-order rules from small number of examples and complexly structured data in various domains, they struggle in dealing with multiclass problems. In most cases they boil down a multiclass problem into multiple black-box binary problems following the one-versus-one or one-versus-rest binarisation techniques and learn a theory for each one. When evaluating the learned theories of multiple class problems in one-versus-rest paradigm particularly, there is a bias caused by the default rule toward the negative classes leading to an unrealistic high performance beside the lack of prediction integrity between the theories. Here we discuss the problem of using one-versus-rest binarisation technique when it comes to evaluating multiclass data and propose several methods to remedy this problem. We also illustrate the methods and highlight their link to binary tree and Formal Concept Analysis (FCA). Our methods allow learning of a simple, consistent, and reliable multiclass theory by combining the rules of the multiple one-versus-rest theories into one rule list or rule set theory. Empirical evaluation over a number of data sets shows that our proposed methods produce coherent and accurate rule models from the rules learned by the ILP system of Aleph. PMID:24696657

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition

DTIC Science & Technology

2006-04-05

classes. In this study we evaluate the effectiveness of one of these formulations that was developed by Crammer and Singer [9], which leads to...significantly more complex model can be learned by directly applying the Crammer -Singer multiclass formulation on the outputs of the binary classifiers...will refer to this as the Crammer -Singer (CS) model. Comparing the scaling approach to the Crammer -Singer approach we can see that the Crammer -Singer

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

Understanding bistability in yeast glycolysis using general properties of metabolic pathways.

PubMed

Planqué, Robert; Bruggeman, Frank J; Teusink, Bas; Hulshof, Josephus

2014-09-01

Glycolysis is the central pathway in energy metabolism in the majority of organisms. In a recent paper, van Heerden et al. showed experimentally and computationally that glycolysis can exist in two states, a global steady state and a so-called imbalanced state. In the imbalanced state, intermediary metabolites accumulate at low levels of ATP and inorganic phosphate. It was shown that Baker's yeast uses a peculiar regulatory mechanism--via trehalose metabolism--to ensure that most yeast cells reach the steady state and not the imbalanced state. Here we explore the apparent bistable behaviour in a core model of glycolysis that is based on a well-established detailed model, and study in great detail the bifurcation behaviour of solutions, without using any numerical information on parameter values. We uncover a rich suite of solutions, including so-called imbalanced states, bistability, and oscillatory behaviour. The techniques employed are generic, directly suitable for a wide class of biochemical pathways, and could lead to better analytical treatments of more detailed models. Copyright © 2014 Elsevier Inc. All rights reserved.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

PubMed

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

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

Multiparticle instability in a spin-imbalanced Fermi gas

NASA Astrophysics Data System (ADS)

Whitehead, T. M.; Conduit, G. J.

2018-01-01

Weak attractive interactions in a spin-imbalanced Fermi gas induce a multiparticle instability, binding multiple fermions together. The maximum binding energy per particle is achieved when the ratio of the number of up- and down-spin particles in the instability is equal to the ratio of the up- and down-spin densities of states in momentum at the Fermi surfaces, to utilize the variational freedom of all available momentum states. We derive this result using an analytical approach, and verify it using exact diagonalization. The multiparticle instability extends the Cooper pairing instability of balanced Fermi gases to the imbalanced case, and could form the basis of a many-body state, analogously to the construction of the Bardeen-Cooper-Schrieffer theory of superconductivity out of Cooper pairs.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

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.

Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

PubMed

Pham-The, Hai; Casañola-Martin, Gerardo; Garrigues, Teresa; Bermejo, Marival; González-Álvarez, Isabel; Nguyen-Hai, Nam; Cabrera-Pérez, Miguel Ángel; Le-Thi-Thu, Huong

2016-02-01

In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

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

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Dynamics of social balance on networks

NASA Astrophysics Data System (ADS)

Antal, T.; Krapivsky, P. L.; Redner, S.

2005-09-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad—a triangular loop with one or three unfriendly links—is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to “paradise”—all links are friendly—as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

Egocentric reciprocity and the role of friendship and anger.

PubMed

Chen, Xiao-Ping; Eberly, Marion B; Bachrach, Daniel G; Wu, Keke; Qu, Qing

2017-01-01

In this research, we examine the phenomenon of egocentric reciprocity, where individuals protect self-interest by adopting an eye-for-an-eye strategy in negatively imbalanced exchanges, and by taking advantage of overly generous treatment in positively imbalanced exchanges. We conducted two experiments using a modified ultimatum game examining attitudinal and behavioral responses to imbalanced exchanges. The experiments allowed us to explore the moderating role of relational closeness (i.e., whether the game partner was a friend or a stranger) and the mediating role of anger and indebtedness in these moderated relationships. Our results consistently demonstrate the phenomenon of egocentric reciprocity. Most importantly, this research reveals that friendship places a boundary on this egocentric tendency, and that the effects may partially be explained by anger experienced in response to exchange.

Polaron Thermodynamics of Spin-Imbalanced Quasi-Two-Dimensional Fermi Gases

NASA Astrophysics Data System (ADS)

Ong, Willie; Cheng, Chingyun; Arakelyan, Ilya; Thomas, John

2015-05-01

We present the first spatial profile measurements for spin-imbalanced mixtures of atomic 6Li fermions in a quasi-2D geometry with tunable strong interactions. The observed minority and majority profiles are not correctly predicted by BCS theory for a true 2D system, but are reasonably well fit by a 2D-polaron model of the free energy. Density difference profiles reveal a flat center with two peaks at the edges, consistent with a fully paired core of the corresponding 2D density profiles. These features are more prominent for higher interaction strengths. Not predicted by the polaron model is an observed transition from a spin-imbalanced normal fluid phase to a spin-balanced central core above a critical imbalance. Supported by ARO, DOE, AFOSR, NSF.

Fermi gases with imaginary mass imbalance and the sign problem in Monte-Carlo calculations

NASA Astrophysics Data System (ADS)

Roscher, Dietrich; Braun, Jens; Chen, Jiunn-Wei; Drut, Joaquín E.

2014-05-01

Fermi gases in strongly coupled regimes are inherently challenging for many-body methods. Although progress has been made analytically, quantitative results require ab initio numerical approaches, such as Monte-Carlo (MC) calculations. However, mass-imbalanced and spin-imbalanced gases are not accessible to MC calculations due to the infamous sign problem. For finite spin imbalance, the problem can be circumvented using imaginary polarizations and analytic continuation, and large parts of the phase diagram then become accessible. We propose to apply this strategy to the mass-imbalanced case, which opens up the possibility to study the associated phase diagram with MC calculations. We perform a first mean-field analysis which suggests that zero-temperature studies, as well as detecting a potential (tri)critical point, are feasible.

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

PubMed

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2016-09-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling

PubMed Central

Wang, Sheng; Sun, Siqi

2017-01-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC. PMID:28884168

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data

PubMed Central

Wong, Raymond K.; Mohammed, Sabah; Fiaidhi, Jinan; Sung, Yunsick

2017-01-01

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method. PMID:28753613

Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data.

PubMed

Ooi, Chia Huey; Chetty, Madhu; Teng, Shyh Wei

2006-06-23

Due to the large number of genes in a typical microarray dataset, feature selection looks set to play an important role in reducing noise and computational cost in gene expression-based tissue classification while improving accuracy at the same time. Surprisingly, this does not appear to be the case for all multiclass microarray datasets. The reason is that many feature selection techniques applied on microarray datasets are either rank-based and hence do not take into account correlations between genes, or are wrapper-based, which require high computational cost, and often yield difficult-to-reproduce results. In studies where correlations between genes are considered, attempts to establish the merit of the proposed techniques are hampered by evaluation procedures which are less than meticulous, resulting in overly optimistic estimates of accuracy. We present two realistically evaluated correlation-based feature selection techniques which incorporate, in addition to the two existing criteria involved in forming a predictor set (relevance and redundancy), a third criterion called the degree of differential prioritization (DDP). DDP functions as a parameter to strike the balance between relevance and redundancy, providing our techniques with the novel ability to differentially prioritize the optimization of relevance against redundancy (and vice versa). This ability proves useful in producing optimal classification accuracy while using reasonably small predictor set sizes for nine well-known multiclass microarray datasets. For multiclass microarray datasets, especially the GCM and NCI60 datasets, DDP enables our filter-based techniques to produce accuracies better than those reported in previous studies which employed similarly realistic evaluation procedures.

Multiclass cancer diagnosis using tumor gene expression signatures

DOE PAGES

Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

2001-12-11

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

A heuristic method for consumable resource allocation in multi-class dynamic PERT networks

NASA Astrophysics Data System (ADS)

Yaghoubi, Saeed; Noori, Siamak; Mazdeh, Mohammad Mahdavi

2013-06-01

This investigation presents a heuristic method for consumable resource allocation problem in multi-class dynamic Project Evaluation and Review Technique (PERT) networks, where new projects from different classes (types) arrive to system according to independent Poisson processes with different arrival rates. Each activity of any project is operated at a devoted service station located in a node of the network with exponential distribution according to its class. Indeed, each project arrives to the first service station and continues its routing according to precedence network of its class. Such system can be represented as a queuing network, while the discipline of queues is first come, first served. On the basis of presented method, a multi-class system is decomposed into several single-class dynamic PERT networks, whereas each class is considered separately as a minisystem. In modeling of single-class dynamic PERT network, we use Markov process and a multi-objective model investigated by Azaron and Tavakkoli-Moghaddam in 2007. Then, after obtaining the resources allocated to service stations in every minisystem, the final resources allocated to activities are calculated by the proposed method.

Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

PubMed

Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

2017-10-01

Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

NASA Astrophysics Data System (ADS)

Chang, Faliang; Liu, Chunsheng

2017-09-01

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

Circular blurred shape model for multiclass symbol recognition.

PubMed

Escalera, Sergio; Fornés, Alicia; Pujol, Oriol; Lladós, Josep; Radeva, Petia

2011-04-01

In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.

Steganalysis feature improvement using expectation maximization

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M.; Peterson, Gilbert L.; Agaian, Sos S.

2007-04-01

Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are over 250 different tools which embed data into an image without causing noticeable changes to the image. From a forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool that can scan and accurately identify files suspected of containing malicious information. The identification process is termed the steganalysis problem which focuses on both blind identification, in which only normal images are available for training, and multi-class identification, in which both the clean and stego images at several embedding rates are available for training. In this paper an investigation of a clustering and classification technique (Expectation Maximization with mixture models) is used to determine if a digital image contains hidden information. The steganalysis problem is for both anomaly detection and multi-class detection. The various clusters represent clean images and stego images with between 1% and 10% embedding percentage. Based on the results it is concluded that the EM classification technique is highly suitable for both blind detection and the multi-class problem.

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.

A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Crimp-Imbalanced Protective (CRIMP) Fabrics

DTIC Science & Technology

2010-03-31

ILLUSTRATIONS ii LIST OF TABLES iv LIST OF ABBREVIATIONS, ACRONYMS, AND SYMBOLS iv INTRODUCTION 1 THREAT TYPES AND PROTECTION LEVELS 2 HISTORICAL...velocity 3-D Three-dimensional v (vi blank) CRIMP-IMBALANCED PROTECTIVE (CRIMP) FABRICS INTRODUCTION Lightweight, soft-armor systems have...1200 ft/sec D-4 (qi-uO /(B jaug UIBJJS 3i;sei3 (qi-uO 3* dSd ui uoipnpay > .’/ .7 • ’/ i •’/ !/ / *- IT> *- m y / II

Core filling and snaking instability of dark solitons in spin-imbalanced superfluid Fermi gases

NASA Astrophysics Data System (ADS)

Reichl, Matthew D.; Mueller, Erich J.

2017-05-01

We use the time-dependent Bogoliubov-de Gennes equations to study dark solitons in three-dimensional spin-imbalanced superfluid Fermi gases. We explore how the shape and dynamics of dark solitons are altered by the presence of excess unpaired spins which fill their low-density core. The unpaired particles broaden the solitons and suppress the transverse snake instability. We discuss ways of observing these phenomena in cold-atom experiments.

Imbalanced network biomarkers for traditional Chinese medicine Syndrome in gastritis patients.

PubMed

Li, Rui; Ma, Tao; Gu, Jin; Liang, Xujun; Li, Shao

2013-01-01

Cold Syndrome and Hot Syndrome are thousand-year-old key therapeutic concepts in traditional Chinese medicine (TCM), which depict the loss of body homeostasis. However, the scientific basis of TCM Syndrome remains unclear due to limitations of current reductionist approaches. Here, we established a network balance model to evaluate the imbalanced network underlying TCM Syndrome and find potential biomarkers. By implementing this approach and investigating a group of chronic superficial gastritis (CSG) and chronic atrophic gastritis (CAG) patients, we found that with leptin as a biomarker, Cold Syndrome patients experience low levels of energy metabolism, while the CCL2/MCP1 biomarker indicated that immune regulation is intensified in Hot Syndrome patients. Such a metabolism-immune imbalanced network is consistent during the course from CSG to CAG. This work provides a new way to understand TCM Syndrome scientifically, which in turn benefits the personalized medicine in terms of the ancient medicine and complex biological systems.

A systems biology-based investigation into the therapeutic effects of Gansui Banxia Tang on reversing the imbalanced network of hepatocellular carcinoma

NASA Astrophysics Data System (ADS)

Zhang, Yanqiong; Guo, Xiaodong; Wang, Danhua; Li, Ruisheng; Li, Xiaojuan; Xu, Ying; Liu, Zhenli; Song, Zhiqian; Lin, Ya; Li, Zhiyan; Lin, Na

2014-02-01

Several complex molecular events are involved in tumorigenesis of hepatocellular carcinoma (HCC). The interactions of these molecules may constitute the HCC imbalanced network. Gansui Banxia Tang (GSBXT), as a classic Chinese herbal formula, is a popular complementary and alternative medicine modality for treating HCC. In order to investigate the therapeutic effects and the pharmacological mechanisms of GSBXT on reversing HCC imbalanced network, we in the current study developed a comprehensive systems approach of integrating disease-specific and drug-specific networks, and successfully revealed the relationships of the ingredients in GSBXT with their putative targets, and with HCC significant molecules and HCC related pathway systems for the first time. Meanwhile, further experimental validation also demonstrated the preventive effects of GSBXT on tumor growth in mice and its regulatory effects on potential targets.

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification.

PubMed

Kang, Qi; Chen, XiaoShuang; Li, SiSi; Zhou, MengChu

2017-12-01

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

Enhanced risk management by an emerging multi-agent architecture

NASA Astrophysics Data System (ADS)

Lin, Sin-Jin; Hsu, Ming-Fu

2014-07-01

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

Camouflage target reconnaissance based on hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Hua, Wenshen; Guo, Tong; Liu, Xun

2015-08-01

Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

Seismic Data Analysis throught Multi-Class Classification.

NASA Astrophysics Data System (ADS)

Anderson, P.; Kappedal, R. D.; Magana-Zook, S. A.

2017-12-01

In this research, we conducted twenty experiments of varying time and frequency bands on 5000seismic signals with the intent of finding a method to classify signals as either an explosion or anearthquake in an automated fashion. We used a multi-class approach by clustering of the data throughvarious techniques. Dimensional reduction was examined through the use of wavelet transforms withthe use of the coiflet mother wavelet and various coefficients to explore possible computational time vsaccuracy dependencies. Three and four classes were generated from the clustering techniques andexamined with the three class approach producing the most accurate and realistic results.

Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.

PubMed

Wu, Ke; Edwards, Andrea; Fan, Wei; Gao, Jing; Zhang, Kun

2014-04-01

Data stream classification and imbalanced data learning are two important areas of data mining research. Each has been well studied to date with many interesting algorithms developed. However, only a few approaches reported in literature address the intersection of these two fields due to their complex interplay. In this work, we proposed an importance sampling driven, dynamic feature group weighting framework (DFGW-IS) for classifying data streams of imbalanced distribution. Two components are tightly incorporated into the proposed approach to address the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concepts are tackled by a weighted ensemble trained on a set of feature groups with each sub-classifier (i.e. a single classifier or an ensemble) weighed by its discriminative power and stable level. The un-even class distribution, on the other hand, is typically battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We derived the theoretical upper bound for the generalization error of the proposed algorithm. We also studied the empirical performance of our method on a set of benchmark synthetic and real world data, and significant improvement has been achieved over the competing algorithms in terms of standard evaluation metrics and parallel running time. Algorithm implementations and datasets are available upon request.

Development of "one-pot" method for multi-class compounds in porcine formula feed by multi-function impurity adsorption cleaning followed ultra-performance liquid chromatography-tandem mass spectrometry detection.

PubMed

Wang, Peilong; Wang, Xiao; Zhang, Wei; Su, Xiaoou

2014-02-01

A novel and efficient determination method for multi-class compounds including β-agonists, sedatives, nitro-imidazoles and aflatoxins in porcine formula feed based on a fast "one-pot" extraction/multifunction impurity adsorption (MFIA) clean-up procedure has been developed. 23 target analytes belonging to four different class compounds could be determined simultaneously in a single run. Conditions for "one-pot" extraction were studied in detail. Under the optimized conditions, the multi-class compounds in porcine formula feed samples were extracted and purified with methanol contained ammonia and absorbents by one step. The compounds in extracts were purified by using multi types of absorbent based on MFIA in one pot. The multi-walled carbon nanotubes were employed to improved clean-up efficiency. Shield BEH C18 column was used to separate 23 target analytes, followed by tandem mass spectrometry (MS/MS) detection using an electro-spray ionization source in positive mode. Recovery studies were done at three fortification levels. Overall average recoveries of target compounds in porcine formula feed at each levels were >51.6% based on matrix fortified calibration with coefficients of variation from 2.7% to 13.2% (n=6). The limit of determination (LOD) of these compounds in porcine formula feed sample matrix was <5.0 μg/kg. This method was successfully applied in screening and confirmation of target drugs in >30 porcine formula feed samples. It was demonstrated that the integration of the MFIA protocol with the MS/MS instrument could serve as a valuable strategy for rapid screening and reliable confirmatory analysis of multi-class compounds in real samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

NASA Astrophysics Data System (ADS)

Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

2014-05-01

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.

Understanding start-up problems in yeast glycolysis.

PubMed

Overal, Gosse B; Teusink, Bas; Bruggeman, Frank J; Hulshof, Josephus; Planqué, Robert

2018-05-01

Yeast glycolysis has been the focus of research for decades, yet a number of dynamical aspects of yeast glycolysis remain poorly understood at present. If nutrients are scarce, yeast will provide its catabolic and energetic needs with other pathways, but the enzymes catalysing upper glycolytic fluxes are still expressed. We conjecture that this overexpression facilitates the rapid transition to glycolysis in case of a sudden increase in nutrient concentration. However, if starved yeast is presented with abundant glucose, it can enter into an imbalanced state where glycolytic intermediates keep accumulating, leading to arrested growth and cell death. The bistability between regularly functioning and imbalanced phenotypes has been shown to depend on redox balance. We shed new light on these phenomena with a mathematical analysis of an ordinary differential equation model, including NADH to account for the redox balance. In order to gain qualitative insight, most of the analysis is parameter-free, i.e., without assigning a numerical value to any of the parameters. The model has a subtle bifurcation at the switch between an inviable equilibrium state and stable flux through glycolysis. This switch occurs if the ratio between the flux through upper glycolysis and ATP consumption rate of the cell exceeds a fixed threshold. If the enzymes of upper glycolysis would be barely expressed, our model predicts that there will be no glycolytic flux, even if external glucose would be at growth-permissable levels. The existence of the imbalanced state can be found for certain parameter conditions independent of the mentioned bifurcation. The parameter-free analysis proved too complex to directly gain insight into the imbalanced states, but the starting point of a branch of imbalanced states can be shown to exist in detail. Moreover, the analysis offers the key ingredients necessary for successful numerical continuation, which highlight the existence of this bistability and the influence of the redox balance. Copyright © 2018 Elsevier Inc. All rights reserved.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Lee, Kyoung-Joung

2018-05-24

In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45,096 events) and evaluated on a test dataset (11,274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. The proposed CNN model reaches a mean -score of 93.0 for the training dataset and 87.0 for the test dataset. Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH. © 2018 Institute of Physics and Engineering in Medicine.

Single classifier, OvO, OvA and RCC multiclass classification method in handheld based smartphone gait identification

NASA Astrophysics Data System (ADS)

Raziff, Abdul Rafiez Abdul; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Gait recognition is widely used in many applications. In the application of the gait identification especially in people, the number of classes (people) is many which may comprise to more than 20. Due to the large amount of classes, the usage of single classification mapping (direct classification) may not be suitable as most of the existing algorithms are mostly designed for the binary classification. Furthermore, having many classes in a dataset may result in the possibility of having a high degree of overlapped class boundary. This paper discusses the application of multiclass classifier mappings such as one-vs-all (OvA), one-vs-one (OvO) and random correction code (RCC) on handheld based smartphone gait signal for person identification. The results is then compared with a single J48 decision tree for benchmark. From the result, it can be said that using multiclass classification mapping method thus partially improved the overall accuracy especially on OvO and RCC with width factor more than 4. For OvA, the accuracy result is worse than a single J48 due to a high number of classes.

Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Adhitya, Yudhi

2018-03-01

The objective of this research is to determine the quality of cocoa beans through morphology of their digital images. Samples of cocoa beans were scattered on a bright white paper under a controlled lighting condition. A compact digital camera was used to capture the images. The images were then processed to extract their morphological parameters. Classification process begins with an analysis of cocoa beans image based on morphological feature extraction. Parameters for extraction of morphological or physical feature parameters, i.e., Area, Perimeter, Major Axis Length, Minor Axis Length, Aspect Ratio, Circularity, Roundness, Ferret Diameter. The cocoa beans are classified into 4 groups, i.e.: Normal Beans, Broken Beans, Fractured Beans, and Skin Damaged Beans. The model of classification used in this paper is the Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM), a proposed improvement model of SVM using ensemble method in which the separate hyperplanes are obtained by least square approach and the multiclass procedure uses One-Against- All method. The result of our proposed model showed that the classification with morphological feature input parameters were accurately as 99.705% for the four classes, respectively.

Mass Spectrometry Parameters Optimization for the 46 Multiclass Pesticides Determination in Strawberries with Gas Chromatography Ion-Trap Tandem Mass Spectrometry

NASA Astrophysics Data System (ADS)

Fernandes, Virgínia C.; Vera, Jose L.; Domingues, Valentina F.; Silva, Luís M. S.; Mateus, Nuno; Delerue-Matos, Cristina

2012-12-01

Multiclass analysis method was optimized in order to analyze pesticides traces by gas chromatography with ion-trap and tandem mass spectrometry (GC-MS/MS). The influence of some analytical parameters on pesticide signal response was explored. Five ion trap mass spectrometry (IT-MS) operating parameters, including isolation time (IT), excitation voltage (EV), excitation time (ET), maximum excitation energy or " q" value (q), and isolation mass window (IMW) were numerically tested in order to maximize the instrument analytical signal response. For this, multiple linear regression was used in data analysis to evaluate the influence of the five parameters on the analytical response in the ion trap mass spectrometer and to predict its response. The assessment of the five parameters based on the regression equations substantially increased the sensitivity of IT-MS/MS in the MS/MS mode. The results obtained show that for most of the pesticides, these parameters have a strong influence on both signal response and detection limit. Using the optimized method, a multiclass pesticide analysis was performed for 46 pesticides in a strawberry matrix. Levels higher than the limit established for strawberries by the European Union were found in some samples.

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Protein instability, haploinsufficiency, and cortical hyper-excitability underlie STXBP1 encephalopathy

PubMed Central

Kovačević, Jovana; Maroteaux, Gregoire; Schut, Desiree; Loos, Maarten; Dubey, Mohit; Pitsch, Julika; Remmelink, Esther; Koopmans, Bastijn; Crowley, James; Cornelisse, L Niels; Sullivan, Patrick F; Schoch, Susanne; Toonen, Ruud F; Stiedl, Oliver; Verhage, Matthijs

2018-01-01

Abstract De novo heterozygous mutations in STXBP1/Munc18-1 cause early infantile epileptic encephalopathies (EIEE4, OMIM #612164) characterized by infantile epilepsy, developmental delay, intellectual disability, and can include autistic features. We characterized the cellular deficits for an allelic series of seven STXBP1 mutations and developed four mouse models that recapitulate the abnormal EEG activity and cognitive aspects of human STXBP1-encephalopathy. Disease-causing STXBP1 variants supported synaptic transmission to a variable extent on a null background, but had no effect when overexpressed on a heterozygous background. All disease variants had severely decreased protein levels. Together, these cellular studies suggest that impaired protein stability and STXBP1 haploinsufficiency explain STXBP1-encephalopathy and that, therefore, Stxbp1+/− mice provide a valid mouse model. Simultaneous video and EEG recordings revealed that Stxbp1+/− mice with different genomic backgrounds recapitulate the seizure/spasm phenotype observed in humans, characterized by myoclonic jerks and spike-wave discharges that were suppressed by the antiepileptic drug levetiracetam. Mice heterozygous for Stxbp1 in GABAergic neurons only, showed impaired viability, 50% died within 2–3 weeks, and the rest showed stronger epileptic activity. c-Fos staining implicated neocortical areas, but not other brain regions, as the seizure foci. Stxbp1+/− mice showed impaired cognitive performance, hyperactivity and anxiety-like behaviour, without altered social behaviour. Taken together, these data demonstrate the construct, face and predictive validity of Stxbp1+/− mice and point to protein instability, haploinsufficiency and imbalanced excitation in neocortex, as the underlying mechanism of STXBP1-encephalopathy. The mouse models reported here are valid models for development of therapeutic interventions targeting STXBP1-encephalopathy. PMID:29538625

Natural products, an important resource for discovery of multitarget drugs and functional food for regulation of hepatic glucose metabolism.

PubMed

Li, Jian; Yu, Haiyang; Wang, Sijian; Wang, Wei; Chen, Qian; Ma, Yanmin; Zhang, Yi; Wang, Tao

2018-01-01

Imbalanced hepatic glucose homeostasis is one of the critical pathologic events in the development of metabolic syndromes (MSs). Therefore, regulation of imbalanced hepatic glucose homeostasis is important in drug development for MS treatment. In this review, we discuss the major targets that regulate hepatic glucose homeostasis in human physiologic and pathophysiologic processes, involving hepatic glucose uptake, glycolysis and glycogen synthesis, and summarize their changes in MSs. Recent literature suggests the necessity of multitarget drugs in the management of MS disorder for regulation of imbalanced glucose homeostasis in both experimental models and MS patients. Here, we highlight the potential bioactive compounds from natural products with medicinal or health care values, and focus on polypharmacologic and multitarget natural products with effects on various signaling pathways in hepatic glucose metabolism. This review shows the advantage and feasibility of discovering multicompound-multitarget drugs from natural products, and providing a new perspective of ways on drug and functional food development for MSs.

Structural imbalance promotes behavior analogous to aesthetic preference in domestic chicks.

PubMed

Elliott, Mark A; Salva, Orsola Rosa; Mulcahy, Paul; Regolin, Lucia

2012-01-01

Visual images may be judged 'aesthetic' when their positioning appears imbalanced. An apparent imbalance may signify an as yet incomplete action or event requiring more detailed processing. As such it may refer to phylogenetically ancient stimulus-response mechanisms such as those mediating attentional deployment. We studied preferences for structural balance or imbalance in week-old domestic chicks (Gallus gallus), using a conditioning procedure to reinforce pecking at either "aligned" (balanced) or "misaligned" (imbalanced) training stimuli. A testing phase with novel balanced and imbalanced stimuli established whether chicks would retain their conditioned behavior or revert to chance responding. Whereas those trained on aligned stimuli were equally likely to choose aligned or misaligned stimuli, chicks trained on misaligned stimuli maintained the trained preference. Our results are consistent with the idea that the coding of structural imbalance is primary and even overrides classical conditioning. Generalized to the humans, these results suggest aesthetic judgments based upon structural imbalance may be based on evolutionarily ancient mechanisms, which are shared by different vertebrate species.

Natural products, an important resource for discovery of multitarget drugs and functional food for regulation of hepatic glucose metabolism

PubMed Central

Wang, Sijian; Wang, Wei; Chen, Qian; Ma, Yanmin; Zhang, Yi; Wang, Tao

2018-01-01

Imbalanced hepatic glucose homeostasis is one of the critical pathologic events in the development of metabolic syndromes (MSs). Therefore, regulation of imbalanced hepatic glucose homeostasis is important in drug development for MS treatment. In this review, we discuss the major targets that regulate hepatic glucose homeostasis in human physiologic and pathophysiologic processes, involving hepatic glucose uptake, glycolysis and glycogen synthesis, and summarize their changes in MSs. Recent literature suggests the necessity of multitarget drugs in the management of MS disorder for regulation of imbalanced glucose homeostasis in both experimental models and MS patients. Here, we highlight the potential bioactive compounds from natural products with medicinal or health care values, and focus on polypharmacologic and multitarget natural products with effects on various signaling pathways in hepatic glucose metabolism. This review shows the advantage and feasibility of discovering multicompound–multitarget drugs from natural products, and providing a new perspective of ways on drug and functional food development for MSs. PMID:29391777

Imbalanced network biomarkers for traditional Chinese medicine Syndrome in gastritis patients

PubMed Central

Li, Rui; Ma, Tao; Gu, Jin; Liang, Xujun; Li, Shao

2013-01-01

Cold Syndrome and Hot Syndrome are thousand-year-old key therapeutic concepts in traditional Chinese medicine (TCM), which depict the loss of body homeostasis. However, the scientific basis of TCM Syndrome remains unclear due to limitations of current reductionist approaches. Here, we established a network balance model to evaluate the imbalanced network underlying TCM Syndrome and find potential biomarkers. By implementing this approach and investigating a group of chronic superficial gastritis (CSG) and chronic atrophic gastritis (CAG) patients, we found that with leptin as a biomarker, Cold Syndrome patients experience low levels of energy metabolism, while the CCL2/MCP1 biomarker indicated that immune regulation is intensified in Hot Syndrome patients. Such a metabolism-immune imbalanced network is consistent during the course from CSG to CAG. This work provides a new way to understand TCM Syndrome scientifically, which in turn benefits the personalized medicine in terms of the ancient medicine and complex biological systems. PMID:23529020

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

PubMed

Jiang, Jiewei; Liu, Xiyang; Zhang, Kai; Long, Erping; Wang, Liming; Li, Wangting; Liu, Lin; Wang, Shuai; Zhu, Mingmin; Cui, Jiangtao; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Wang, Jinghui; Lin, Haotian

2017-11-21

Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

Simple-random-sampling-based multiclass text classification algorithm.

PubMed

Liu, Wuying; Wang, Lin; Yi, Mianzhu

2014-01-01

Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications. The space-time overhead of the algorithms must be concerned about the era of big data. Through the investigation of the token frequency distribution in a Chinese web document collection, this paper reexamines the power law and proposes a simple-random-sampling-based MTC (SRSMTC) algorithm. Supported by a token level memory to store labeled documents, the SRSMTC algorithm uses a text retrieval approach to solve text classification problems. The experimental results on the TanCorp data set show that SRSMTC algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements.

StruLocPred: structure-based protein subcellular localisation prediction using multi-class support vector machine.

PubMed

Zhou, Wengang; Dickerson, Julie A

2012-01-01

Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme

PubMed Central

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

A Realistic Seizure Prediction Study Based on Multiclass SVM.

PubMed

Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

2017-05-01

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

PubMed

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

NASA Astrophysics Data System (ADS)

Mustapha, S.; Braytee, A.; Ye, L.

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Modeling the risk of water pollution by pesticides from imbalanced data.

PubMed

Trajanov, Aneta; Kuzmanovski, Vladimir; Real, Benoit; Perreau, Jonathan Marks; Džeroski, Sašo; Debeljak, Marko

2018-04-30

The pollution of ground and surface waters with pesticides is a serious ecological issue that requires adequate treatment. Most of the existing water pollution models are mechanistic mathematical models. While they have made a significant contribution to understanding the transfer processes, they face the problem of validation because of their complexity, the user subjectivity in their parameterization, and the lack of empirical data for validation. In addition, the data describing water pollution with pesticides are, in most cases, very imbalanced. This is due to strict regulations for pesticide applications, which lead to only a few pollution events. In this study, we propose the use of data mining to build models for assessing the risk of water pollution by pesticides in field-drained outflow water. Unlike the mechanistic models, the models generated by data mining are based on easily obtainable empirical data, while the parameterization of the models is not influenced by the subjectivity of ecological modelers. We used empirical data from field trials at the La Jaillière experimental site in France and applied the random forests algorithm to build predictive models that predict "risky" and "not-risky" pesticide application events. To address the problems of the imbalanced classes in the data, cost-sensitive learning and different measures of predictive performance were used. Despite the high imbalance between risky and not-risky application events, we managed to build predictive models that make reliable predictions. The proposed modeling approach can be easily applied to other ecological modeling problems where we encounter empirical data with highly imbalanced classes.

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

PubMed Central

Sacramento, João; Wichert, Andreas; van Rossum, Mark C. W.

2015-01-01

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L 1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. PMID:26046817

SVM classification of microaneurysms with imbalanced dataset based on borderline-SMOTE and data cleaning techniques

NASA Astrophysics Data System (ADS)

Wang, Qingjie; Xin, Jingmin; Wu, Jiayi; Zheng, Nanning

2017-03-01

Microaneurysms are the earliest clinic signs of diabetic retinopathy, and many algorithms were developed for the automatic classification of these specific pathology. However, the imbalanced class distribution of dataset usually causes the classification accuracy of true microaneurysms be low. Therefore, by combining the borderline synthetic minority over-sampling technique (BSMOTE) with the data cleaning techniques such as Tomek links and Wilson's edited nearest neighbor rule (ENN) to resample the imbalanced dataset, we propose two new support vector machine (SVM) classification algorithms for the microaneurysms. The proposed BSMOTE-Tomek and BSMOTE-ENN algorithms consist of: 1) the adaptive synthesis of the minority samples in the neighborhood of the borderline, and 2) the remove of redundant training samples for improving the efficiency of data utilization. Moreover, the modified SVM classifier with probabilistic outputs is used to divide the microaneurysm candidates into two groups: true microaneurysms and false microaneurysms. The experiments with a public microaneurysms database shows that the proposed algorithms have better classification performance including the receiver operating characteristic (ROC) curve and the free-response receiver operating characteristic (FROC) curve.

Neuroprotective Effects of Ferruginol, Jatrophone, and Junicedric Acid Against Amyloid-β Injury in Hippocampal Neurons.

PubMed

Zolezzi, Juan M; Lindsay, Carolina B; Serrano, Felipe G; Ureta, Roxana C; Theoduloz, Cristina; Schmeda-Hirschmann, Guillermo; Inestrosa, Nibaldo C

2018-01-01

Soluble amyloid-β (Aβ) oligomers have been recognized as early neurotoxic intermediates with a key role in the synaptic dysfunction observed in Alzheimer's disease (AD). Aβ oligomers block hippocampal long-term potentiation (LTP) and impair rodent spatial memory. Additionally, the presence of Aβ oligomers is associated with imbalanced intracellular calcium levels and apoptosis in neurons. In this context, we evaluated the effects of three diterpenes (ferruginol, jatrophone, and junicedric acid) that are found in medicinal plants and have several forms of biological activity. The intracellular calcium levels in hippocampal neurons increased in the presence of ferruginol, jatrophone, and junicedric acid, a result that was consistent with the observed increase in CA1 synaptic transmission in mouse hippocampal slices. Additionally, assays using Aβ peptide demonstrated that diterpenes, particularly ferruginol, restore LTP and reduce apoptosis. Recovery of the Aβ oligomer-induced loss of the synaptic proteins PSD-95, synapsin, VGlut, and NMDA receptor subunit 2A was observed in mouse hippocampal slices treated with junicedric acid. This cascade of events may be associated with the regulation of kinases, e.g., protein kinase C (PKC) and calcium/calmodulin-dependent protein kinase II (CaMKII), in addition to the activation of the canonical Wnt signaling pathway and could thus provide protection against Aβ oligomers, which trigger synaptic dysfunction. Our results suggest a potential neuroprotective role for diterpenes against the Aβ oligomers-induced neurodegenerative alterations, which make them interesting molecules to be further studied in the context of AD.

Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Sya'Rani Machrizzandi, M.

2018-03-01

Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person’s mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.; Anuta, P. E. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed.

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.

PubMed

Stanescu, Ana; Caragea, Doina

2015-01-01

Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework.

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets

PubMed Central

2015-01-01

Background Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Results Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. Conclusions In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework. PMID:26356316

Multi-class segmentation of neuronal electron microscopy images using deep learning

NASA Astrophysics Data System (ADS)

Khobragade, Nivedita; Agarwal, Chirag

2018-03-01

Study of connectivity of neural circuits is an essential step towards a better understanding of functioning of the nervous system. With the recent improvement in imaging techniques, high-resolution and high-volume images are being generated requiring automated segmentation techniques. We present a pixel-wise classification method based on Bayesian SegNet architecture. We carried out multi-class segmentation on serial section Transmission Electron Microscopy (ssTEM) images of Drosophila third instar larva ventral nerve cord, labeling the four classes of neuron membranes, neuron intracellular space, mitochondria and glia / extracellular space. Bayesian SegNet was trained using 256 ssTEM images of 256 x 256 pixels and tested on 64 different ssTEM images of the same size, from the same serial stack. Due to high class imbalance, we used a class-balanced version of Bayesian SegNet by re-weighting each class based on their relative frequency. We achieved an overall accuracy of 93% and a mean class accuracy of 88% for pixel-wise segmentation using this encoder-decoder approach. On evaluating the segmentation results using similarity metrics like SSIM and Dice Coefficient, we obtained scores of 0.994 and 0.886 respectively. Additionally, we used the network trained using the 256 ssTEM images of Drosophila third instar larva for multi-class labeling of ISBI 2012 challenge ssTEM dataset.

Classification of Automated Search Traffic

NASA Astrophysics Data System (ADS)

Buehrer, Greg; Stokes, Jack W.; Chellapilla, Kumar; Platt, John C.

As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a web site’s rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic (sometimes referred to as bot traffic) in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by automated means. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. Using the these detection features, we next classify the query stream using multiple binary classifiers. In addition, a multiclass classifier is then developed to identify subclasses of both normal and automated traffic. An active learning algorithm is used to suggest which user sessions to label to improve the accuracy of the multiclass classifier, while also seeking to discover new classes of automated traffic. Performance analysis are then provided. Finally, the multiclass classifier is used to predict the subclass distribution for the search query stream.

Effect of human cell malignancy on activity of DNA polymerase iota.

PubMed

Kazakov, A A; Grishina, E E; Tarantul, V Z; Gening, L V

2010-07-01

An increased level of mutagenesis, partially caused by imbalanced activities of error prone DNA polymerases, is a key symptom of cell malignancy. To clarify the possible role of incorrect DNA polymerase iota (Pol iota) function in increased frequency of mutations in mammalian cells, the activity of this enzyme in extracts of cells of different mouse organs and human eye (melanoma) and eyelid (basal-cell skin carcinoma) tumor cells was studied. Both Mg2+, considered as the main activator of the enzyme reaction of in vivo DNA replication, and Mn2+, that activates homogeneous Pol iota preparations in experiments in vitro more efficiently compared to all other bivalent cations, were used as cofactors of the DNA polymerase reaction in these experiments. In the presence of Mg2+, the enzyme was active only in cell extracts of mouse testicles and brain, whereas in the presence of Mn2+ the activity of Pol iota was found in all studied normal mouse organs. It was found that in cell extracts of both types of malignant tumors (basal-cell carcinoma and melanoma) Pol iota activity was observed in the presence of either Mn2+ or Mg2+. Manganese ions activated Pol iota in both cases, though to a different extent. In the presence of Mn2+ the Pol iota activity in the basal-cell carcinoma exceeded 2.5-fold that in control cells (benign tumors from the same eyelid region). In extracts of melanoma cells in the presence of either cation, the level of the enzyme activity was approximately equal to that in extracts of cells of surrounding tumor-free tissues as well as in eyes removed after traumas. The distinctive feature of tissue malignancy (in basal-cell carcinoma and in melanoma) was the change in DNA synthesis revealed as Mn2+-activated continuation of DNA synthesis after incorrect incorporation of dG opposite dT in the template by Pol iota. Among cell extracts of different normal mouse organs, only those of testicles exhibited a similar feature. This similarity can be explained by cell division blocking that occurs in all normal cells except in testicles and in malignant cells.

Probabilistic combination of static and dynamic gait features for verification

NASA Astrophysics Data System (ADS)

Bazin, Alex I.; Nixon, Mark S.

2005-03-01

This paper describes a novel probabilistic framework for biometric identification and data fusion. Based on intra and inter-class variation extracted from training data, posterior probabilities describing the similarity between two feature vectors may be directly calculated from the data using the logistic function and Bayes rule. Using a large publicly available database we show the two imbalanced gait modalities may be fused using this framework. All fusion methods tested provide an improvement over the best modality, with the weighted sum rule giving the best performance, hence showing that highly imbalanced classifiers may be fused in a probabilistic setting; improving not only the performance, but also generalized application capability.

Quantum criticality and universal scaling of strongly attractive spin-imbalanced Fermi gases in a one-dimensional harmonic trap

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

Yin Xiangguo; Chen Shu; Guan Xiwen

2011-07-15

We investigate quantum criticality and universal scaling of strongly attractive Fermi gases confined in a one-dimensional harmonic trap. We demonstrate from the power-law scaling of the thermodynamic properties that current experiments on this system are capable of measuring universal features at quantum criticality, such as universal scaling and Tomonaga-Luttinger liquid physics. The results also provide insights on recent measurements of key features of the phase diagram of a spin-imbalanced atomic Fermi gas [Y. Liao et al., Nature (London) 467, 567 (2010)] and point to further study of quantum critical phenomena in ultracold atomic Fermi gases.

An imbalanced parental genome ratio affects the development of rice zygotes.

PubMed

Toda, Erika; Ohnishi, Yukinosuke; Okamoto, Takashi

2018-04-27

Upon double fertilization, one sperm cell fuses with the egg cell to form a zygote with a 1:1 maternal-to-paternal genome ratio (1m:1p), and another sperm cell fuses with the central cell to form a triploid primary endosperm cell with a 2m:1p ratio, resulting in formation of the embryo and the endosperm, respectively. The endosperm is known to be considerably sensitive to the ratio of the parental genomes. However, the effect of an imbalance of the parental genomes on zygotic development and embryogenesis has not been well studied, because it is difficult to reproduce the parental genome-imbalanced situation in zygotes and to monitor the developmental profile of zygotes without external effects from the endosperm. In this study, we produced polyploid zygotes with an imbalanced parental genome ratio by electro-fusion of isolated rice gametes and observed their developmental profiles. Polyploid zygotes with an excess maternal gamete/genome developed normally, whereas approximately half to three-quarters of polyploid zygotes with a paternal excess showed developmental arrests. These results indicate that paternal and maternal genomes synergistically serve zygote development with distinct functions, and that genes with monoallelic expression play important roles during zygotic development and embryogenesis.

Oversampling the Minority Class in the Feature Space.

PubMed

Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

2016-09-01

The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

NASA Astrophysics Data System (ADS)

Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

2013-02-01

Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

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

Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr; Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr

2015-03-10

We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap schememore » is introduced to numerically approximate the model’s flow equations.« less

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

Popp, R.A.; Marsh, C.L.; Skow, L.C.

Hemoglobins of mouse embryos at 11.5 through 16.5 days of gestation were separated by electrophoresis on cellulose acetate and quantitated by a scanning densitometer to study the effects of two radiation-induced mutations on the expression of embryonic hemoglobin genes in mice. Normal mice produce three kinds of embryonic hemoglobins. In heterozygous ..cap alpha..-thalassemic embryos, expression of EI (x/sub 2/y/sub 2/) and EII (..cap alpha../sub 2/y/sub 2/) is deficient because the x- and ..cap alpha..-globin genes of one of the allelic pairs of Hba on chromosome 11 was deleted or otherwise inactivated by X irradiation. Simultaneous inactivation of the x- andmore » ..cap alpha..-globin genes indicates that these genes must be closely linked. Reduced x- and ..cap alpha..-chain synthesis results in an excess of y chains that associate as homotetramers. This unique y/sub 4/ hemoglobin also appears in ..beta..-duplication embryos where excess y chains are produced by the presence of three rather than two functional alleles of y- and ..beta..-globin genes. In double heterozygotes, which have a single functional allele of x- and ..cap alpha..-globin genes and three functional alleles of y- and ..beta..-globin genes, synthesis of ..cap alpha.. and non-..cap alpha.. chains is severely imbalanced and half of the total hemoglobin is y/sub 4/. Mouse y/sub 4/ has a high affinity for oxygen, P/sub 50/ of less than 10 mm Hg, but it lacks cooperativity so is inefficient for oxygen transport. The death of double heterozygotes in late fetal or neonatal life may be in large part to oxygen deprivation to the tissues.« less

Ingenious Snake: An Adaptive Multi-Class Contours Extraction

NASA Astrophysics Data System (ADS)

Li, Baolin; Zhou, Shoujun

2018-04-01

Active contour model (ACM) plays an important role in computer vision and medical image application. The traditional ACMs were used to extract single-class of object contours. While, simultaneous extraction of multi-class of interesting contours (i.e., various contours with closed- or open-ended) have not been solved so far. Therefore, a novel ACM model named “Ingenious Snake” is proposed to adaptively extract these interesting contours. In the first place, the ridge-points are extracted based on the local phase measurement of gradient vector flow field; the consequential ridgelines initialization are automated with high speed. Secondly, the contours’ deformation and evolvement are implemented with the ingenious snake. In the experiments, the result from initialization, deformation and evolvement are compared with the existing methods. The quantitative evaluation of the structure extraction is satisfying with respect of effectiveness and accuracy.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

NASA Astrophysics Data System (ADS)

Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics

PubMed Central

Best, Myron G.; Sol, Nik; Kooi, Irsan; Tannous, Jihane; Westerman, Bart A.; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Koster, Jan; Ylstra, Bauke; Ameziane, Najim; Dorsman, Josephine; Smit, Egbert F.; Verheul, Henk M.; Noske, David P.; Reijneveld, Jaap C.; Nilsson, R. Jonas A.; Tannous, Bakhos A.; Wesseling, Pieter; Wurdinger, Thomas

2015-01-01

Summary Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based “liquid biopsies”. PMID:26525104

Evaluation of Multiclass Model Observers in PET LROC Studies

NASA Astrophysics Data System (ADS)

Gifford, H. C.; Kinahan, P. E.; Lartizien, C.; King, M. A.

2007-02-01

A localization ROC (LROC) study was conducted to evaluate nonprewhitening matched-filter (NPW) and channelized NPW (CNPW) versions of a multiclass model observer as predictors of human tumor-detection performance with PET images. Target localization is explicitly performed by these model observers. Tumors were placed in the liver, lungs, and background soft tissue of a mathematical phantom, and the data simulation modeled a full-3D acquisition mode. Reconstructions were performed with the FORE+AWOSEM algorithm. The LROC study measured observer performance with 2D images consisting of either coronal, sagittal, or transverse views of the same set of cases. Versions of the CNPW observer based on two previously published difference-of-Gaussian channel models demonstrated good quantitative agreement with human observers. One interpretation of these results treats the CNPW observer as a channelized Hotelling observer with implicit internal noise

Topological phases in a Kitaev chain with imbalanced pairing

NASA Astrophysics Data System (ADS)

Li, C.; Zhang, X. Z.; Zhang, G.; Song, Z.

2018-03-01

We systematically study a Kitaev chain with imbalanced pair creation and annihilation, which is introduced by non-Hermitian pairing terms. An exact phase diagram shows that the topological phase is still robust under the influence of the conditional imbalance. The gapped phases are characterized by a topological invariant, the extended Zak phase, which is defined by the biorthonormal inner product. Such phases are destroyed at the points where the coalescence of ground states occurs, associated with the time-reversal symmetry breaking. We find that the Majorana edge modes also exist in an open chain in the time-reversal symmetry-unbroken region, demonstrating the bulk-edge correspondence in such a non-Hermitian system.

Multiclass method for the determination of 62 antibiotics in milk.

PubMed

Moretti, Simone; Cruciani, Gabriele; Romanelli, Sara; Rossi, Rosanna; Saluti, Giorgio; Galarini, Roberta

2016-09-01

A multiclass method for screening and confirmatory analysis of antimicrobial residues in milk has been developed and validated. Sixty-two antibiotics belonging to ten different drug families (amphenicols, cephalosporins, lincosamides, macrolides, penicillin, pleuromutilins, quinolones, rifamycins, sulfonamides and tetracyclines) have been included. After the addition of an aqueous solution of EDTA, the milk samples were extracted twice with acetonitrile, evaporated and dissolved in ammonium acetate. After centrifugation, 10 µl were analysed using LC-Q-Orbitrap operating in positive electrospray ionization mode. The method was validated in bovine milk in the range 2-150 µg kg(-1) for all antibiotics; for four compounds with maximum residue limits higher than 100 µg kg(-1) , the validation interval has been extended until 333 µg kg(-1) . The estimated performance characteristics were satisfactory complying with the requirements of Commission Decision 2002/657/EC. Good accuracies were obtained also taking advantage from the versatility of the hybrid mass analyser. Identification criteria were achieved verifying the mass accuracy and ion ratio of two ions, including the pseudomolecular one, where possible. Finally, the developed procedure was applied to 13 real cases of suspect milk samples (microbiological assay) confirming the presence of one or more antibiotics, although frequently, the maximum residue limits were not exceeded. The availability of rapid multiclass confirmatory methods can avoid wastes of suspect, but compliant, raw milk samples. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings.

PubMed

Mordang, Jan-Jurre; Gubern-Mérida, Albert; Bria, Alessandro; Tortorella, Francesco; den Heeten, Gerard; Karssemeijer, Nico

2017-04-01

Computer-aided detection (CADe) systems for mammography screening still mark many false positives. This can cause radiologists to lose confidence in CADe, especially when many false positives are obviously not suspicious to them. In this study, we focus on obvious false positives generated by microcalcification detection algorithms. We aim at reducing the number of obvious false-positive findings by adding an additional step in the detection method. In this step, a multiclass machine learning method is implemented in which dedicated classifiers learn to recognize the patterns of obvious false-positive subtypes that occur most frequently. The method is compared to a conventional two-class approach, where all false-positive subtypes are grouped together in one class, and to the baseline CADe system without the new false-positive removal step. The methods are evaluated on an independent dataset containing 1,542 screening examinations of which 80 examinations contain malignant microcalcifications. Analysis showed that the multiclass approach yielded a significantly higher sensitivity compared to the other two methods (P < 0.0002). At one obvious false positive per 100 images, the baseline CADe system detected 61% of the malignant examinations, while the systems with the two-class and multiclass false-positive reduction step detected 73% and 83%, respectively. Our study showed that by adding the proposed method to a CADe system, the number of obvious false positives can decrease significantly (P < 0.0002). © 2017 American Association of Physicists in Medicine.

Eco-friendly LC-MS/MS method for analysis of multi-class micropollutants in tap, fountain, and well water from northern Portugal.

PubMed

Barbosa, Marta O; Ribeiro, Ana R; Pereira, Manuel F R; Silva, Adrián M T

2016-11-01

Organic micropollutants present in drinking water (DW) may cause adverse effects for public health, and so reliable analytical methods are required to detect these pollutants at trace levels in DW. This work describes the first green analytical methodology for multi-class determination of 21 pollutants in DW: seven pesticides, an industrial compound, 12 pharmaceuticals, and a metabolite (some included in Directive 2013/39/EU or Decision 2015/495/EU). A solid-phase extraction procedure followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (offline SPE-UHPLC-MS/MS) method was optimized using eco-friendly solvents, achieving detection limits below 0.20 ng L -1 . The validated analytical method was successfully applied to DW samples from different sources (tap, fountain, and well waters) from different locations in the north of Portugal, as well as before and after bench-scale UV and ozonation experiments in spiked tap water samples. Thirteen compounds were detected, many of them not regulated yet, in the following order of frequency: diclofenac > norfluoxetine > atrazine > simazine > warfarin > metoprolol > alachlor > chlorfenvinphos > trimethoprim > clarithromycin ≈ carbamazepine ≈ PFOS > citalopram. Hazard quotients were also estimated for the quantified substances and suggested no adverse effects to humans. Graphical Abstract Occurrence and removal of multi-class micropollutants in drinking water, analyzed by an eco-friendly LC-MS/MS method.

Social Balance on Networks: The Dynamics of Friendship and Hatred

NASA Astrophysics Data System (ADS)

Redner, Sidney

2006-03-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad---a triangular loop with 1 or 3 unfriendly links---is reversed to make the triad balanced. Thus an imbalanced triad is analogous to a frustrated plaquette in a random magnet, while a balanced triad fulfills the adage: ``a friend of my friend is my friend; an enemy of my friend is my enemy; a friend of my enemy is my enemy; an enemy of my enemy is my friend.'' With this frustration-reducing dynamics, an infinite network undergoes a dynamic phase transition from a steady state to ``paradise''---all links are friendly---as the propensity for friendly links to be created in an update event passes through 1/2. On the other hand, a finite network always falls into a socially-balanced absorbing state where no imbalanced triads remain. A prominent example of the achievement of social balance is the evolution of pacts and treaties between various European countries during the late 1800's and early 1900's. Here social balance gave rise to the two major alliances that comprised the protagonists of World War I.

Proanthocyanidin-Rich Grape Seed Extract Modulates Intestinal Microbiota in Ovariectomized Mice.

PubMed

Jin, Guangwen; Asou, Yoshinori; Ishiyama, Kirika; Okawa, Atsushi; Kanno, Taro; Niwano, Yoshimi

2018-04-01

Grape-seed extract (GSE) is rich in proanthocyanidins (polymers of flavan-3-ols). GSE is well known to have various beneficial effects to health. The objective of this study was to examine the effect of dietary GSE on the intestinal microbiota in ovariectomized (OVX) mice as a model of menopause. Phylum-level analyses using 16S rRNA-targeted group-specific polymerase-chain reaction primers in fecal samples collected 8 weeks postoperatively from OVX mice revealed that the proportion of Firmicutes and Bacteroidetes populations became imbalanced as compared with that in sham-operated control mice. That is, the ratio of Firmicutes:Bacteroidetes populations in the OVX group were increased significantly. When OVX animals were given dietary GSE, the imbalanced proportion of Firmicutes and Bacteroidetes populations was normalized to that seen in control mice. In addition, the body weight of OVX animals measured at 6 weeks postoperatively was significantly higher than that in sham-operated control animals. Dietary GSE also prevented OVX animals from increasing body weight. Thus, we postulated that GSE can improve imbalanced populations of intestinal microbiota, leading to prevention of obesity under conditions of not only menopause but morbidity. The GSE has a great potential to be a functional food to improve dysbiosis in post-menopausal women. © 2018 Institute of Food Technologists®.

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method.

PubMed

Tharwat, Alaa; Moemen, Yasmine S; Hassanien, Aboul Ella

2016-12-09

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Kotsampasakou, Eleni; Ecker, Gerhard F.

2018-05-01

Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.

Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Kotsampasakou, Eleni; Ecker, Gerhard F.

2018-04-01

Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.

A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance

PubMed Central

Song, Ge

2014-01-01

Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications. According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment. In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM). The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs). An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM. In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks. Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift. Thus the distribution of resampled instances is similar to the current concept. The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment. Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models. PMID:24982961

Dynamic balance abilities of collegiate men for the bench press.

PubMed

Piper, Timothy J; Radlo, Steven J; Smith, Thomas J; Woodward, Ryan W

2012-12-01

This study investigated the dynamic balance detection ability of college men for the bench press exercise. Thirty-five college men (mean ± SD: age = 22.4 ± 2.76 years, bench press experience = 8.3 ± 2.79 years, and estimated 1RM = 120.1 ± 21.8 kg) completed 1 repetition of the bench press repetitions for each of 3 bar loading arrangements. In a randomized fashion, subjects performed the bench press with a 20-kg barbell loaded with one of the following: a balanced load, one 20-kg plate on each side; an imbalanced asymmetrical load, one 20-kg plate on one side and a 20-kg plate plus a 1.25-kg plate on the other side; or an imbalanced asymmetrical center of mass, 20-kg plate on one side and sixteen 1.25-kg plates on the other side. Subjects were blindfolded and wore ear protection throughout all testing to decrease the ability to otherwise detect loads. Binomial data analysis indicated that subjects correctly detected the imbalance of the imbalanced asymmetrical center of mass condition (p[correct detection] = 0.89, p < 0.01) but did not correctly detect the balanced condition (p[correct detection] = 0.46, p = 0.74) or the imbalanced asymmetrical condition (p[correct detection] = 0.60, p = 0.31). Although it appears that a substantial shift in the center of mass of plates leads to the detection of barbell imbalance, minor changes of the addition of 1.25 kg (2.5 lb) to the asymmetrical condition did not result in consistent detection. Our data indicate that the establishment of a biofeedback loop capable of determining balance detection was only realized under a high degree of imbalance. Although balance detection was not present in either the even or the slightly uneven loading condition, the inclusion of balance training for upper body may be futile if exercises are unable to establish such a feedback loop and thus eliciting an improvement of balance performance.

Antiretroviral Drugs Used in the Treatment of HIV Infection

MedlinePlus

... on April 12, 2018. Multi-class Combination Products Brand Generic Name Pediatric Use Approval Date Time to ... can be found at Drugs@FDA or DailyMed . Brand Name Generic Name Pediatric Use Approval Date Time ...

Network system effects of mileage fee.

DOT National Transportation Integrated Search

2015-08-01

This project presents a comprehensive investigation about the network effects of MF to facilitate the : developments of proper MF policies. After a practice scan and a review of the recent literature on MF, a multi-class mathematical programming with...

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

An ordinal classification approach for CTG categorization.

PubMed

Georgoulas, George; Karvelis, Petros; Gavrilis, Dimitris; Stylios, Chrysostomos D; Nikolakopoulos, George

2017-07-01

Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Activity recognition from minimal distinguishing subsequence mining

NASA Astrophysics Data System (ADS)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics.

PubMed

Best, Myron G; Sol, Nik; Kooi, Irsan; Tannous, Jihane; Westerman, Bart A; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Koster, Jan; Ylstra, Bauke; Ameziane, Najim; Dorsman, Josephine; Smit, Egbert F; Verheul, Henk M; Noske, David P; Reijneveld, Jaap C; Nilsson, R Jonas A; Tannous, Bakhos A; Wesseling, Pieter; Wurdinger, Thomas

2015-11-09

Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based "liquid biopsies". Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

PubMed Central

Pan, Rui; Wang, Hansheng; Li, Runze

2016-01-01

This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Mass-imbalanced Hubbard model in optical lattice with site-dependent interactions

NASA Astrophysics Data System (ADS)

Le, Duc-Anh; Tran, Thi-Thu-Trang; Hoang, Anh-Tuan; Nguyen, Toan-Thang; Tran, Minh-Tien

2018-03-01

We study the half-filled mass-imbalanced Hubbard model with spatially alternating interactions on an optical bipartite lattice by means of the dynamical mean-field theory. The Mott transition is investigated via the spin-dependent density of states and double occupancies. The phase diagrams for the homogeneous phases at zero temperature are constructed numerically. The boundary between metallic and insulating phases at zero temperature is analytically derived within the dynamical mean field theory using the equation of motion approach as the impurity solver. We found that the metallic region is reduced with increasing interaction anisotropy or mass imbalance. Our results are closely relevant to current researches in ultracold fermion experiments and can be verified through experimental observations.

Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

2015-01-01

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user.

PubMed

Simon, Nadine; Käthner, Ivo; Ruf, Carolin A; Pasqualotto, Emanuele; Kübler, Andrea; Halder, Sebastian

2014-01-01

Brain-computer interfaces (BCIs) can serve as muscle independent communication aids. Persons, who are unable to control their eye muscles (e.g., in the completely locked-in state) or have severe visual impairments for other reasons, need BCI systems that do not rely on the visual modality. For this reason, BCIs that employ auditory stimuli were suggested. In this study, a multiclass BCI spelling system was implemented that uses animal voices with directional cues to code rows and columns of a letter matrix. To reveal possible training effects with the system, 11 healthy participants performed spelling tasks on 2 consecutive days. In a second step, the system was tested by a participant with amyotrophic lateral sclerosis (ALS) in two sessions. In the first session, healthy participants spelled with an average accuracy of 76% (3.29 bits/min) that increased to 90% (4.23 bits/min) on the second day. Spelling accuracy by the participant with ALS was 20% in the first and 47% in the second session. The results indicate a strong training effect for both the healthy participants and the participant with ALS. While healthy participants reached high accuracies in the first session and second session, accuracies for the participant with ALS were not sufficient for satisfactory communication in both sessions. More training sessions might be needed to improve spelling accuracies. The study demonstrated the feasibility of the auditory BCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.

Simultaneous determination of 41 multiclass organic pollutants in environmental waters by means of polyethersulfone microextraction followed by liquid chromatography-tandem mass spectrometry.

PubMed

Mijangos, Leire; Ziarrusta, Haizea; Olivares, Maitane; Zuloaga, Olatz; Möder, Monika; Etxebarria, Nestor; Prieto, Ailette

2018-01-01

A new procedure using polyethersulfone (PES) microextraction followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was developed in this work for the simultaneous determination of 41 multiclass priority and emerging organic pollutants including herbicides, hormones, personal care products, and pharmaceuticals, among others, in seawater, wastewater treatment plant (WWTP) effluents, and estuary samples. The optimization of the analysis included two different chromatographic columns and different variables (polarity, fragmentor voltage, collision energy, and collision cell accelerator) of the mass spectrometer. In the case of PES extraction, ion strength of the water, pH, addition of EDTA, and the amount of the polymeric material were thoroughly investigated. The developed procedure was compared with a previously validated one based on a standard solid-phase extraction (SPE). In contrast to the SPE protocol, the PES method allowed a cost-efficient extraction of complex aqueous samples with lower matrix effect from 120 mL of water sample. Satisfactory and comparable apparent recovery values (80-119 and 70-131%) and method quantification limits (MQLs, 0.4-26 and 0.2-23 ng/L) were obtained for PES and SPE procedures, respectively, regardless of the matrix. Repeatability values lower than 27% were obtained. Finally, the developed methods were applied to the analysis of real samples from the Basque Country and irbesartan, valsartan, acesulfame, and sucralose were the analytes most often detected at the highest concentrations (51-1096 ng/L). Graphical abstract Forty-one multiclass pollutant determination in environmental waters by means of PES/SPE-LC-MS/MS.

Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm.

PubMed

Ren, Fulong; Cao, Peng; Li, Wei; Zhao, Dazhe; Zaiane, Osmar

2017-01-01

Diabetic retinopathy (DR) is a progressive disease, and its detection at an early stage is crucial for saving a patient's vision. An automated screening system for DR can help in reduce the chances of complete blindness due to DR along with lowering the work load on ophthalmologists. Among the earliest signs of DR are microaneurysms (MAs). However, current schemes for MA detection appear to report many false positives because detection algorithms have high sensitivity. Inevitably some non-MAs structures are labeled as MAs in the initial MAs identification step. This is a typical "class imbalance problem". Class imbalanced data has detrimental effects on the performance of conventional classifiers. In this work, we propose an ensemble based adaptive over-sampling algorithm for overcoming the class imbalance problem in the false positive reduction, and we use Boosting, Bagging, Random subspace as the ensemble framework to improve microaneurysm detection. The ensemble based over-sampling methods we proposed combine the strength of adaptive over-sampling and ensemble. The objective of the amalgamation of ensemble and adaptive over-sampling is to reduce the induction biases introduced from imbalanced data and to enhance the generalization classification performance of extreme learning machines (ELM). Experimental results show that our ASOBoost method has higher area under the ROC curve (AUC) and G-mean values than many existing class imbalance learning methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Shoulder strength imbalances as injury risk in handball.

PubMed

Edouard, P; Degache, F; Oullion, R; Plessis, J-Y; Gleizes-Cervera, S; Calmels, P

2013-07-01

This study was conducted to analyze whether internal (IR) and external (ER) rotator shoulder muscles weakness and/or imbalance collected through a preseason assessment could be predictors of subsequent shoulder injury during a season in handball players. In preseason, 16 female elite handball players (HPG) and 14 healthy female nonathletes (CG) underwent isokinetic IR and ER strength test with use of a Con-Trex® dynamometer in a seated position with 45° shoulder abduction in scapular plane, at 60, 120 and 240°/s in concentric and at 60°/s in eccentric, for both sides. An imbalanced muscular strength profile was determined using -statistically selected cut-offs from CG values. For HPG, all newly incurred shoulder injuries were reported during the season. There were significant differences between HPG and CG only for dominant eccentric IR strength, ER/IR ratio at 240°/s and for IRecc/ERcon ratio. In HPG, IR and ER strength was higher, and ER/IR ratios lower for dominant than for nondominant side. The relative risk was 2.57 (95%CI: 1.60-3.54; P<0.05) if handball players had an imbalanced muscular strength profile. In youth female handball players IR and ER muscle strength increases on the dominant side without ER/IR imbalances; and higher injury risk was associated with imbalanced muscular strength profile. © Georg Thieme Verlag KG Stuttgart · New York.

Post-boosting of classification boundary for imbalanced data using geometric mean.

PubMed

Du, Jie; Vong, Chi-Man; Pun, Chi-Man; Wong, Pak-Kin; Ip, Weng-Fai

2017-12-01

In this paper, a novel imbalance learning method for binary classes is proposed, named as Post-Boosting of classification boundary for Imbalanced data (PBI), which can significantly improve the performance of any trained neural networks (NN) classification boundary. The procedure of PBI simply consists of two steps: an (imbalanced) NN learning method is first applied to produce a classification boundary, which is then adjusted by PBI under the geometric mean (G-mean). For data imbalance, the geometric mean of the accuracies of both minority and majority classes is considered, that is statistically more suitable than the common metric accuracy. PBI also has the following advantages over traditional imbalance methods: (i) PBI can significantly improve the classification accuracy on minority class while improving or keeping that on majority class as well; (ii) PBI is suitable for large data even with high imbalance ratio (up to 0.001). For evaluation of (i), a new metric called Majority loss/Minority advance ratio (MMR) is proposed that evaluates the loss ratio of majority class to minority class. Experiments have been conducted for PBI and several imbalance learning methods over benchmark datasets of different sizes, different imbalance ratios, and different dimensionalities. By analyzing the experimental results, PBI is shown to outperform other imbalance learning methods on almost all datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

DYRK1A promotes dopaminergic neuron survival in the developing brain and in a mouse model of Parkinson's disease.

PubMed

Barallobre, M J; Perier, C; Bové, J; Laguna, A; Delabar, J M; Vila, M; Arbonés, M L

2014-06-12

In the brain, programmed cell death (PCD) serves to adjust the numbers of the different types of neurons during development, and its pathological reactivation in the adult leads to neurodegeneration. Dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A) is a pleiotropic kinase involved in neural proliferation and cell death, and its role during brain growth is evolutionarily conserved. Human DYRK1A lies in the Down syndrome critical region on chromosome 21, and heterozygous mutations in the gene cause microcephaly and neurological dysfunction. The mouse model for DYRK1A haploinsufficiency (the Dyrk1a(+/-) mouse) presents neuronal deficits in specific regions of the adult brain, including the substantia nigra (SN), although the mechanisms underlying these pathogenic effects remain unclear. Here we study the effect of DYRK1A copy number variation on dopaminergic cell homeostasis. We show that mesencephalic DA (mDA) neurons are generated in the embryo at normal rates in the Dyrk1a haploinsufficient model and in a model (the mBACtgDyrk1a mouse) that carries three copies of Dyrk1a. We also show that the number of mDA cells diminishes in postnatal Dyrk1a(+/-) mice and increases in mBACtgDyrk1a mice due to an abnormal activity of the mitochondrial caspase9 (Casp9)-dependent apoptotic pathway during the main wave of PCD that affects these neurons. In addition, we show that the cell death induced by 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP), a toxin that activates Casp9-dependent apoptosis in mDA neurons, is attenuated in adult mBACtgDyrk1a mice, leading to an increased survival of SN DA neurons 21 days after MPTP intoxication. Finally, we present data indicating that Dyrk1a phosphorylation of Casp9 at the Thr125 residue is the mechanism by which this kinase hinders both physiological and pathological PCD in mDA neurons. These data provide new insight into the mechanisms that control cell death in brain DA neurons and they show that deregulation of developmental apoptosis may contribute to the phenotype of patients with imbalanced DYRK1A gene dosage.

Altered Intrinsic Pyramidal Neuron Properties and Pathway-Specific Synaptic Dysfunction Underlie Aberrant Hippocampal Network Function in a Mouse Model of Tauopathy

PubMed Central

Booth, Clair A.; Witton, Jonathan; Nowacki, Jakub; Tsaneva-Atanasova, Krasimira; Jones, Matthew W.; Randall, Andrew D.

2016-01-01

The formation and deposition of tau protein aggregates is proposed to contribute to cognitive impairments in dementia by disrupting neuronal function in brain regions, including the hippocampus. We used a battery of in vivo and in vitro electrophysiological recordings in the rTg4510 transgenic mouse model, which overexpresses a mutant form of human tau protein, to investigate the effects of tau pathology on hippocampal neuronal function in area CA1 of 7- to 8-month-old mice, an age point at which rTg4510 animals exhibit advanced tau pathology and progressive neurodegeneration. In vitro recordings revealed shifted theta-frequency resonance properties of CA1 pyramidal neurons, deficits in synaptic transmission at Schaffer collateral synapses, and blunted plasticity and imbalanced inhibition at temporoammonic synapses. These changes were associated with aberrant CA1 network oscillations, pyramidal neuron bursting, and spatial information coding in vivo. Our findings relate tauopathy-associated changes in cellular neurophysiology to altered behavior-dependent network function. SIGNIFICANCE STATEMENT Dementia is characterized by the loss of learning and memory ability. The deposition of tau protein aggregates in the brain is a pathological hallmark of dementia; and the hippocampus, a brain structure known to be critical in processing learning and memory, is one of the first and most heavily affected regions. Our results show that, in area CA1 of hippocampus, a region involved in spatial learning and memory, tau pathology is associated with specific disturbances in synaptic, cellular, and network-level function, culminating in the aberrant encoding of spatial information and spatial memory impairment. These studies identify several novel ways in which hippocampal information processing may be disrupted in dementia, which may provide targets for future therapeutic intervention. PMID:26758828

Imbalanced Rab3D versus Rab27 increases cathepsin S secretion from lacrimal acini in a mouse model of Sjögren's Syndrome

PubMed Central

Meng, Zhen; Edman, Maria C.; Hsueh, Pang-Yu; Chen, Chiao-Yu; Klinngam, Wannita; Tolmachova, Tanya; Okamoto, Curtis T.

2016-01-01

The mechanism responsible for the altered spectrum of tear proteins secreted by lacrimal gland acinar cells (LGAC) in patients with Sjögren's Syndrome (SS) remains unknown. We have previously identified increased cathepsin S (CTSS) activity as a unique characteristic of tears of patients with SS. Here, we investigated the role of Rab3D, Rab27a, and Rab27b proteins in the enhanced release of CTSS from LGAC. Similar to patients with SS and to the male nonobese diabetic (NOD) mouse model of SS, CTSS activity was elevated in tears of mice lacking Rab3D. Findings of lower gene expression and altered localization of Rab3D in NOD LGAC reinforce a role for Rab3D in suppressing excess CTSS release under physiological conditions. However, CTSS activity was significantly reduced in tears of mice lacking Rab27 isoforms. The reliance of CTSS secretion on Rab27 activity was supported by in vitro findings that newly synthesized CTSS was detected in and secreted from Rab27-enriched secretory vesicles and that expression of dominant negative Rab27b reduced carbachol-stimulated secretion of CTSS in cultured LGAC. High-resolution 3D-structured illumination microscopy revealed microdomains of Rab3D and Rab27 isoforms on the same secretory vesicles but present in different proportions on different vesicles, suggesting that changes in their relative association with secretory vesicles may tailor the vesicle contents. We propose that a loss of Rab3D from secretory vesicles, leading to disproportionate Rab27-to-Rab3D activity, may contribute to the enhanced release of CTSS in tears of patients with SS. PMID:27076615

Altered Intrinsic Pyramidal Neuron Properties and Pathway-Specific Synaptic Dysfunction Underlie Aberrant Hippocampal Network Function in a Mouse Model of Tauopathy.

PubMed

Booth, Clair A; Witton, Jonathan; Nowacki, Jakub; Tsaneva-Atanasova, Krasimira; Jones, Matthew W; Randall, Andrew D; Brown, Jonathan T

2016-01-13

The formation and deposition of tau protein aggregates is proposed to contribute to cognitive impairments in dementia by disrupting neuronal function in brain regions, including the hippocampus. We used a battery of in vivo and in vitro electrophysiological recordings in the rTg4510 transgenic mouse model, which overexpresses a mutant form of human tau protein, to investigate the effects of tau pathology on hippocampal neuronal function in area CA1 of 7- to 8-month-old mice, an age point at which rTg4510 animals exhibit advanced tau pathology and progressive neurodegeneration. In vitro recordings revealed shifted theta-frequency resonance properties of CA1 pyramidal neurons, deficits in synaptic transmission at Schaffer collateral synapses, and blunted plasticity and imbalanced inhibition at temporoammonic synapses. These changes were associated with aberrant CA1 network oscillations, pyramidal neuron bursting, and spatial information coding in vivo. Our findings relate tauopathy-associated changes in cellular neurophysiology to altered behavior-dependent network function. Dementia is characterized by the loss of learning and memory ability. The deposition of tau protein aggregates in the brain is a pathological hallmark of dementia; and the hippocampus, a brain structure known to be critical in processing learning and memory, is one of the first and most heavily affected regions. Our results show that, in area CA1 of hippocampus, a region involved in spatial learning and memory, tau pathology is associated with specific disturbances in synaptic, cellular, and network-level function, culminating in the aberrant encoding of spatial information and spatial memory impairment. These studies identify several novel ways in which hippocampal information processing may be disrupted in dementia, which may provide targets for future therapeutic intervention. Copyright © 2016 Booth, Witton et al.

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification.

PubMed

Liu, Yanqiu; Lu, Huijuan; Yan, Ke; Xia, Haixia; An, Chunlin

2016-01-01

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam

NASA Astrophysics Data System (ADS)

Chen, Zhang; Peng, Zhenming; Peng, Lingbing; Liao, Dongyi; He, Xin

2011-11-01

With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree. The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the average detection time for each image less than 1 second.

Holographic entanglement entropy in imbalanced superconductors

NASA Astrophysics Data System (ADS)

Dutta, Arghya; Modak, Sujoy Kumar

2014-01-01

We study the behavior of holographic entanglement entropy (HEE) for imbalanced holographic superconductors. We employ a numerical approach to consider the robust case of fully back-reacted gravity system. The hairy black hole solution is found by using our numerical scheme. Then it is used to compute the HEE for the superconducting case. The cases we study show that in presence of a mismatch between two chemical potentials, below the critical temperature, superconducting phase has a lower HEE in comparison to the AdS-Reissner-Nordström black hole phase. Interestingly, the effects of chemical imbalance are different in the contexts of black hole and superconducting phases. For black hole, HEE increases with increasing imbalance parameter while it behaves oppositely for the superconducting phase. The implications of these results are discussed.

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

PubMed Central

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Comparison of two microextraction methods based on solidification of floating organic droplet for the determination of multiclass analytes in river water samples by liquid chromatography tandem mass spectrometry using Central Composite Design.

PubMed

Asati, Ankita; Satyanarayana, G N V; Patel, Devendra K

2017-09-01

Two low density organic solvents based liquid-liquid microextraction methods, namely Vortex assisted liquid-liquid microextraction based on solidification of floating organic droplet (VALLME-SFO) and Dispersive liquid-liquid microextraction based on solidification of floating organic droplet(DLLME-SFO) have been compared for the determination of multiclass analytes (pesticides, plasticizers, pharmaceuticals and personal care products) in river water samples by using liquid chromatography tandem mass spectrometry (LC-MS/MS). The effect of various experimental parameters on the efficiency of the two methods and their optimum values were studied with the aid of Central Composite Design (CCD) and Response Surface Methodology(RSM). Under optimal conditions, VALLME-SFO was validated in terms of limit of detection, limit of quantification, dynamic linearity range, determination of coefficient, enrichment factor and extraction recovery for which the respective values were (0.011-0.219ngmL -1 ), (0.035-0.723ngmL -1 ), (0.050-0.500ngmL -1 ), (R 2 =0.992-0.999), (40-56), (80-106%). However, when the DLLME-SFO method was validated under optimal conditions, the range of values of limit of detection, limit of quantification, dynamic linearity range, determination of coefficient, enrichment factor and extraction recovery were (0.025-0.377ngmL -1 ), (0.083-1.256ngmL -1 ), (0.100-1.000ngmL -1 ), (R 2 =0.990-0.999), (35-49), (69-98%) respectively. Interday and intraday precisions were calculated as percent relative standard deviation (%RSD) and the values were ≤15% for VALLME-SFO and DLLME-SFO methods. Both methods were successfully applied for determining multiclass analytes in river water samples. Copyright © 2017 Elsevier B.V. All rights reserved.

A workflow for multiclass determination of 256 pesticides in essential oils by liquid chromatography tandem mass spectrometry using evaporation and dilution approaches: Application to lavandin, lemon and cypress essential oils.

PubMed

Fillatre, Yoann; Rondeau, David; Daguin, Antoine; Communal, Pierre-Yves

2016-01-01

This paper describes the determination of 256 multiclass pesticides in cypress and lemon essential oils (EOs) by the way of liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI/MS/MS) analysis using the scheduled selected reaction monitoring mode (sSRM) available on a hybrid quadrupole linear ion trap (QLIT) mass spectrometer. The performance of a sample preparation of lemon and cypress EOs based on dilution or evaporation under nitrogen assisted by a controlled heating were assessed. The best limits of quantification (LOQs) were achieved with the evaporation under nitrogen method giving LOQs≤10µgL(-1) for 91% of the pesticides. In addition the very satisfactory results obtained for recovery, repeatability and linearity showed that for EOs of relatively low evaporation temperature, a sample preparation based on evaporation under nitrogen is well adapted and preferable to dilution. By compiling these results with those previously published by some of us on lavandin EO, we proposed a workflow dedicated to multiresidue determination of pesticides in various EOs by LC-ESI/sSRM. Among the steps involved in this workflow, the protocol related to mass spectrometry proposes an alternative confirmation method to the classical SRM ratio criteria based on a sSRM survey scan followed by an information-dependent acquisition using the sensitive enhanced product ion (EPI) scan to generate MS/MS spectra then compared to a reference. The submitted workflow was applied to the case of lemon EOs samples highlighting for the first time the simultaneous detection of 20 multiclass pesticides in one EO. Some pesticides showed very high concentration levels with amounts greatly exceeding the mgL(-1). Copyright © 2015 Elsevier B.V. All rights reserved.

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals.

PubMed

Braga, Rodolpho C; Alves, Vinicius M; Muratov, Eugene N; Strickland, Judy; Kleinstreuer, Nicole; Trospsha, Alexander; Andrade, Carolina Horta

2017-05-22

Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70-0.81 and 0.72-0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass prediction of murine skin sensitization; and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure-activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal ( http://labmol.com.br/predskin/ ), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

PubMed

Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

2015-01-01

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

24 CFR 330.35 - Investors.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false Investors. 330.35 Section 330.35... SECURITIES § 330.35 Investors. Association guaranteed multiclass securities may not be suitable investments for all investors. No investor should purchase securities of any class unless the investor understands...

24 CFR 330.20 - Eligible participants.

Code of Federal Regulations, 2010 CFR

2010-04-01

...'s policies regarding participation by minority and/or women-owned businesses and take appropriate... status as a minority and/or women-owned business. (iii) Trustees. A trustee is selected by the Sponsor... deliver, eligible collateral to a trust in exchange for a single Association guaranteed multiclass...

WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound

NASA Astrophysics Data System (ADS)

Hunt, Peter A.; Segall, Matthew D.; Tyzack, Jonathan D.

2018-02-01

In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug-drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism. We describe the generation of a multi-class random forest model to predict which, out of a list of the seven leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion and shows significant enrichment over randomised models.

Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble

PubMed Central

Liu, Hang; Chu, Renzhi; Tang, Zhenan

2015-01-01

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. PMID:25942640

Analytical studies on the instabilities of heterogeneous intelligent traffic flow

NASA Astrophysics Data System (ADS)

Ngoduy, D.

2013-10-01

It has been widely reported in literature that a small perturbation in traffic flow such as a sudden deceleration of a vehicle could lead to the formation of traffic jams without a clear bottleneck. These traffic jams are usually related to instabilities in traffic flow. The applications of intelligent traffic systems are a potential solution to reduce the amplitude or to eliminate the formation of such traffic instabilities. A lot of research has been conducted to theoretically study the effect of intelligent vehicles, for example adaptive cruise control vehicles, using either computer simulation or analytical method. However, most current analytical research has only applied to single class traffic flow. To this end, the main topic of this paper is to perform a linear stability analysis to find the stability threshold of heterogeneous traffic flow using microscopic models, particularly the effect of intelligent vehicles on heterogeneous (or multi-class) traffic flow instabilities. The analytical results will show how intelligent vehicle percentages affect the stability of multi-class traffic flow.

Highly informative multiclass profiling of lipids by ultra-high performance liquid chromatography - Low resolution (quadrupole) mass spectrometry by using electrospray ionization and atmospheric pressure chemical ionization interfaces.

PubMed

Beccaria, Marco; Inferrera, Veronica; Rigano, Francesca; Gorynski, Krzysztof; Purcaro, Giorgia; Pawliszyn, Janusz; Dugo, Paola; Mondello, Luigi

2017-08-04

A simple, fast, and versatile method, using an ultra-high performance liquid chromatography system coupled with a low resolution (single quadrupole) mass spectrometer was optimized to perform multiclass lipid profiling of human plasma. Particular attention was made to develop a method suitable for both electrospray ionization and atmospheric pressure chemical ionization interfaces (sequentially in positive- and negative-ion mode), without any modification of the chromatographic conditions (mobile phase, flow-rate, gradient, etc.). Emphasis was given to the extrapolation of the structural information based on the fragmentation pattern obtained using atmospheric pressure chemical ionization interface, under each different ionization condition, highlighting the complementary information obtained using the electrospray ionization interface, of support for related molecule ions identification. Furthermore, mass spectra of phosphatidylserine and phosphatidylinositol obtained using the atmospheric pressure chemical ionization interface are reported and discussed for the first time. Copyright © 2017 Elsevier B.V. All rights reserved.

A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Sainani, Varsha

The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

An improved dispersive solid-phase extraction clean-up method for the gas chromatography-negative chemical ionisation tandem mass spectrometric determination of multiclass pesticide residues in edible oils.

PubMed

Deme, Pragney; Azmeera, Tirupathi; Prabhavathi Devi, B L A; Jonnalagadda, Padmaja R; Prasad, R B N; Vijaya Sarathi, U V R

2014-01-01

An improved sample preparation using dispersive solid-phase extraction clean-up was proposed for the trace level determination of 35 multiclass pesticide residues (organochlorine, organophosphorus and synthetic pyrethroids) in edible oils. Quantification of the analytes was carried out by gas chromatography-mass spectrometry in negative chemical ionisation mode (GC-NCI-MS/MS). The limit of detection and limit of quantification of residues were in the range of 0.01-1ng/g and 0.05-2ng/g, respectively. The analytes showed recoveries between 62% and 110%, and the matrix effect was observed to be less than 25% for most of the pesticides. Crude edible oil samples showed endosulfan isomers, p,p'-DDD, α-cypermethrin, chlorpyrifos, and diazinon residues in the range of 0.56-2.14ng/g. However, no pesticide residues in the detection range of the method were observed in refined oils. Copyright © 2013 Elsevier Ltd. All rights reserved.

Spatiotemporal source tuning filter bank for multiclass EEG based brain computer interfaces.

PubMed

Acharya, Soumyadipta; Mollazadeh, Moshen; Murari, Kartikeya; Thakor, Nitish

2006-01-01

Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the event-related spectral perturbation (ERSP) were measured and used to train a linear support vector machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (large Laplacian and common average reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.

High-fat diet exacerbates cognitive rigidity and social deficiency in the BTBR mouse model of autism.

PubMed

Zilkha, N; Kuperman, Y; Kimchi, T

2017-03-14

The global increase in rates of obesity has been accompanied by a similar surge in the number of autism diagnoses. Accumulating epidemiological evidence suggest a possible link between overweight and the risk for autism spectrum disorders (ASD), as well as autism severity. In laboratory animals, several studies have shown a connection between various environmental factors, including diet-induced obesity, and the development of autism-related behaviors. However, the effect of high-fat or imbalanced diet on a pre-existing autism-like phenotype is unclear. In this study, we employed the BTBR inbred mouse strain, a well-established mouse model for autism, to assess the impact of inadequate fattening nutrition on the autism-related behavioral phenotype. Male mice were fed by high-fat diet (HFD) or control balanced diet (control) from weaning onward, and tested in a series of behavioral assays as adults. In addition, we measured the hypothalamic expression levels of several genes involved in oxytocin and dopamine signaling, in search of a possible neurobiological underlying mechanism. As an internal control, we also employed similar metabolic and behavioral measures on neurotypical C57 mice. Compared to control-fed mice, BTBR mice fed by HFD showed marked aggravation in autism-related behaviors, manifested in increased cognitive rigidity and diminished preference for social novelty. Moreover, the total autism composite (severity) score was higher in the HFD group, and positively correlated with higher body weight. Finally, we revealed negative correlations associating dopamine signaling factors in the hypothalamus, to autism-related severity and body weight. In contrast, we found no significant effects of HFD on autism-related behaviors of C57 mice, though the metabolic effects of the diet were similar for both strains. Our results indicate a direct causative link between diet-induced obesity and worsening of a pre-existing autism-related behavior and emphasize the need for adequate nutrition in ASD patients. These findings might also implicate the involvement of hypothalamic dopamine in mediating this effect. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Long-time behavior of the momentum distribution during the sudden expansion of a spin-imbalanced Fermi gas in one dimension.

PubMed

Bolech, C J; Heidrich-Meisner, F; Langer, S; McCulloch, I P; Orso, G; Rigol, M

2012-09-14

We study the sudden expansion of spin-imbalanced ultracold lattice fermions with attractive interactions in one dimension after turning off the longitudinal confining potential. We show that the momentum distribution functions of majority and minority fermions quickly approach stationary values due to a quantum distillation mechanism that results in a spatial separation of pairs and majority fermions. As a consequence, Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) correlations are lost during the expansion. Furthermore, we argue that the shape of the stationary momentum distribution functions can be understood by relating them to the integrals of motion in this integrable quantum system. We discuss our results in the context of proposals to observe FFLO correlations, related to recent experiments by Liao et al., Nature (London) 467, 567 (2010).

Luttinger theorem and imbalanced Fermi systems

NASA Astrophysics Data System (ADS)

Pieri, Pierbiagio; Strinati, Giancarlo Calvanese

2017-04-01

The proof of the Luttinger theorem, which was originally given for a normal Fermi liquid with equal spin populations formally described by the exact many-body theory at zero temperature, is here extended to an approximate theory given in terms of a "conserving" approximation also with spin imbalanced populations. The need for this extended proof, whose underlying assumptions are here spelled out in detail, stems from the recent interest in superfluid trapped Fermi atoms with attractive inter-particle interaction, for which the difference between two spin populations can be made large enough that superfluidity is destroyed and the system remains normal even at zero temperature. In this context, we will demonstrate the validity of the Luttinger theorem separately for the two spin populations for any "Φ-derivable" approximation, and illustrate it in particular for the self-consistent t-matrix approximation.

Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets.

PubMed

Li, Der-Chiang; Hu, Susan C; Lin, Liang-Sian; Yeh, Chun-Wu

2017-01-01

It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods.

KINETIC EVOLUTION OF CORONAL HOLE PROTONS BY IMBALANCED ION-CYCLOTRON WAVES: IMPLICATIONS FOR MEASUREMENTS BY SOLAR PROBE PLUS

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

Isenberg, Philip A.; Vasquez, Bernard J.

We extend the kinetic guiding-center model of collisionless coronal hole protons presented in Isenberg and Vasquez to consider driving by imbalanced spectra of obliquely propagating ion-cyclotron waves. These waves are assumed to be a small by-product of the imbalanced turbulent cascade to high perpendicular wavenumber, and their total intensity is taken to be 1% of the total fluctuation energy. We also extend the kinetic solutions for the proton distribution function in the resulting fast solar wind to heliocentric distances of 20 solar radii, which will be attainable by the Solar Probe Plus spacecraft. We consider three ratios of outward-propagating tomore » inward-propagating resonant intensities: 1, 4, and 9. The self-consistent bulk flow speed reaches fast solar wind values in all cases, and these speeds are basically independent of the intensity ratio. The steady-state proton distribution is highly organized into nested constant-density shells by the resonant wave-particle interaction. The radial evolution of this kinetic distribution as the coronal hole plasma flows outward is understood as a competition between the inward- and outward-directed large-scale forces, causing an effective circulation of particles through the (v{sub ∥}, v{sub ⊥}) phase space and a characteristic asymmetric shape to the distribution. These asymmetries are substantial and persist to the outer limit of the model computation, where they should be observable by the Solar Probe Plus instruments.« less

CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations

PubMed Central

van Houte, Bart PP; Binsl, Thomas W; Hettling, Hannes; Pirovano, Walter; Heringa, Jaap

2009-01-01

Background Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods. Results In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers. Conclusion We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at . PMID:19709427

Development of multi-class, multi-criteria bicycle traffic assignment models and solution algorithms

DOT National Transportation Integrated Search

2015-08-31

Cycling is gaining popularity both as a mode of travel in urban communities and as an alternative mode to private motorized vehicles due to its wide range of benefits (health, environmental, and economical). However, this change in modal share is not...

Functional analysis of HSPA1A and HSPA8 in parturition.

PubMed

Geng, Junnan; Li, Huanan; Huang, Cong; Chai, Jin; Zheng, Rong; Li, Fenge; Jiang, Siwen

2017-01-29

Many factors are involved in parturition, such as apoptosis, inflammatory mediators, and hormones. Previous studies indicated that HSP70 directly or indirectly regulates apoptosis, inflammatory immune response and hormone stimulus. To gain new insights into molecular mechanism underlying HSP70 for regulating parturition, we overexpressed and knocked down two representative members of HSP70 (HSPA1A and HSPA8) through transfection of their recombinant plasmid and si-RNA separately in WISH (human amniotic epithelial) cells. The expression changes of several pathways' marker genes were investigated by Western blotting and quantitative real-time PCR (qRT-PCR). Results showed extreme expression changes in the genes of IL-8 and ESR2. HSP70 was found to stimulate estrogen response by regulating ESR2 through ERK1/2 after treating WISH cells with the special phosphorylation inhibitor of ERK1/2 and analyzing the changes of E2 concentration by ELISA. HSP70 was also observed to contribute to preterm birth after administering the special inhibitor of HSP70-PFT-μ with LPS-induced preterm birth mouse model. Overall, HSP70 induces parturition through stimulating immune inflammatory and estrogen response. The balanced HSP70 expression could ensure a smooth parturition, while the imbalanced expression may cause a pathological state like preterm. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of a Hidden Bayes Naive Multiclass Classifier in Network Intrusion Detection

ERIC Educational Resources Information Center

Koc, Levent

2013-01-01

With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify…

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

DTIC Science & Technology

2008-09-01

B.H. (2000). STEGANOGRAPHY: Hidden Images, A New Challenge in the Fight Against Child Porn . UPDATE, Volume 13, Number 2, pp. 1-4, Retrieved June 3...Other crimes involving the use of steganography include child pornography where the stego files are used to hide a predator’s location when posting

24 CFR 330.25 - Fees.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false Fees. 330.25 Section 330.25 Housing... SECURITIES § 330.25 Fees. The Association, in its discretion, through publication in the Multiclass Guide or on the GNMA electronic bulletin board, may impose fees for application, guaranty, transfer, change...

Participant Analysis of a Multi-Class, Multi-State, On-Line, Discussion List.

ERIC Educational Resources Information Center

Knupfer, Nancy Nelson; And Others

As interest in distance education increases, many university instructors are experimenting with listserv discussion within their courses. Six educational technology professors at four universities initiated a listserv discussion group within their various classes for 52 graduate students. Discussion topics were four general themes within…

Perfection and the Bomb: Nuclear Weapons, Teleology, and Motives.

ERIC Educational Resources Information Center

Brummett, Barry

1989-01-01

Uses Kenneth Burke's theory of perfection to explore the vocabularies of nuclear weapons in United States public discourse and how "the Bomb" as a God term has gained imbalanced ascendancy in centers of power. (MS)

Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

NASA Astrophysics Data System (ADS)

Purnami, S. W.; Khasanah, P. M.; Sumartini, S. H.; Chosuvivatwong, V.; Sriplung, H.

2016-04-01

According to the WHO, every two minutes there is one patient who died from cervical cancer. The high mortality rate is due to the lack of awareness of women for early detection. There are several factors that supposedly influence the survival of cervical cancer patients, including age, anemia status, stage, type of treatment, complications and secondary disease. This study wants to classify/predict cervical cancer survival based on those factors. Various classifications methods: classification and regression tree (CART), smooth support vector machine (SSVM), three order spline SSVM (TSSVM) were used. Since the data of cervical cancer are imbalanced, synthetic minority oversampling technique (SMOTE) is used for handling imbalanced dataset. Performances of these methods are evaluated using accuracy, sensitivity and specificity. Results of this study show that balancing data using SMOTE as preprocessing can improve performance of classification. The SMOTE-SSVM method provided better result than SMOTE-TSSVM and SMOTE-CART.

Evidence of Self-correction of Child Sex Ratios in India: A District-Level Analysis of Child Sex Ratios From 1981 to 2011.

PubMed

Diamond-Smith, Nadia; Bishai, David

2015-04-01

Sex ratios in India have become increasingly imbalanced over the past decades. We hypothesize that when sex ratios become very uneven, the shortage of girls will increase girls' future value, leading sex ratios to self-correct. Using data on children under 5 from the last four Indian censuses, we examine the relationship between the sex ratio at one point in time and the change in sex ratio over the next 10 years by district. Fixed-effects models show that when accounting for unobserved district-level characteristics--including total fertility rate, infant mortality rate, percentage literate, percentage rural, percentage scheduled caste, percentage scheduled tribe, and a time trend variable--sex ratios are significantly negatively correlated with the change in sex ratio in the successive 10-year period. This suggests that self-corrective forces are at work on imbalanced sex ratios in India.

Propeller torque load and propeller shaft torque response correlation during ice-propeller interaction

NASA Astrophysics Data System (ADS)

Polić, Dražen; Ehlers, Sören; Æsøy, Vilmar

2017-03-01

Ships use propulsion machinery systems to create directional thrust. Sailing in ice-covered waters involves the breaking of ice pieces and their submergence as the ship hull advances. Sometimes, submerged ice pieces interact with the propeller and cause irregular fluctuations of the torque load. As a result, the propeller and engine dynamics become imbalanced, and energy propagates through the propulsion machinery system until equilibrium is reached. In such imbalanced situations, the measured propeller shaft torque response is not equal to the propeller torque. Therefore, in this work, the overall system response is simulated under the ice-related torque load using the Bond graph model. The energy difference between the propeller and propeller shaft is estimated and related to their corresponding mechanical energy. Additionally, the mechanical energy is distributed among modes. Based on the distribution, kinetic and potential energy are important for the correlation between propeller torque and propeller shaft response.

Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

NASA Astrophysics Data System (ADS)

Liu, J.; Lan, T.; Qin, H.

2017-10-01

Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.

Collective modes of an imbalanced unitary Fermi gas

NASA Astrophysics Data System (ADS)

Hofmann, Johannes; Chevy, Frédéric; Goulko, Olga; Lobo, Carlos

2018-03-01

We study theoretically the collective mode spectrum of a strongly imbalanced two-component unitary Fermi gas in a cigar-shaped trap, where the minority species forms a gas of polarons. We describe the collective breathing mode of the gas in terms of the Fermi-liquid kinetic equation taking collisions into account using the method of moments. Our results for the frequency and damping of the longitudinal in-phase breathing mode are in good quantitative agreement with an experiment by Nascimbène et al. [Phys. Rev. Lett. 103, 170402 (2009), 10.1103/PhysRevLett.103.170402] and interpolate between a hydrodynamic and a collisionless regime as the polarization is increased. A separate out-of phase breathing mode, which for a collisionless gas is sensitive to the effective mass of the polaron, however, is strongly damped at finite temperature, whereas the experiment observes a well-defined oscillation.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

PubMed

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

PubMed Central

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. PMID:26764911

Effects of Imbalanced Muscle Loading on Hip Joint Development and Maturation

PubMed Central

Ford, Caleb A.; Nowlan, Niamh C.; Thomopoulos, Stavros; Killian, Megan L.

2017-01-01

The mechanical loading environment influences the development and maturation of joints. In this study, the influence of imbalanced muscular loading on joint development was studied using localized chemical denervation of hip stabilizing muscle groups in neonatal mice. It was hypothesized that imbalanced muscle loading, targeting either gluteal muscles or quadriceps muscles, would lead to bilateral hip joint asymmetry, as measured by acetabular coverage, femoral head volume and bone morphometry, and femoral-acetabular shape. The contralateral hip joints as well as age-matched, uninjected mice were used as controls. Altered bone development was analyzed using micro-computed tomography, histology, and image registration techniques at postnatal days (P) 28, 56, and 120. This study found that unilateral muscle unloading led to reduced acetabular coverage of the femoral head, lower total volume, lower bone volume ratio, and lower mineral density, at all three time points. Histologically, the femoral head was smaller in unloaded hips, with thinner triradiate cartilage at P28 and thinner cortical bone at P120 compared to contralateral hips. Morphological shape changes were evident in unloaded hips at P56. Unloaded hips had lower trabecular thickness and increased trabecular spacing of the femoral head compared to contralateral hips. The present study suggests that decreased muscle loading of the hip leads to altered bone and joint shape and growth during postnatal maturation. Statement of Clinical Significance: Adaptations from altered muscle loading during postnatal growth investigated in this study have implications on developmental hip disorders that result from asymmetric loading, such as patients with limb-length inequality or dysplasia. PMID:27391299

Evolutionary transition in symbiotic syndromes enabled diversification of phytophagous insects on an imbalanced diet

PubMed Central

Sudakaran, Sailendharan; Retz, Franziska; Kikuchi, Yoshitomo; Kost, Christian; Kaltenpoth, Martin

2015-01-01

Evolutionary adaptations for the exploitation of nutritionally challenging or toxic host plants represent a major force driving the diversification of phytophagous insects. Although symbiotic bacteria are known to have essential nutritional roles for insects, examples of radiations into novel ecological niches following the acquisition of specific symbionts remain scarce. Here we characterized the microbiota across bugs of the family Pyrrhocoridae and investigated whether the acquisition of vitamin-supplementing symbionts enabled the hosts to diversify into the nutritionally imbalanced and chemically well-defended seeds of Malvales plants as a food source. Our results indicate that vitamin-provisioning Actinobacteria (Coriobacterium and Gordonibacter), as well as Firmicutes (Clostridium) and Proteobacteria (Klebsiella) are widespread across Pyrrhocoridae, but absent from the sister family Largidae and other outgroup taxa. Despite the consistent association with a specific microbiota, the Pyrrhocoridae phylogeny is neither congruent with a dendrogram based on the hosts' microbial community profiles nor phylogenies of individual symbiont strains, indicating frequent horizontal exchange of symbiotic partners. Phylogenetic dating analyses based on the fossil record reveal an origin of the Pyrrhocoridae core microbiota in the late Cretaceous (81.2–86.5 million years ago), following the transition from crypt-associated beta-proteobacterial symbionts to an anaerobic community localized in the M3 region of the midgut. The change in symbiotic syndromes (that is, symbiont identity and localization) and the acquisition of the pyrrhocorid core microbiota followed the evolution of their preferred host plants (Malvales), suggesting that the symbionts facilitated their hosts' adaptation to this imbalanced nutritional resource and enabled the subsequent diversification in a competition-poor ecological niche. PMID:26023876

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY

PubMed Central

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. PMID:24176869

Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.

PubMed

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M; Ye, Jieping

2014-02-15

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. © 2013 Elsevier Inc. All rights reserved.

75 FR 10853 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-09

...-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing and Immediate Effectiveness of Proposed Rule Change Related to Multi-Class Broad Based Index Option Spread Orders March 2... thereunder,\\2\\ notice is hereby given that on February 18, 2010, the Chicago Board Options Exchange...

Embedded feature ranking for ensemble MLP classifiers.

PubMed

Windeatt, Terry; Duangsoithong, Rakkrit; Smith, Raymond

2011-06-01

A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

A Different Mirror: The Position of Immigrant Writers in British Society.

ERIC Educational Resources Information Center

Williams, Bronwyn T.

In teaching international students in Britain and students in the United States in multicultural and multiclass classrooms, a common resistance was found to the consideration of how culture and society shape identity. Even international students from collectivist cultures, who see their identities as inextricable from their communities in their…

Global Citizens Are Made, Not Born: Multiclass Role-Playing Simulation of Global Decision Making

ERIC Educational Resources Information Center

Levintova, Ekaterina; Johnson, Terri; Scheberle, Denise; Vonck, Kevin

2011-01-01

Globalization, global citizenship, and political engagement have become such buzzwords and cliches that we often lose the sense of their meaning. Global citizenship in particular is an elusive concept to operationalize. This article proposes to look at three dimensions of global citizenship: legal (rights and obligations), psychological…

78 FR 23281 - Notice of Proposed Information Collection; Comment Request: Ginnie Mae Multiclass Securities...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-18

... Statements Accountant for 15 8 120 32 3840 (attached to closing Sponsor. letter). Accountants' Closing Accountant...... 15 8 120 8 960 Letter. Accountants' OCS Letter... Accountant...... 15 8 120 8 960 Structuring Data Accountant...... 15 8 120 8 960 Financial Statements...... Accountant...... 15 8 120 1 120...

Optimization of sample preparation by central composite design for multi-class determination of veterinary drugs in bovine muscle, kidney and liver by ultra-high-performance liquid chromatographic-tandem mass spectrometry.

PubMed

Rizzetti, Tiele M; de Souza, Maiara P; Prestes, Osmar D; Adaime, Martha B; Zanella, Renato

2018-04-25

In this study a simple and fast multi-class method for the determination of veterinary drugs in bovine liver, kidney and muscle was developed. The method employed acetonitrile for extraction followed by clean-up with EMR-Lipid® sorbent and trichloracetic acid. Tests indicated that the use of TCA was most effective when added in the final step of the clean-up procedure instead of during extraction. Different sorbents were tested and optimized using central composite design and the analytes determined by ultra-high-performance liquid chromatographic-tandem mass spectrometry (UHPLC-MS/MS). The method was validated according the European Commission Decision 2002/657 presenting satisfactory results for 69 veterinary drugs in bovine liver and 68 compounds in bovine muscle and kidney. The method was applied in real samples and in proficiency tests and proved to be adequate for routine analysis. Residues of abamectin, doramectin, eprinomectin and ivermectin were found in samples of bovine muscle and only ivermectin in bovine liver. Copyright © 2017 Elsevier Ltd. All rights reserved.

New materials for solid-phase extraction and multiclass high-performance liquid chromatographic analysis of pesticides in grapes.

PubMed

Melo, Lucio F C; Collins, Carol H; Jardim, Isabel C S F

2004-04-02

Sample preparation procedures which included the use of new aminopropyl (NH2) and octadecyl (C18) solid-phase extraction (SPE) sorbents are proposed for the simultaneous multiclass determination of the fungicide benomyl and of the herbicides tebuthiuron, diuron, simazine, atrazine, and ametryn in grapes, using single wavelength high-performance liquid chromatography. Sorbent preparation uses a fast, easy, and effective procedure to obtain silica-based materials, made by depositing polysiloxanes on a silica support followed by thermal immobilization. Recovery results of the compounds, after elution from the SPE cartridges, indicate that the most efficient system employed silica loaded with 40% of an aminofunctional polydimethylsiloxane as sorbent, using dichloromethane:methanol (95:5, v/v) as eluent. Method validation, carried out in agreement with International Conference on Harmonization directives, was performed at three fortification levels (100, 200, and 1000 microg kg(-1)). Limits of detection and quantification show that the method developed can be used to detect the pesticides at concentrations below the maximum residue levels established by Codex Alimentarius, the US Environmental Protection Agency, the European Union, and Brazilian legislation.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

PubMed

Duong, Bach Phi; Kim, Jong-Myon

2018-04-07

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

Automatic threshold selection for multi-class open set recognition

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2017-05-01

Multi-class open set recognition is the problem of supervised classification with additional unknown classes encountered after a model has been trained. An open set classifer often has two core components. The first component is a base classifier which estimates the most likely class of a given example. The second component consists of open set logic which estimates if the example is truly a member of the candidate class. Such a system is operated in a feed-forward fashion. That is, a candidate label is first estimated by the base classifier, and the true membership of the example to the candidate class is estimated afterward. Previous works have developed an iterative threshold selection algorithm for rejecting examples from classes which were not present at training time. In those studies, a Platt-calibrated SVM was used as the base classifier, and the thresholds were applied to class posterior probabilities for rejection. In this work, we investigate the effectiveness of other base classifiers when paired with the threshold selection algorithm and compare their performance with the original SVM solution.

Direct immersion single drop micro-extraction method for multi-class pesticides analysis in mango using GC-MS.

PubMed

Pano-Farias, Norma S; Ceballos-Magaña, Silvia G; Muñiz-Valencia, Roberto; Jurado, Jose M; Alcázar, Ángela; Aguayo-Villarreal, Ismael A

2017-12-15

Due the negative effects of pesticides on environment and human health, more efficient and environmentally friendly methods are needed. In this sense, a simple, fast, free from memory effects and economical direct-immersion single drop micro-extraction (SDME) method and GC-MS for multi-class pesticides determination in mango samples was developed. Sample pre-treatment using ultrasound-assisted solvent extraction and factors affecting the SDME procedure (extractant solvent, drop volume, stirring rate, ionic strength, time, pH and temperature) were optimized using factorial experimental design. This method presented high sensitive (LOD: 0.14-169.20μgkg -1 ), acceptable precision (RSD: 0.7-19.1%), satisfactory recovery (69-119%) and high enrichment factors (20-722). Several obtained LOQs are below the MRLs established by the European Commission; therefore, the method could be applied for pesticides determination in routing analysis and custom laboratories. Moreover, this method has shown to be suitable for determination of some of the studied pesticides in lime, melon, papaya, banana, tomato, and lettuce. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simultaneous determination of multi-residue and multi-class antibiotics in aquaculture shrimps by UPLC-MS/MS.

PubMed

Saxena, Sushil Kumar; Rangasamy, Rajesh; Krishnan, Anoop A; Singh, Dhirendra P; Uke, Sumedh P; Malekadi, Praveen Kumar; Sengar, Anoop S; Mohamed, D Peer; Gupta, Ananda

2018-09-15

An accurate, reliable and fast multi-residue, multi-class method using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed and validated for simultaneous determination and quantification of 24 pharmacologically active substances of three different classes (Quinolones including fluoroquinolones, sulphonamides and tetracyclines) in aquaculture shrimps. Sample preparation involves extraction with acetonitrile containing 0.1% formic acid and followed by clean up with n-hexane and 0.1% methanol in water by UPLC-MS/MS within 8 min. The method was validated according to European Commission Decision 2002/657. Acceptable values were obtained for linearity (5-200 μg kg -1 ), specificity, Limit of Quantification (5-10 μg kg -1 ), recovery (between 83 and 100%), repeatability (RSD < 9%), within lab reproducibility (RSD < 15%), reproducibility (RSD ≤ 22%), decision limit (105-116 μg kg -1 ) and detection capability (110-132 μg kg -1 ). The validated method was applied to aquaculture shrimp samples from India. Copyright © 2018 Elsevier Ltd. All rights reserved.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

Multiclass determination and confirmation of antibiotic residues in honey using LC-MS/MS.

PubMed

Lopez, Mayda I; Pettis, Jeffery S; Smith, I Barton; Chu, Pak-Sin

2008-03-12

A multiclass method has been developed for the determination and confirmation in honey of tetracyclines (chlortetracycline, doxycycline, oxytetracycline, and tetracycline), fluoroquinolones (ciprofloxacin, danofloxacin, difloxacin, enrofloxacin, and sarafloxacin), macrolides (tylosin), lincosamides (lincomycin), aminoglycosides (streptomycin), sulfonamides (sulfathiazole), phenicols (chloramphenicol), and fumagillin residues using liquid chromatography tandem mass spectrometry (LC-MS/MS). Erythromycin (a macrolide) and monensin (an ionophore) can be detected and confirmed but not quantitated. Honey samples (approximately 2 g) are dissolved in 10 mL of water and centrifuged. An aliquot of the supernatant is used to determine streptomycin. The remaining supernatant is filtered through a fine-mesh nylon fabric and cleaned up by solid phase extraction. After solvent evaporation and sample reconstitution, 15 antibiotics are assayed by LC-MS/MS using electrospray ionization (ESI) in positive ion mode. Afterward, chloramphenicol is assayed using ESI in negative ion mode. The method has been validated at the low part per billion levels for most of the drugs with accuracies between 65 and 104% and coefficients of variation less than 17%. The evaluation of matrix effects caused by honey of different floral origin is presented.

Development of an Analytical Procedure for the Determination of Multiclass Compounds for Forensic Veterinary Toxicology.

PubMed

Sell, Bartosz; Sniegocki, Tomasz; Zmudzki, Jan; Posyniak, Andrzej

2018-04-01

Reported here is a new analytical multiclass method based on QuEChERS technique, which has proven to be effective in diagnosing fatal poisoning cases in animals. This method has been developed for the determination of analytes in liver samples comprising rodenticides, carbamate and organophosphorus pesticides, coccidiostats and mycotoxins. The procedure entails addition of acetonitrile and sodium acetate to 2 g of homogenized liver sample. The mixture was shaken intensively and centrifuged for phase separation, which was followed by an organic phase transfer into a tube containing sorbents (PSA and C18) and magnesium sulfate, then it was centrifuged, the supernatant was filtered and analyzed by liquid chromatography tandem mass spectrometry. A validation of the procedure was performed. Repeatability variation coefficients <15% have been achieved for most of the analyzed substances. Analytical conditions allowed for a successful separation of variety of poisons with the typical screening detection limit at ≤10 μg/kg levels. The method was used to investigate more than 100 animals poisoning incidents and proved that is useful to be used in animal forensic toxicology cases.

The prevalence of antiretroviral multidrug resistance in highly active antiretroviral therapy-treated patients with HIV/AIDS between 2004 and 2009 in South Korea.

PubMed

Choi, Ju-yeon; Kwon, Oh-Kyung; Choi, Byeong-Sun; Kee, Mee-Kyung; Park, Mina; Kim, Sung Soon

2014-06-01

Highly active antiretroviral therapy (HAART) including protease inhibitors (PIs) has been used in South Korea since 1997. Currently, more than 20 types of antiretroviral drugs are used in the treatment of human immunodeficiency virus-infected/acquired immune deficiency syndrome patients in South Korea. Despite the rapid development of various antiretroviral drugs, many drug-resistant variants have been reported after initiating HAART, and the efficiency of HAART is limited by these variants. To investigate and estimate the annual antiretroviral drug resistance and prevalence of antiretroviral multi-class drug resistance in Korean patients with experience of treatment. The amplified HIV-1 pol gene in 535 patients requested for genotypic drug resistance testing from 2004 to 2009 by the Korea Centers for Disease Control and Prevention was sequenced and analyzed annually and totally. The prevalence of antiretroviral drug resistance was estimated based on "SIR" interpretation of the Stanford sequence database. Of viruses derived from 787 specimens, 380 samples (48.3%) showed at least one drug class-related resistance. Predicted NRTI drug resistance was highest at 41.9%. NNRTI showed 27.2% resistance with 23.3% for PI. The percent of annual drug resistance showed similar pattern and slightly declined except 2004 and 2005. The prevalence of multi-class drug resistance against each drug class was: NRTI/NNRTI/PI, 9.8%; NRTI/PI, 21.9%; NNRTI/PI, 10.4%; and NRTI/NNRTI, 21.5%. About 50% and less than 10% of patients infected with HIV-1 have multidrug and multiclass resistance linked to 16 antiretroviral drugs, respectively. The significance of this study lies in its larger-scale examination of the prevalence of drug-resistant variants and multidrug resistance in HAART-experienced patients in South Korea. Copyright © 2014 Elsevier B.V. All rights reserved.

Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

2017-12-01

This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that using EOF techniques can capture the groundwater flow tendency and detects the correction vector of the simulated error sources. Hence, the established EOF-based methodology can effectively and accurately identify the multiple recharges and hydrogeological parameters.

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

Simultaneous determination of multiclass pesticide residues in human plasma using a mini QuEChERS method.

PubMed

Srivastava, Anshuman; Rai, Satyajeet; Kumar Sonker, Ashish; Karsauliya, Kajal; Pandey, Chandra Prabha; Singh, Sheelendra Pratap

2017-06-01

Blood is one of the most assessable matrices for the determination of pesticide residue exposure in humans. Effective sample preparation/cleanup of biological samples is very important in the development of a sensitive, reproducible, and robust method. In the present study, a simple, cost-effective, and rapid gas chromatography-tandem mass spectrometry method has been developed and validated for simultaneous analysis of 31 multiclass (organophosphates, organochlorines, and synthetic pyrethroids) pesticide residues in human plasma by means of a mini QuEChERS (quick, easy, cheap, effective, rugged, and safe) method. We have adopted a modified version of the QuEChERS method, which is primarily used for pesticide residue analysis in food commodities. The QuEChERS method was optimized by use of different extraction solvents and different amounts and combinations of salts and sorbents (primary-secondary amines and C 18 ) for the dispersive solid-phase extraction step. The results show that a combination of ethyl acetate with 2% acetic acid, magnesium sulfate (0.4 g), and solid-phase extraction for sample cleanup with primary-secondary amines (50 mg) per 1-mL volume of plasma is the most suitable for generating acceptable results with high recoveries for all multiclass pesticides from human plasma. The mean recovery ranged from 74% to 109% for all the analytes. The limit of quantification and limit of detection of the method ranged from 0.12 to 13.53 ng mL -1 and from 0.04 to 4.10 ng mL -1 respectively. The intraday precision and the interday precision of the method were 6% or less and 11% or less respectively. This method would be useful for the analysis of a wide range of pesticides of interest in a small volume of clinical and/or forensic samples to support biomonitoring and toxicological applications. Graphical Abstract Pesticide residues analysis in human plasma using mini QuEChERS method.

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.

PubMed

Yang, Lingjian; Ainali, Chrysanthi; Tsoka, Sophia; Papageorgiou, Lazaros G

2014-12-05

Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data.

PubMed

Wang, Kung-Jeng; Makond, Bunjira; Wang, Kung-Min

2013-11-09

Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE), cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

PubMed Central

2013-01-01

Background Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Methods Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Results Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. Conclusions LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR. PMID:24207108

Bayes classifiers for imbalanced traffic accidents datasets.

PubMed

Mujalli, Randa Oqab; López, Griselda; Garach, Laura

2016-03-01

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Imbalanced plasma ACE and ACE2 level in the uremic patients with cardiovascular diseases and its change during a single hemodialysis session.

PubMed

Yang, Chung-Wei; Lu, Li-Che; Chang, Chia-Chu; Cho, Ching-Chang; Hsieh, Wen-Yeh; Tsai, Chin-Hung; Lin, Yi-Chang; Lin, Chih-Sheng

2017-11-01

The renin-angiotensin system (RAS) has significant influences on heart and renal disease progression. Angiotensin converting enzyme (ACE) and angiotensin converting enzyme II (ACE2) are major peptidases of RAS components and play counteracting functions through angiotensin II (Ang II)/ATIR and angiotensin-(1-7) (Ang-(1-7))/Mas axis, respectively. There were 360 uremic patients on regular hemodialysis (HD) treatment (inclusive of 119 HD patients with cardiovascular diseases (CVD) and 241 HD patients without CVD and 50 healthy subjects were enrolled in this study. Plasma ACE, ACE2, Ang II and Ang-(1-7) levels of the HD patients were determined. We compared pre-HD levels of plasma ACE, ACE2, Ang II and Ang-(1-7) in the HD patients with and without CVD to those of the controls. The HD patients, particularly those with CVD, showed a significant increase in the levels of ACE and Ang II, whereas ACE2 and Ang-(1-7) levels were lower than those in the healthy controls. Therefore, imbalanced ACE/ACE2 was observed in the HD patients with CVD. In the course of a single HD session, the plasma ACE, ACE/ACE2 and Ang II levels in the HD patients with CVD were increased from pre-HD to post-HD. On the contrary, ACE2 levels were decreased after the HD session. These changes were not detected in the HD patients without CVD. Pathogenically imbalanced circulating ACE/ACE2 was detected in the HD patients, particularly those with CVD. HD session could increase ACE/Ang II/AT1R axis and decrease ACE2/Ang-(1-7)/Mas axis activity in the circulation of HD patients with CVD.

Effects of imbalanced muscle loading on hip joint development and maturation.

PubMed

Ford, Caleb A; Nowlan, Niamh C; Thomopoulos, Stavros; Killian, Megan L

2017-05-01

The mechanical loading environment influences the development and maturation of joints. In this study, the influence of imbalanced muscular loading on joint development was studied using localized chemical denervation of hip stabilizing muscle groups in neonatal mice. It was hypothesized that imbalanced muscle loading, targeting either gluteal muscles or quadriceps muscles, would lead to bilateral hip joint asymmetry, as measured by acetabular coverage, femoral head volume and bone morphometry, and femoral-acetabular shape. The contralateral hip joints as well as age-matched, uninjected mice were used as controls. Altered bone development was analyzed using micro-computed tomography, histology, and image registration techniques at postnatal days (P) 28, 56, and 120. This study found that unilateral muscle unloading led to reduced acetabular coverage of the femoral head, lower total volume, lower bone volume ratio, and lower mineral density, at all three time points. Histologically, the femoral head was smaller in unloaded hips, with thinner triradiate cartilage at P28 and thinner cortical bone at P120 compared to contralateral hips. Morphological shape changes were evident in unloaded hips at P56. Unloaded hips had lower trabecular thickness and increased trabecular spacing of the femoral head compared to contralateral hips. The present study suggests that decreased muscle loading of the hip leads to altered bone and joint shape and growth during postnatal maturation. Statement of Clinical Significance: Adaptations from altered muscle loading during postnatal growth investigated in this study have implications on developmental hip disorders that result from asymmetric loading, such as patients with limb-length inequality or dysplasia. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1128-1136, 2017. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Online breakage detection of multitooth tools using classifier ensembles for imbalanced data

NASA Astrophysics Data System (ADS)

Bustillo, Andrés; Rodríguez, Juan J.

2014-12-01

Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutting process. In this paper, we present an analysis of different data-mining techniques applied to the detection of insert breakage in multitooth tools. The analysis applies only one experimental variable: the electrical power consumption of the tool drive. This restriction profiles real industrial conditions more accurately than other physical variables, such as acoustic or vibration signals, which are not so easily measured. Many efforts have been made to design a method that is able to identify breakages with a high degree of reliability within a short period of time. The solution is based on classifier ensembles for imbalanced data-sets. Classifier ensembles are combinations of classifiers, which in many situations are more accurate than individual classifiers. Six different base classifiers are tested: Decision Trees, Rules, Naïve Bayes, Nearest Neighbour, Multilayer Perceptrons and Logistic Regression. Three different balancing strategies are tested with each of the classifier ensembles and compared to their performance with the original data-set: Synthetic Minority Over-Sampling Technique (SMOTE), undersampling and a combination of SMOTE and undersampling. To identify the most suitable data-mining solution, Receiver Operating Characteristics (ROC) graph and Recall-precision graph are generated and discussed. The performance of logistic regression ensembles on the balanced data-set using the combination of SMOTE and undersampling turned out to be the most suitable technique. Finally a comparison using industrial performance measures is presented, which concludes that this technique is also more suited to this industrial problem than the other techniques presented in the bibliography.

Ruggedness testing and validation of a practical analytical method for > 100 veterinary drug residues in bovine muscle by ultrahigh performance liquid chromatography – tandem mass spectrometry

USDA-ARS?s Scientific Manuscript database

In this study, optimization, extension, and validation of a streamlined, qualitative and quantitative multiclass, multiresidue method was conducted to monitor great than100 veterinary drug residues in meat using ultrahigh-performance liquid chromatography – tandem mass spectrometry (UHPLC-MS/MS). I...

Working Together: An Empirical Analysis of a Multiclass Legislative-Executive Branch Simulation

ERIC Educational Resources Information Center

Kalaf-Hughes, Nicole; Mills, Russell W.

2016-01-01

Much of the research on the use of simulations in the political science classroom focuses on how simulations model different events in the real world, including political campaigns, international diplomacy, and legislative bargaining. In the case of American Politics, many simulations focus on the behavior of Congress and the legislative process,…

77 FR 71433 - Notice of Proposed Information Collection: Comment Request; Ginnie Mae Multiclass Securities...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-30

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75 FR 5336 - Notice of Proposed Information Collection: Comment Request; Ginnie Mae Multiclass Securities...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-02

... 8 120 0.5 60 Tax Opinion Attorney for Sponsor........ 15 8 120 4 480 Transfer Affidavit Attorney for... Final Data Statements (attached to closing Accountant for Sponsor...... 15 8 120 32 3,840 letter). Accountants' Closing Letter Accountant 15 8 120 8 960 Accountants' OCS Letter Accountant 15 8 120 8 960...

QuEChERS, a sample preparation technique that is “catching on”: an up-to-date interview with its inventors

USDA-ARS?s Scientific Manuscript database

The technique of QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) is only 7 years old, yet it is revolutionizing the manner in which multiresidue, multiclass pesticide analysis (and perhaps beyond) is performed. Columnist Ron Majors sits down with inventors Steve Lehotay and Michelangelo An...

Designing an Engaged Swarm: Toward a "Techne" for Multi-Class, Interdisciplinary Collaborations with Nonprofit Partners

ERIC Educational Resources Information Center

McCarthy, Seán

2016-01-01

This essay proposes a model of university-community partnership called "an engaged swarm" that mobilizes networks of students from across classes and disciplines to work with off-campus partners such as nonprofits. Based on theories that translate the distributed, adaptive, and flexible activity of actors in biological systems to…

Negative Correlates of Part-Time Employment during Adolescence: Replication and Elaboration.

ERIC Educational Resources Information Center

Steinberg, Laurence; Dornbusch, Sanford M.

This study examined the relation between part-time employment and adolescent behavior and development in a multi-ethnic, multi-class sample of approximately 4,000 15- through 18-year-olds. The results indicated that long work hours during the school year were associated with diminished investment in schooling and lowered school performance,…

Fisher classifier and its probability of error estimation

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

78 FR 21393 - Notice of Submission of Proposed Information Collection to OMB Ginnie Mae Multiclass Securities...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-10

..., allowing the private sector to combine and restructure cash flows from Ginnie Mae Single Class MBS into... program, Ginnie Mae guarantees, with the full faith and credit of the United States, the timely payment of... combine and restructure cash flows from Ginnie Mae Single Class MBS into securities that meet unique...

Multi-Class Analytical Models of the DECsystem-10 Job-Swapping Behavior.

DTIC Science & Technology

1981-12-01

Mark of the Unicorn for developing Scribble, the best text formatter for a CP/M home computer I’ve ever seen, and for their patient help when I...requests for service will tend to decrease as the queue length grows . The finite population model, also known as the machine interference model, was

Generalized Partial Least Squares Approach for Nominal Multinomial Logit Regression Models with a Functional Covariate

ERIC Educational Resources Information Center

Albaqshi, Amani Mohammed H.

2017-01-01

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

Mechanisms of the prevention and inhibition of the progression and development of non-alcoholic steatohepatitis by genetic and pharmacological decoy receptor 3 supplementation.

PubMed

Lee, Pei-Chang; Yang, Ling-Yu; Wang, Ying-Wen; Huang, Shiang-Fen; Lee, Kuei-Chuan; Hsieh, Yun-Cheng; Yang, Ying-Ying; Hsieh, Shie-Liang; Hou, Ming-Chih; Lin, Han-Chieh; Lee, Fa-Yuah; Lee, Shou-Dong

2017-11-01

Treatment of non-alcoholic steatohepatitis (NASH) is difficult due to the absence of a proven treatment and its comprehensive mechanisms. In the NASH animal model, upregulated hepatic inflammation and oxidative stress, with the resultant M1 polarization of macrophages as well as imbalanced adipocytokines, all accelerate NASH progression. As a member of the tumor necrosis factor receptor superfamily, decoy receptor 3 (DcR3) not only neutralizes the death ligands, but also performs immune modulations. In this study, we aimed to investigate the possible non-decoy effects of DcR3 on diet-induced NASH mice. Methionine- and choline-deficient (MCD) diet feeding for 9 weeks was applied to induce NASH in BALB/c mice. Decoy receptor 3 heterozygous transgenesis or pharmacological pretreatment with DcR3a for 1 month were designed as interventions. Intrahepatic inflammatory status as well as macrophage polarization, oxidative stress, and steatosis as well as lipogenic gene expression and fibrotic status were analyzed. Additionally, acute effects of DcR3a on HepG2 cells, Hep3B cells, and primary mouse hepatocytes in various MCD medium-stimulated changes were also evaluated. Both DcR3 genetic and pharmacologic supplement significantly reduced MCD diet-induced hepatic M1 polarization. In addition, DcR3 supplement attenuated MCD diet-increased hepatic inflammation, oxidative stress, adipocytokine imbalance, steatosis, and fibrogenesis. Moreover, acute DcR3a incubation in HepG2 cells, Hep3B cells, and mouse hepatocytes could normalize the expression of genes related to lipid oxidation along with inflammation and oxidative stress. The ability of DcR3 to attenuate hepatic steatosis and inflammation through its non-decoy effects of immune modulation and oxidative stress attenuation makes it a potential treatment for NASH. © 2017 The Japan Society of Hepatology.

7 CFR 1980.469 - Loan servicing.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Regulations of the Department of Agriculture (Continued) RURAL HOUSING SERVICE, RURAL BUSINESS-COOPERATIVE... REGULATIONS (CONTINUED) GENERAL Business and Industrial Loan Program § 1980.469 Loan servicing. The lender is.... Adverse trends in the borrower's operation or an imbalanced position in the balance sheet which has not...

7 CFR 1980.469 - Loan servicing.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Regulations of the Department of Agriculture (Continued) RURAL HOUSING SERVICE, RURAL BUSINESS-COOPERATIVE... REGULATIONS (CONTINUED) GENERAL Business and Industrial Loan Program § 1980.469 Loan servicing. The lender is.... Adverse trends in the borrower's operation or an imbalanced position in the balance sheet which has not...

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Double light-cone dynamics establish thermal states in integrable 1D Bose gases

NASA Astrophysics Data System (ADS)

Langen, T.; Schweigler, T.; Demler, E.; Schmiedmayer, J.

2018-02-01

We theoretically investigate the non-equilibrium dynamics in a quenched pair of one-dimensional Bose gases with density imbalance. We describe the system using its low-energy effective theory, the Luttinger liquid model. In this framework the system shows strictly integrable relaxation dynamics via dephasing of its approximate many-body eigenstates. In the balanced case, this leads to the well-known light-cone-like establishment of a prethermalized state, which can be described by a generalized Gibbs ensemble. In the imbalanced case the integrable dephasing leads to a state that, counter-intuitively, closely resembles a thermal equilibrium state. The approach to this state is characterized by two separate light-cone dynamics with distinct characteristic velocities. This behavior is a result of the fact that in the imbalanced case observables are not aligned with the conserved quantities of the integrable system. We discuss a concrete experimental realization to study this effect using matterwave interferometry and many-body revivals on an atom chip.

Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests.

PubMed

Sadiq, Saad; Yan, Yilin; Shyu, Mei-Ling; Chen, Shu-Ching; Ishwaran, Hemant

2016-07-01

Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed to generate predictor variables based on multivariate regressions using variable importance (VIMP). By fine tuning the forests and significantly reducing the predictor variables, the concept detection scores are evaluated when the concept of interest is rare and imbalanced, i.e., having little collaboration with other high level concepts. Using classical multivariate statistics, estimating the value of one coordinate using other coordinates standardizes the covariates and it depends upon the variance of the correlations instead of the mean. Thus, conditional dependence on the data being normally distributed is eliminated. Experimental results demonstrate that the proposed framework outperforms those approaches in the comparison in terms of the Mean Average Precision (MAP) values.

FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

PubMed Central

Seeja, K. R.; Zareapoor, Masoumeh

2014-01-01

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers. PMID:25302317

FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

PubMed

Seeja, K R; Zareapoor, Masoumeh

2014-01-01

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

Spin-imbalanced pairing and Fermi surface deformation in flat bands

NASA Astrophysics Data System (ADS)

Huhtinen, Kukka-Emilia; Tylutki, Marek; Kumar, Pramod; Vanhala, Tuomas I.; Peotta, Sebastiano; Törmä, Päivi

2018-06-01

We study the attractive Hubbard model with spin imbalance on two lattices featuring a flat band: the Lieb and kagome lattices. We present mean-field phase diagrams featuring exotic superfluid phases, similar to the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, whose stability is confirmed by dynamical mean-field theory. The nature of the pairing is found to be richer than just the Fermi surface shift responsible for the usual FFLO state. The presence of a flat band allows for changes in the particle momentum distributions at null energy cost. This facilitates formation of nontrivial superfluid phases via multiband Cooper pair formation: the momentum distribution of the spin component in the flat band deforms to mimic the Fermi surface of the other spin component residing in a dispersive band. The Fermi surface of the unpaired particles that are typical for gapless superfluids becomes deformed as well. The results highlight the profound effect of flat dispersions on Fermi surface instabilities, and provide a potential route for observing spin-imbalanced superfluidity and superconductivity.

An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

PubMed

Yu, Hualong; Ni, Jun

2014-01-01

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

Symmetry Breaking in the Supramolecular Gels of an Achiral Gelator Exclusively Driven by π-π Stacking.

PubMed

Shen, Zhaocun; Jiang, Yuqian; Wang, Tianyu; Liu, Minghua

2015-12-30

Supramolecular symmetry breaking, in which chiral assemblies with imbalanced right- and left-handedness emerge from achiral molecular building blocks, has been achieved in the organogels of a C3-symmetric molecule only via π-π stacking. Specifically, an achiral C3-symmetric benzene-1,3,5-tricarboxylate substituted with methyl cinnamate through ester bond was found to form organogels in various organic solvents. More interestingly, when gels formed in cyclohexane, symmetry breaking occurred; i.e., optically active organogels together with the helical nanofibers with predominant handedness were obtained. Furthermore, the stochastically appeared imbalanced helicity could be driven to desired handedness by utilizing slight chiral solvents such as (R)- or (S)-terpinen-4-ol. Remarkably, the handedness of supramolecular assemblies thus formed could be kept even when the chiral solvents were removed. For the first time, we show that symmetry breaking can occur in supramolecular gel system driven exclusively through π-π stacking.

Quench dynamics of the spin-imbalanced Fermi-Hubbard model in one dimension

NASA Astrophysics Data System (ADS)

Yin, Xiao; Radzihovsky, Leo

2016-12-01

We study a nonequilibrium dynamics of a one-dimensional spin-imbalanced Fermi-Hubbard model following a quantum quench of on-site interaction, realizable, for example, in Feshbach-resonant atomic Fermi gases. We focus on the post-quench evolution starting from the initial BCS and Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) ground states and analyze the corresponding spin-singlet, spin-triplet, density-density, and magnetization-magnetization correlation functions. We find that beyond a light-cone crossover time, rich post-quench dynamics leads to thermalized and pre-thermalized stationary states that display strong dependence on the initial ground state. For initially gapped BCS state, the long-time stationary state resembles thermalization with the effective temperature set by the initial value of the Hubbard interaction. In contrast, while the initial gapless FFLO state reaches a stationary pre-thermalized form, it remains far from equilibrium. We suggest that such post-quench dynamics can be used as a fingerprint for identification and study of the FFLO phase.

Barriers and facilitators of HIV prevention with heterosexual Latino couples: beliefs of four stakeholder groups.

PubMed

Pérez-Jiménez, David; Seal, David W; Serrano-García, Irma

2009-01-01

Although HIV prevention interventions for women are efficacious, long-term behavior change maintenance within power-imbalanced heterosexual relationships has been difficult. To explore the feasibility, content, and format of an HIV intervention for Latino couples, the authors conducted 13 focus groups with HIV/AIDS researchers, service providers, and heterosexual men and women in Puerto Rico, the Dominican Republic, and Mexico. Reasons that participants thought that men should be involved in prevention efforts included promotion of shared responsibility, creation of a safe environment for open conversation about sex, and increased sexual negotiation skills. Perceived barriers to men's involvement included cultural taboos, sexual conservatism associated with Catholicism and machismo, and power-imbalanced relationships. Participants stressed the need for recruitment of men within naturally occurring settings or by influential community leaders. Participants indicated that couples-level interventions would be successful if they used strong coed facilitators, included both unigender and mixed-gender discussion opportunities, and addressed personally meaningful topics. Implications of these findings are discussed.

Barriers and Facilitators of HIV Prevention With Heterosexual Latino Couples: Beliefs of Four Stakeholder Groups

PubMed Central

Pérez-Jiménez, David; Seal, David W.; Serrano-García, Irma

2012-01-01

Although HIV prevention interventions for women are efficacious, long-term behavior change maintenance within power-imbalanced heterosexual relationships has been difficult. To explore the feasibility, content, and format of an HIV intervention for Latino couples, the authors conducted 13 focus groups with HIV/AIDS researchers, service providers, and heterosexual men and women in Puerto Rico, the Dominican Republic, and Mexico. Reasons that participants thought that men should be involved in prevention efforts included promotion of shared responsibility, creation of a safe environment for open conversation about sex, and increased sexual negotiation skills. Perceived barriers to men’s involvement included cultural taboos, sexual conservatism associated with Catholicism and machismo, and power-imbalanced relationships. Participants stressed the need for recruitment of men within naturally occurring settings or by influential community leaders. Participants indicated that couples-level interventions would be successful if they used strong coed facilitators, included both unigender and mixed-gender discussion opportunities, and addressed personally meaningful topics. Implications of these findings are discussed. PMID:19209976

Imbalanced pattern completion vs. separation in cognitive disease: network simulations of synaptic pathologies predict a personalized therapeutics strategy.

PubMed

Hanson, Jesse E; Madison, Daniel V

2010-08-13

Diverse Mouse genetic models of neurodevelopmental, neuropsychiatric, and neurodegenerative causes of impaired cognition exhibit at least four convergent points of synaptic malfunction: 1) Strength of long-term potentiation (LTP), 2) Strength of long-term depression (LTD), 3) Relative inhibition levels (Inhibition), and 4) Excitatory connectivity levels (Connectivity). To test the hypothesis that pathological increases or decreases in these synaptic properties could underlie imbalances at the level of basic neural network function, we explored each type of malfunction in a simulation of autoassociative memory. These network simulations revealed that one impact of impairments or excesses in each of these synaptic properties is to shift the trade-off between pattern separation and pattern completion performance during memory storage and recall. Each type of synaptic pathology either pushed the network balance towards intolerable error in pattern separation or intolerable error in pattern completion. Imbalances caused by pathological impairments or excesses in LTP, LTD, inhibition, or connectivity, could all be exacerbated, or rescued, by the simultaneous modulation of any of the other three synaptic properties. Because appropriate modulation of any of the synaptic properties could help re-balance network function, regardless of the origins of the imbalance, we propose a new strategy of personalized cognitive therapeutics guided by assay of pattern completion vs. pattern separation function. Simulated examples and testable predictions of this theorized approach to cognitive therapeutics are presented.

An Interdistrict Transfer Program

ERIC Educational Resources Information Center

Gross, Norman

1975-01-01

This testimony, before the May 1974 public hearings of the New York City Commission on Human Rights by the Administrator, Urban-Suburban Transfer Program and Inter district Transfer Program, West Irondequoit School District, New York, reviews a program which began with 25 minority group youngsters from one racially-imbalanced Rochester school…

Multi-class methodology to determine pesticides and mycotoxins in green tea and royal jelly supplements by liquid chromatography coupled to Orbitrap high resolution mass spectrometry.

PubMed

Martínez-Domínguez, Gerardo; Romero-González, Roberto; Garrido Frenich, Antonia

2016-04-15

A multi-class methodology was developed to determine pesticides and mycotoxins in food supplements. The extraction was performed using acetonitrile acidified with formic acid (1%, v/v). Different clean-up sorbents were tested, and the best results were obtained using C18 and zirconium oxide for green tea and royal jelly, respectively. The compounds were determined using ultra high performance liquid chromatography (UHPLC) coupled to Exactive-Orbitrap high resolution mass spectrometry (HRMS). The recovery rates obtained were between 70% and 120% for most of the compounds studied with a relative standard deviation <25%, at three different concentration levels. The calculated limits of quantification (LOQ) were <10 μg/kg. The method was applied to green tea (10) and royal jelly (8) samples. Nine (eight of green tea and one of royal jelly) samples were found to be positive for pesticides at concentrations ranging from 10.6 (cinosulfuron) to 47.9 μg/kg (paclobutrazol). The aflatoxin B1 (5.4 μg/kg) was also found in one of the green tea samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Freeze-thaw approach: A practical sample preparation strategy for residue analysis of multi-class veterinary drugs in chicken muscle.

PubMed

Zhang, Meiyu; Li, Erfen; Su, Yijuan; Song, Xuqin; Xie, Jingmeng; Zhang, Yingxia; He, Limin

2018-06-01

Seven drugs from different classes, namely, fluoroquinolones (enrofloxacin, ciprofloxacin, sarafloxacin), sulfonamides (sulfadimidine, sulfamonomethoxine), and macrolides (tilmicosin, tylosin), were used as test compounds in chickens by oral administration, a simple extraction step after cryogenic freezing might allow the effective extraction of multi-class veterinary drug residues from minced chicken muscles by mix vortexing. On basis of the optimized freeze-thaw approach, a convenient, selective, and reproducible liquid chromatography with tandem mass spectrometry method was developed. At three spiking levels in blank chicken and medicated chicken muscles, average recoveries of the analytes were in the range of 71-106 and 63-119%, respectively. All the relative standard deviations were <20%. The limits of quantification of analytes were 0.2-5.0 ng/g. Regardless of the chicken levels, there were no significant differences (P > 0.05) in the average contents of almost any of the analytes in medicated chickens between this method and specific methods in the literature for the determination of specific analytes. Finally, the developed method was successfully extended to the monitoring of residues of 55 common veterinary drugs in food animal muscles. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

PubMed Central

Kim, Jong-Myon

2018-01-01

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

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.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

Natural stimuli improve auditory BCIs with respect to ergonomics and performance

NASA Astrophysics Data System (ADS)

Höhne, Johannes; Krenzlin, Konrad; Dähne, Sven; Tangermann, Michael

2012-08-01

Moving from well-controlled, brisk artificial stimuli to natural and less-controlled stimuli seems counter-intuitive for event-related potential (ERP) studies. As natural stimuli typically contain a richer internal structure, they might introduce higher levels of variance and jitter in the ERP responses. Both characteristics are unfavorable for a good single-trial classification of ERPs in the context of a multi-class brain-computer interface (BCI) system, where the class-discriminant information between target stimuli and non-target stimuli must be maximized. For the application in an auditory BCI system, however, the transition from simple artificial tones to natural syllables can be useful despite the variance introduced. In the presented study, healthy users (N = 9) participated in an offline auditory nine-class BCI experiment with artificial and natural stimuli. It is shown that the use of syllables as natural stimuli does not only improve the users’ ergonomic ratings; also the classification performance is increased. Moreover, natural stimuli obtain a better balance in multi-class decisions, such that the number of systematic confusions between the nine classes is reduced. Hopefully, our findings may contribute to make auditory BCI paradigms more user friendly and applicable for patients.

Relationships between Child Behavior Problems and Family Functioning: A Literature Review.

ERIC Educational Resources Information Center

van As, N. M. C.; Janssens, J. M. A. M.

2002-01-01

Reviews research examining the relationship between family functioning and child behavior problems. Focuses on parenting styles, intergenerational relationships, family structure, and family interaction patterns. Finds that child behavior problems are related to a lack of parental support, an imbalanced parent- child relationship, a lack of…

Tools to Support Expository Video Capture and Access

ERIC Educational Resources Information Center

Carter, Scott; Cooper, Matthew; Adcock, John; Branham, Stacy

2014-01-01

Video tends to be imbalanced as a medium. Typically, content creators invest enormous effort creating work that is then watched passively. However, learning tasks require that users not only consume video but also engage, interact with, and repurpose content. Furthermore, to promote learning across domains where content creators are not…

Gender, Interest, and Prior Experience Shape Opportunities to Learn Programming in Robotics Competitions

ERIC Educational Resources Information Center

Witherspoon, Eben B.; Schunn, Christian D.; Higashi, Ross M.; Baehr, Emily C.

2016-01-01

Background: Robotics competitions are increasingly popular and potentially provide an on-ramp to computer science, which is currently highly gender imbalanced. However, within competitive robotics teams, student participation in programming is not universal. This study gathered surveys from over 500 elementary, middle, and high school robotics…

Teacher-Student Power Relations as a Reflection of Multileveled Intertwined Interactions

ERIC Educational Resources Information Center

Wong, Mei-Yee

2016-01-01

This paper examines the factors shaping teacher-student power relations, based on observations, interviews and document analysis from a Hong Kong study. It identifies and examines six factors: China's traditional culture of respect, examination-oriented teaching and religious culture were found to encourage imbalanced teacher-student power…

Evaluation in the Design of Complex Systems

ERIC Educational Resources Information Center

Ho, Li-An; Schwen, Thomas M.

2006-01-01

We identify literature that argues the process of creating knowledge-based system is often imbalanced. In most knowledge-based systems, development is often technology-driven instead of requirement-driven. Therefore, we argue designers must recognize that evaluation is a critical link in the application of requirement-driven development models…

Experience with an Independent Study Program in Pathophysiology for Doctor of Pharmacy Students.

ERIC Educational Resources Information Center

Nahata, Milap C.

1986-01-01

A pharmacy doctoral program's independent-study component in pathophysiology, supported by computer-assisted instruction and self-evaluation, has the advantages of self-pacing, reduced faculty time commitment, and increased ability to work effectively with physicians. Disadvantages include student feeling of isolation, imbalanced content, and…

Multiclass Data Segmentation using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2014-01-01

37] that performs interac- tive image segmentation using the solution to a combinatorial Dirichlet problem. Elmoataz et al . have developed general...izations of the graph Laplacian [25] for image denoising and manifold smoothing. Couprie et al . in [18] define a conve- niently parameterized graph...continuous setting carry over to the discrete graph representation. For general data segmentation, Bresson et al . in [8], present rigorous convergence

Divergence and Necessary Conditions for Extremums

NASA Technical Reports Server (NTRS)

Quirein, J. A.

1973-01-01

The problem is considered of finding a dimension reducing transformation matrix B that maximizes the divergence in the reduced dimension for multi-class cases. A comparitively simple expression for the gradient of the average divergence with respect to B is developed. The developed expression for the gradient contains no eigenvectors or eigenvalues; also, all matrix inversions necessary to evaluate the gradient are available from computing the average divergence.

AI User Support System for SAP ERP

NASA Astrophysics Data System (ADS)

Vlasov, Vladimir; Chebotareva, Victoria; Rakhimov, Marat; Kruglikov, Sergey

2017-10-01

An intelligent system for SAP ERP user support is proposed in this paper. It enables automatic replies on users’ requests for support, saving time for problem analysis and resolution and improving responsiveness for end users. The system is based on an ensemble of machine learning algorithms of multiclass text classification, providing efficient question understanding, and a special framework for evidence retrieval, providing the best answer derivation.

Evaluation of a new carbon/zirconia-based sorbent for the cleanup of food extracts in multiclass analysis of pesticides and environmental contaminants

USDA-ARS?s Scientific Manuscript database

A novel carbon/zirconia based material, SupelTM QuE Verde (Verde), was evaluated in a filter-vial dispersive solid phase extraction (d-SPE) cleanup of QuEChERS extracts of pork, salmon, kale, and avocado for residual analysis of pesticides and environmental contaminants. Low pressure (LP) GC-MS/MS w...

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

PubMed

Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

2010-01-30

Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.

Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.

PubMed

Dimitriadis, Stavros I; Marimpis, Avraam D

2018-01-01

A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class ( N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class ( N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class ( N = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5-4 Hz), θ: (4-8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 324 bits/min with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of 10-25 bits/min . In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

PubMed Central

2010-01-01

Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context. PMID:20113515

Short-Term Volunteer Teachers in Rural China: Challenges and Needs

ERIC Educational Resources Information Center

Zhou, Huiquan; Shang, Xinyuan

2011-01-01

The brain-drain caused by imbalanced economic development has produced a lack of qualified teachers in rural China. Short-term volunteer teaching has emerged as a response. Despite the popularity of such programs, little systematic data have been gathered regarding their strengths and weaknesses. A short-term volunteer teaching program was…

Neural Dynamics of Autistic Behaviors: Cognitive, Emotional, and Timing Substrates

ERIC Educational Resources Information Center

Grossberg, Stephen; Seidman, Don

2006-01-01

What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that proposes how cognitive, emotional, timing, and motor processes that involve brain regions such as the prefrontal and temporal…

"The Great Transformation" and Suicide: Local and Long-Lasting Effects of 1930 Bank Suspensions

ERIC Educational Resources Information Center

Baller, Robert D.; Levchak, Phil; Schultz, Mark

2010-01-01

Depression-era bank suspensions and failures are conceptualized as products of the first part of what Polanyi (1994) called "The Great Transformation," which involved an imbalanced institutional arrangement in which the economy dominated other institutions. Relying on Durkheim (1897/1951) and Merton (1938, 1968), it is argued that these…

Teacher Perspectives of Challenges within the Norwegian Educational System

ERIC Educational Resources Information Center

Anderson, Sarah K.; Terras, Katherine L.

2015-01-01

This research examines teacher perspectives' of educational challenges in Norway. Norway is one of the most well-resourced, prosperous, social welfare states in the world, yet the OECD (2011) recognized students' weak basic skills and insufficient teacher ability in content and pedagogy, along with engagement and imbalanced resources as points for…

Can Restorative Practices Help to Reduce Disparities in School Discipline Data? A Review of the Literature

ERIC Educational Resources Information Center

Kline, Dana M. Stewart

2016-01-01

Disparities in school discipline data is a national concern. Schools across the nation are working to eliminate these disparities. This literature review considers one response to the disproportionate data, restorative practices. This article examines why exclusionary approaches to discipline are ineffective, contribute to imbalanced discipline…

Data Science for Imbalanced Data: Methods and Applications

ERIC Educational Resources Information Center

Johnson, Reid A.

2016-01-01

Data science is a broad, interdisciplinary field concerned with the extraction of knowledge or insights from data, with the classification of data as a core, fundamental task. One of the most persistent challenges faced when performing classification is the class imbalance problem. Class imbalance refers to when the frequency with which each class…

Balancing energy consumption with hybrid clustering and routing strategy in wireless sensor networks.

PubMed

Xu, Zhezhuang; Chen, Liquan; Liu, Ting; Cao, Lianyang; Chen, Cailian

2015-10-20

Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in Sensors 2015, 15 26584 gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.

Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.

PubMed

He, Jianjun; Gu, Hong; Liu, Wenqi

2012-01-01

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches.

Improving imbalanced scientific text classification using sampling strategies and dictionaries.

PubMed

Borrajo, L; Romero, R; Iglesias, E L; Redondo Marey, C M

2011-09-15

Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation. Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when they are applied to search on the PubMed scientific database. Another purpose of this paper is to study the use of dictionaries in the classification of biomedical texts. Experiments are conducted with three different dictionaries (BioCreative, NLPBA, and an ad-hoc subset of the UniProt database named Protein) using the mentioned classifiers and sampling strategies. Best results were obtained with NLPBA and Protein dictionaries and the SVM classifier using the Subsampling balancing technique. These results were compared with those obtained by other authors using the TREC Genomics 2005 public corpus. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.

A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems.

PubMed

Wu, Zhenyu; Guo, Yang; Lin, Wenfang; Yu, Shuyang; Ji, Yang

2018-04-05

Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional fault diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced fault diagnosis for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods.

Study of imbalanced internal resistance on drop voltage of LiFePO4 battery system connected in parallel

NASA Astrophysics Data System (ADS)

Adie Perdana, Fengky; Supriyanto, Agus; Purwanto, Agus; Jamaluddin, Anif

2017-01-01

The purpose of this research focuses on the effect of imbalanced internal resistance for the drop voltage of LiFePO4 18650 battery system connected in parallel. The battery pack has been assembled consist of two cell battery LiFePO4 18650 that has difference combination of internal resistance. Battery pack was tested with 1/C constant current charging, 3,65V per group sel, 3,65V constant voltage charging, 5 minutes of rest time between charge and discharge process, 1/2C Constant current discharge until 2,2V, 26 cycle of measurement test, and 4320 minutes rest time after the last charge cycle. We can conclude that the difference combination of internal resistance on the battery pack seriously influence the drop voltage of a battery. Theoretical and experimental result show that the imbalance of internal resistance during cycling are mainly responsible for the drop voltage of LiFePO4 parallel batteries. It is thus a good way to avoid drop voltage fade of parallel battery system by suppressing variations of internal resistance.

Rayleigh-Taylor mixing with time-dependent acceleration

NASA Astrophysics Data System (ADS)

Abarzhi, Snezhana

2016-10-01

We extend the momentum model to describe Rayleigh-Taylor (RT) mixing driven by a time-dependent acceleration. The acceleration is a power-law function of time, similarly to astrophysical and plasma fusion applications. In RT flow the dynamics of a fluid parcel is driven by a balance per unit mass of the rates of momentum gain and loss. We find analytical solutions in the cases of balanced and imbalanced gains and losses, and identify their dependence on the acceleration exponent. The existence is shown of two typical regimes of self-similar RT mixing-acceleration-driven Rayleigh-Taylor-type and dissipation-driven Richtymer-Meshkov-type with the latter being in general non-universal. Possible scenarios are proposed for transitions from the balanced dynamics to the imbalanced self-similar dynamics. Scaling and correlations properties of RT mixing are studied on the basis of dimensional analysis. Departures are outlined of RT dynamics with time-dependent acceleration from canonical cases of homogeneous turbulence as well as blast waves with first and second kind self-similarity. The work is supported by the US National Science Foundation.

Rayleigh-Taylor mixing with space-dependent acceleration

NASA Astrophysics Data System (ADS)

Abarzhi, Snezhana

2016-11-01

We extend the momentum model to describe Rayleigh-Taylor (RT) mixing driven by a space-dependent acceleration. The acceleration is a power-law function of space coordinate, similarly to astrophysical and plasma fusion applications. In RT flow the dynamics of a fluid parcel is driven by a balance per unit mass of the rates of momentum gain and loss. We find analytical solutions in the cases of balanced and imbalanced gains and losses, and identify their dependence on the acceleration exponent. The existence is shown of two typical sub-regimes of self-similar RT mixing - the acceleration-driven Rayleigh-Taylor-type mixing and dissipation-driven Richtymer-Meshkov-type mixing with the latter being in general non-universal. Possible scenarios are proposed for transitions from the balanced dynamics to the imbalanced self-similar dynamics. Scaling and correlations properties of RT mixing are studied on the basis of dimensional analysis. Departures are outlined of RT dynamics with space-dependent acceleration from canonical cases of homogeneous turbulence as well as blast waves with first and second kind self-similarity. The work is supported by the US National Science Foundation.

Testing universality of Efimov Physics based on a mass-imbalanced Li-Cs mixture

NASA Astrophysics Data System (ADS)

Johansen, Jacob; Desalvo, Brian; Patel, Krutik; Chin, Cheng

2017-04-01

Efimov states are notable for their universal geometric scaling and are observable in ultracold atomic systems employing magnetic Feshbach resonances. In addition to geometric scaling, which we observed previously by taking advantage of a reduced Efimov scaling constant in our mass imbalanced 6Li-133Cs system, an interesting pattern has emerged in Efimov measurements: while expected to be non-universal, the absolute positions of Efimov resonances appear to scale simply with van der Waals length. Theories attempting to explain this observation have predicted a dependence on the strength of the Feshbach resonance for narrow resonances, yet experiments attempting to probe this regime have so far been inconsistent with the predicted dependence. In this talk, we focus primarily on our recent measurements showing dependence on Feshbach resonance strength. We directly compare two Feshbach resonances, one broad and one very narrow, which are nearly identical with the exception of the resonance strength, and find a striking difference in the first Efimov resonance position. Our measurement makes significant strides toward resolving the discrepancy between experiment and theory which exists in the field today.

Spatiotemporal Evolution of the Imbalanced Regional Development in Mainland China Using Dmsp-Ols Data

NASA Astrophysics Data System (ADS)

Chen, K.; Jia, T.

2017-09-01

The Defense Meteorological Satellite Programs Operational Linescan System (DMSP-OLS) nighttime lights imagery has been widely used to monitor economic activities and regional development in recent decades. In this paper, we firstly processed the nighttime light imageries of the Mainland China from 1992 to 2013 due to the radiation or geometric errors. Secondly, by dividing the Mainland China into seven regions, we found high correlation between the sum light values and GDP of each region. Thirdly, we extracted the economic centers of each region based on their nighttime light images. Through the analysis, we found the distribution of these economic centers was relatively concentrated and the migration of these economic centers showed certain directional trend or circuitous changes, which suggested the imbalanced socio-economic development of each region. Then, we calculated the Regional Development Gini of each region using the nighttime light data, which indicated that social-economic development in South China presents great imbalance while it is relatively balanced in Southwest China. This study would benefit the macroeconomic control to regional economic development and the introduction of appropriate economic policies from the national level.

A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems

PubMed Central

Guo, Yang; Lin, Wenfang; Yu, Shuyang; Ji, Yang

2018-01-01

Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional fault diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced fault diagnosis for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods. PMID:29621131

A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification.

PubMed

Krawczyk, Bartosz; Schaefer, Gerald; Woźniak, Michał

2015-11-01

Early recognition of breast cancer, the most commonly diagnosed form of cancer in women, is of crucial importance, given that it leads to significantly improved chances of survival. Medical thermography, which uses an infrared camera for thermal imaging, has been demonstrated as a particularly useful technique for early diagnosis, because it detects smaller tumors than the standard modality of mammography. In this paper, we analyse breast thermograms by extracting features describing bilateral symmetries between the two breast areas, and present a classification system for decision making. Clearly, the costs associated with missing a cancer case are much higher than those for mislabelling a benign case. At the same time, datasets contain significantly fewer malignant cases than benign ones. Standard classification approaches fail to consider either of these aspects. In this paper, we introduce a hybrid cost-sensitive classifier ensemble to address this challenging problem. Our approach entails a pool of cost-sensitive decision trees which assign a higher misclassification cost to the malignant class, thereby boosting its recognition rate. A genetic algorithm is employed for simultaneous feature selection and classifier fusion. As an optimisation criterion, we use a combination of misclassification cost and diversity to achieve both a high sensitivity and a heterogeneous ensemble. Furthermore, we prune our ensemble by discarding classifiers that contribute minimally to the decision making. For a challenging dataset of about 150 thermograms, our approach achieves an excellent sensitivity of 83.10%, while maintaining a high specificity of 89.44%. This not only signifies improved recognition of malignant cases, it also statistically outperforms other state-of-the-art algorithms designed for imbalanced classification, and hence provides an effective approach for analysing breast thermograms. Our proposed hybrid cost-sensitive ensemble can facilitate a highly accurate early diagnostic of breast cancer based on thermogram features. It overcomes the difficulties posed by the imbalanced distribution of patients in the two analysed groups. Copyright © 2015 Elsevier B.V. All rights reserved.

Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

NASA Astrophysics Data System (ADS)

Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.

Imbalanced target prediction with pattern discovery on clinical data repositories.

PubMed

Chan, Tak-Ming; Li, Yuxi; Chiau, Choo-Chiap; Zhu, Jane; Jiang, Jie; Huo, Yong

2017-04-20

Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.

Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation

PubMed Central

Fédrigo, Olivier; Haygood, Ralph; Mukherjee, Sayan; Wray, Gregory A.

2009-01-01

Variation in gene expression is an important contributor to phenotypic diversity within and between species. Although this variation often has a genetic component, identification of the genetic variants driving this relationship remains challenging. In particular, measurements of gene expression usually do not reveal whether the genetic basis for any observed variation lies in cis or in trans to the gene, a distinction that has direct relevance to the physical location of the underlying genetic variant, and which may also impact its evolutionary trajectory. Allelic imbalance measurements identify cis-acting genetic effects by assaying the relative contribution of the two alleles of a cis-regulatory region to gene expression within individuals. Identification of patterns that predict commonly imbalanced genes could therefore serve as a useful tool and also shed light on the evolution of cis-regulatory variation itself. Here, we show that sequence motifs, polymorphism levels, and divergence levels around a gene can be used to predict commonly imbalanced genes in a human data set. Reduction of this feature set to four factors revealed that only one factor significantly differentiated between commonly imbalanced and nonimbalanced genes. We demonstrate that these results are consistent between the original data set and a second published data set in humans obtained using different technical and statistical methods. Finally, we show that variation in the single allelic imbalance-associated factor is partially explained by the density of genes in the region of a target gene (allelic imbalance is less probable for genes in gene-dense regions), and, to a lesser extent, the evenness of expression of the gene across tissues and the magnitude of negative selection on putative regulatory regions of the gene. These results suggest that the genomic distribution of functional cis-regulatory variants in the human genome is nonrandom, perhaps due to local differences in evolutionary constraint. PMID:19506001

Multiclass Data Segmentation Using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2014-01-01

interac- tive image segmentation using the solution to a combinatorial Dirichlet problem. Elmoataz et al . have developed general- izations of the graph...Laplacian [25] for image denoising and manifold smoothing. Couprie et al . in [18] define a conve- niently parameterized graph-based energy function that...over to the discrete graph representation. For general data segmentation, Bresson et al . in [8], present rigorous convergence results for two algorithms

Constraint Drive Generation of Vision Algorithms on an Elastic Infrastructure

DTIC Science & Technology

2014-10-01

DIRECTOR: / S / / S / PATRICK K. McCABE MICHAEL J . WESSING Work Unit Manager Deputy Chief...partitioned into training and validation slices and we anno - tate images in descending order from the beginning of the key space. A separate contiguous...and S. Nowozin. On feature combination for multiclass object classification. In ICCV, pages 221–228, 2009. [4] J .-P. Heo, Y. Lee, J . He, S.-F. Chang

37 CFR 2.161 - Requirements for a complete affidavit or declaration of continued use or excusable nonuse.

Code of Federal Regulations, 2010 CFR

2010-07-01

... the registration number; (d)(1) Include the fee required by § 2.6 for each class of goods or services... period under section 8(a)(3) of the Act, include the grace period surcharge per class required by § 2.6; (3) If at least one fee is submitted for a multi-class registration, but the class(es) to which the...

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2014-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...APPROACH Odontocete click detection and classification. A multi-class support vector machine (SVM) classifier was previously developed ( Jarvis ...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, particularly S. Jarvis , is improving the SVM

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2011-09-30

Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR Systems Center Pacific...APPROACH Odontocete click detection and classification. A multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier

Selecting a restoration technique to minimize OCR error.

PubMed

Cannon, M; Fugate, M; Hush, D R; Scovel, C

2003-01-01

This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.

Training the max-margin sequence model with the relaxed slack variables.

PubMed

Niu, Lingfeng; Wu, Jianmin; Shi, Yong

2012-09-01

Sequence models are widely used in many applications such as natural language processing, information extraction and optical character recognition, etc. We propose a new approach to train the max-margin based sequence model by relaxing the slack variables in this paper. With the canonical feature mapping definition, the relaxed problem is solved by training a multiclass Support Vector Machine (SVM). Compared with the state-of-the-art solutions for the sequence learning, the new method has the following advantages: firstly, the sequence training problem is transformed into a multiclassification problem, which is more widely studied and already has quite a few off-the-shelf training packages; secondly, this new approach reduces the complexity of training significantly and achieves comparable prediction performance compared with the existing sequence models; thirdly, when the size of training data is limited, by assigning different slack variables to different microlabel pairs, the new method can use the discriminative information more frugally and produces more reliable model; last but not least, by employing kernels in the intermediate multiclass SVM, nonlinear feature space can be easily explored. Experimental results on the task of named entity recognition, information extraction and handwritten letter recognition with the public datasets illustrate the efficiency and effectiveness of our method. Copyright © 2012 Elsevier Ltd. All rights reserved.

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

PubMed Central

Ankışhan, Haydar; Yılmaz, Derya

2013-01-01

Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS. PMID:24194786

Decoding of top-down cognitive processing for SSVEP-controlled BMI

PubMed Central

Min, Byoung-Kyong; Dähne, Sven; Ahn, Min-Hee; Noh, Yung-Kyun; Müller, Klaus-Robert

2016-01-01

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting. PMID:27808125

Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface.

PubMed

Xu, Ren; Jiang, Ning; Dosen, Strahinja; Lin, Chuang; Mrachacz-Kersting, Natalie; Dremstrup, Kim; Farina, Dario

2016-08-01

In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of  ∼ 80% and  ∼ 70%, and an information transfer rate of  ∼ 7 bits/min and  ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.

Design and analysis of compound flexible skin based on deformable honeycomb

NASA Astrophysics Data System (ADS)

Zou, Tingting; Zhou, Li

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Discrimination of fish populations using parasites: Random Forests on a 'predictable' host-parasite system.

PubMed

Pérez-Del-Olmo, A; Montero, F E; Fernández, M; Barrett, J; Raga, J A; Kostadinova, A

2010-10-01

We address the effect of spatial scale and temporal variation on model generality when forming predictive models for fish assignment using a new data mining approach, Random Forests (RF), to variable biological markers (parasite community data). Models were implemented for a fish host-parasite system sampled along the Mediterranean and Atlantic coasts of Spain and were validated using independent datasets. We considered 2 basic classification problems in evaluating the importance of variations in parasite infracommunities for assignment of individual fish to their populations of origin: multiclass (2-5 population models, using 2 seasonal replicates from each of the populations) and 2-class task (using 4 seasonal replicates from 1 Atlantic and 1 Mediterranean population each). The main results are that (i) RF are well suited for multiclass population assignment using parasite communities in non-migratory fish; (ii) RF provide an efficient means for model cross-validation on the baseline data and this allows sample size limitations in parasite tag studies to be tackled effectively; (iii) the performance of RF is dependent on the complexity and spatial extent/configuration of the problem; and (iv) the development of predictive models is strongly influenced by seasonal change and this stresses the importance of both temporal replication and model validation in parasite tagging studies.

Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

NASA Astrophysics Data System (ADS)

Freiman, M.; Sela, Y.; Edrei, Y.; Pappo, O.; Joskowicz, L.; Abramovitch, R.

2010-03-01

We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the resulting classification model with the leave-one out technique and compared it to both full multi-class SVM and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of more invasive approaches, such as biopsy or contrast-enhanced imaging.

Decoding of top-down cognitive processing for SSVEP-controlled BMI

NASA Astrophysics Data System (ADS)

Min, Byoung-Kyong; Dähne, Sven; Ahn, Min-Hee; Noh, Yung-Kyun; Müller, Klaus-Robert

2016-11-01

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

Experiments on Supervised Learning Algorithms for Text Categorization

NASA Technical Reports Server (NTRS)

Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

2005-01-01

Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

A Status Quo of Segregation: Racial and Economic Imbalance in New Jersey Schools, 1989-2010

ERIC Educational Resources Information Center

Flaxman, Greg

2013-01-01

New Jersey has a curious status regarding school desegregation. It has had the nation's most venerable and strongest state law prohibiting racially segregated schooling and requiring racial balance in the schools whenever feasible. Yet, it simultaneously has had one of the worst records of racially imbalanced schools. Against the legal and…

Too Many Men? Sex Ratios and Women's Partnering Behavior in China

ERIC Educational Resources Information Center

Trent, Katherine; South, Scott J.

2011-01-01

The relative numbers of women and men are changing dramatically in China, but the consequences of these imbalanced sex ratios have received little empirical attention. We merge data from the Chinese Health and Family Life Survey with community-level data from Chinese censuses to examine the relationship between cohort- and community-specific sex…

The Effects of Numerical Imbalance and Gender on Tokens: An Examination of Kanter's Theory.

ERIC Educational Resources Information Center

Fairhurst, Gail Theus; Snavely, Bretta Kay

A study examined the effects of gender on social interaction under numerically imbalanced conditions. Specifically, the study tested R. M. Kanter's assumption that all tokens (individuals who enter a work environment where their gender is numerically scarce) respond in a similar manner to token conditions, although evidence exists that males and…

New Public Management and Research Productivity--A Precarious State of Affairs of Academic Work in the Netherlands

ERIC Educational Resources Information Center

Leišyte, Liudvika

2016-01-01

New Public Management reforms have fostered universities to focus on performance and competition which has resulted in different pressures to perform and disruption of strong teaching-research balance at universities. The imbalanced division of teaching research workloads may be gendered and can strengthen the differences in research productivity…

Promoting Compulsory Education in Rural China: What Are the NPOs Doing?

ERIC Educational Resources Information Center

Zhou, Huiquan

2012-01-01

Due to imbalanced social and economic development, education in poverty-stricken rural areas in China is lagging behind that of urban areas. The current study explores the role of the nonprofit organizations (NPOs) involved in rural compulsory education promotion. Results show that the NPOs are providing a variety of programs to promote rural…

Antimatter

NASA Astrophysics Data System (ADS)

Fraser, Gordon

2002-03-01

1. Science fiction becomes science fact; 2. Mirror worlds; 3. An imbalanced kit of electrical parts; 4. The quantum master; 5. Positive proof; 6. The back passage of time; 7. The quark and the antiquark; 8. Broken mirrors; 9. The cosmic corkscrew; 10. Antiparticle collision course; 11. Setting a trap for antimatter; 12. Glue versus antichemistry; 13. Antimatter in action; 14. Antimatter of the utmost gravity.

Art: A Constituent in Orders of Science

ERIC Educational Resources Information Center

Ukpong, Onoyom Godfrey

2007-01-01

Since the last century, there has been at virtually all levels of formal learning an increasingly imbalanced funding of art and science fields of study in the United States. From the look of things, science has been misappropriated to the extent that its ascendancy has eclipsed art. Art has been named science and the effects of this misnomer are…

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

PubMed

Taguchi, Y-h; Iwadate, Mitsuo; Umeyama, Hideaki

2015-04-30

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

DTIC Science & Technology

2013-05-28

those of the support vector machine and relevance vector machine, and the model runs more quickly than the other algorithms . When one class occurs...incremental support vector machine algorithm for online learning when fewer than 50 data points are available. (a) Papers published in peer-reviewed journals...learning environments, where data processing occurs one observation at a time and the classification algorithm improves over time with new

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

DTIC Science & Technology

2010-10-28

rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of...analyze toxicological mechanisms for two military- unique explosive compounds 2,4,6-trinitrotolune (TNT) and 1,3,5- trinitro-1,3,5-triazacyclohexane...also known as Royal Demolition eXplosive or RDX) [7,8]. These two compounds exhibit distinctive toxicological properties that are accompanied by

Immune Centroids Over-Sampling Method for Multi-Class Classification

DTIC Science & Technology

2015-05-22

recognize to specific antigens . The response of a receptor to an antigen can activate its hosting B-cell. Activated B-cell then proliferates and...modifying N.K. Jerne’s theory. The theory states that in a pre-existing group of lympho- cytes ( specifically B cells), a specific antigen only...the clusters of each small class, which have high data density, called global immune centroids over-sampling (denoted as Global-IC). Specifically

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2013-09-30

N0001411WX21394 Steve W. Martin SPAWAR Systems Center Pacific 53366 Front St. San Diego, CA 92152-6551 phone: (619) 553-9882 email: Steve.W.Martin...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, particularly S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

The Online Identity Formation of the Institution of Higher Education: Analysis of Power Relations and Subject Positions

ERIC Educational Resources Information Center

Massie, Keith R.

2011-01-01

The current study examined over 3000 visual images on the homepages of 234 National University to determine how power relations are depicted. Using a hybrid methodology of grounded theory, critical discursive analysis, and facial prominence scoring, the work culminates in a theory: The (Im)Balanced Theory of College Identity Formation Online. The…

Neuroplasticity and amblyopia: vision at the balance point.

PubMed

Tailor, Vijay K; Schwarzkopf, D Samuel; Dahlmann-Noor, Annegret H

2017-02-01

New insights into triggers and brakes of plasticity in the visual system are being translated into new treatment approaches which may improve outcomes not only in children, but also in adults. Visual experience-driven plasticity is greatest in early childhood, triggered by maturation of inhibitory interneurons which facilitate strengthening of synchronous synaptic connections, and inactivation of others. Normal binocular development leads to progressive refinement of monocular visual acuity, stereoacuity and fusion of images from both eyes. At the end of the 'critical period', structural and functional brakes such as dampening of acetylcholine receptor signalling and formation of perineuronal nets limit further synaptic remodelling. Imbalanced visual input from the two eyes can lead to imbalanced neural processing and permanent visual deficits, the commonest of which is amblyopia. The efficacy of new behavioural, physical and pharmacological interventions aiming to balance visual input and visual processing have been described in humans, and some are currently under evaluation in randomised controlled trials. Outcomes may change amblyopia treatment for children and adults, but the safety of new approaches will need careful monitoring, as permanent adverse events may occur when plasticity is re-induced after the end of the critical period.Video abstracthttp://links.lww.com/CONR/A42.

Hfe Deficiency Impairs Pulmonary Neutrophil Recruitment in Response to Inflammation

PubMed Central

Benesova, Karolina; Vujić Spasić, Maja; Schaefer, Sebastian M.; Stolte, Jens; Baehr-Ivacevic, Tomi; Waldow, Katharina; Zhou, Zhe; Klingmueller, Ursula; Benes, Vladimir; Mall, Marcus A.; Muckenthaler, Martina U.

2012-01-01

Regulation of iron homeostasis and the inflammatory response are tightly linked to protect the host from infection. Here we investigate how imbalanced systemic iron homeostasis in a murine disease model of hereditary hemochromatosis (Hfe−/− mice) affects the inflammatory responses of the lung. We induced acute pulmonary inflammation in Hfe−/− and wild-type mice by intratracheal instillation of 20 µg of lipopolysaccharide (LPS) and analyzed local and systemic inflammatory responses and iron-related parameters. We show that in Hfe−/− mice neutrophil recruitment to the bronchoalveolar space is attenuated compared to wild-type mice although circulating neutrophil numbers in the bloodstream were elevated to similar levels in Hfe−/− and wild-type mice. The underlying molecular mechanisms are likely multifactorial and include elevated systemic iron levels, alveolar macrophage iron deficiency and/or hitherto unexplored functions of Hfe in resident pulmonary cell types. As a consequence, pulmonary cytokine expression is out of balance and neutrophils fail to be recruited efficiently to the bronchoalveolar compartment, a process required to protect the host from infections. In conclusion, our findings suggest a novel role for Hfe and/or imbalanced iron homeostasis in the regulation of the inflammatory response in the lung and hereditary hemochromatosis. PMID:22745741

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

PubMed Central

Galpert, Deborah; del Río, Sara; Herrera, Francisco; Ancede-Gallardo, Evys; Antunes, Agostinho; Agüero-Chapin, Guillermin

2015-01-01

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification. PMID:26605337

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species.

PubMed

Galpert, Deborah; Del Río, Sara; Herrera, Francisco; Ancede-Gallardo, Evys; Antunes, Agostinho; Agüero-Chapin, Guillermin

2015-01-01

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

PubMed

Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan

2003-12-01

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.

Cultivating engineering innovation ability based on optoelectronic experimental platform

NASA Astrophysics Data System (ADS)

Li, Dangjuan; Wu, Shenjiang

2017-08-01

As the supporting experimental platform of the Xi'an Technological University education reform experimental class, "optical technological innovation experimental platform" integrated the design and comprehensive experiments of the optical multi-class courses. On the basis of summing up the past two years teaching experience, platform pilot projects were improve. It has played a good role by making the use of an open teaching model in the cultivating engineering innovation spirit and scientific thinking of the students.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

PubMed

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

PubMed Central

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

A novel framework for change detection in bi-temporal polarimetric SAR images

NASA Astrophysics Data System (ADS)

Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo

2016-10-01

Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.

Mapping online transportation service quality and multiclass classification problem solving priorities

NASA Astrophysics Data System (ADS)

Alamsyah, Andry; Rachmadiansyah, Imam

2018-03-01

Online transportation service is known for its accessibility, transparency, and tariff affordability. These points make online transportation have advantages over the existing conventional transportation service. Online transportation service is an example of disruptive technology that change the relationship between customers and companies. In Indonesia, there are high competition among online transportation provider, hence the companies must maintain and monitor their service level. To understand their position, we apply both sentiment analysis and multiclass classification to understand customer opinions. From negative sentiments, we can identify problems and establish problem-solving priorities. As a case study, we use the most popular online transportation provider in Indonesia: Gojek and Grab. Since many customers are actively give compliment and complain about company’s service level on Twitter, therefore we collect 61,721 tweets in Bahasa during one month observations. We apply Naive Bayes and Support Vector Machine methods to see which model perform best for our data. The result reveal Gojek has better service quality with 19.76% positive and 80.23% negative sentiments than Grab with 9.2% positive and 90.8% negative. The Gojek highest problem-solving priority is regarding application problems, while Grab is about unusable promos. The overall result shows general problems of both case study are related to accessibility dimension which indicate lack of capability to provide good digital access to the end users.

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

Analytical method for fast screening and confirmation of multi-class veterinary drug residues in fish and shrimp by LC-MS/MS.

PubMed

Kim, Junghyun; Suh, Joon Hyuk; Cho, Hyun-Deok; Kang, Wonjae; Choi, Yong Seok; Han, Sang Beom

2016-01-01

A multi-class, multi-residue analytical method based on LC-MS/MS detection was developed for the screening and confirmation of 28 veterinary drug and metabolite residues in flatfish, shrimp and eel. The chosen veterinary drugs are prohibited or unauthorised compounds in Korea, which were categorised into various chemical classes including nitroimidazoles, benzimidazoles, sulfones, quinolones, macrolides, phenothiazines, pyrethroids and others. To achieve fast and simultaneous extraction of various analytes, a simple and generic liquid extraction procedure using EDTA-ammonium acetate buffer and acetonitrile, without further clean-up steps, was applied to sample preparation. The final extracts were analysed by ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). The method was validated for each compound in each matrix at three different concentrations (5, 10 and 20 ng g(-1)) in accordance with Codex guidelines (CAC/GL 71-2009). For most compounds, the recoveries were in the range of 60-110%, and precision, expressed as the relative standard deviation (RSD), was in the range of 5-15%. The detection capabilities (CCβs) were below or equal to 5 ng g(-1), which indicates that the developed method is sufficient to detect illegal fishery products containing the target compounds above the residue limit (10 ng g(-1)) of the new regulatory system (Positive List System - PLS).

Enhanced Dissipation of Triazole and Multiclass Pesticide Residues on Grapes after Foliar Application of Grapevine-Associated Bacillus Species.

PubMed

Salunkhe, Varsha P; Sawant, Indu S; Banerjee, Kaushik; Wadkar, Pallavi N; Sawant, Sanjay D

2015-12-23

Disease management in vineyards with fungicides sometimes results in undesirable residue accumulations in grapes at harvest. Bioaugmentation of the grape fructosphere can be a useful approach for enhancing the degradation rate and reducing the residues to safe levels. This paper reports the in vitro and in vivo biodegradation of three triazole fungicides commonly used in Indian vineyards, by Bacillus strains, namely, DR-39, CS-126, TL-171, and TS-204, which were earlier found to enhance the dissipation rate of profenophos and carbendazim. The strains utilized the triazoles as carbon source and enhanced their in vitro rate of degradation. Myclobutanil, tetraconazole, and flusilazole were applied in separate vineyard plots at field doses of 0.40 g L(-1), 0.75 mL L(-1), and 0.125 mL L(-1), respectively. Residue analysis of field samples from the treated fields reflected 87.38 and >99% degradations of myclobutanil and tetraconazole, respectively, by the strain DR-39, and 90.82% degradation of flusilazole by the strain CS-126 after 15-20 days of treatment. In the respective controls, the corresponding percent degradations were 72.07, 58.88, and 54.28, respectively. These Bacillus strains could also simultaneously degrade the residues of profenofos, carbendazim, and tetraconazole on the grape berries and can be useful in multiclass pesticide residue biodegradation.

Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.

PubMed

Chen, Qingyu; Zobel, Justin; Zhang, Xiuzhen; Verspoor, Karin

2016-01-01

First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and existing automatic systems cannot detect duplicates as precisely as can experts. Supervised learning has the potential to address such problems by building automatic systems that learn from expert curation to detect duplicates precisely and efficiently. While machine learning is a mature approach in other duplicate detection contexts, it has seen only preliminary application in genomic sequence databases. We developed and evaluated a supervised duplicate detection method based on an expert curated dataset of duplicates, containing over one million pairs across five organisms derived from genomic sequence databases. We selected 22 features to represent distinct attributes of the database records, and developed a binary model and a multi-class model. Both models achieve promising performance; under cross-validation, the binary model had over 90% accuracy in each of the five organisms, while the multi-class model maintains high accuracy and is more robust in generalisation. We performed an ablation study to quantify the impact of different sequence record features, finding that features derived from meta-data, sequence identity, and alignment quality impact performance most strongly. The study demonstrates machine learning can be an effective additional tool for de-duplication of genomic sequence databases. All Data are available as described in the supplementary material.

Inter-class sparsity based discriminative least square regression.

PubMed

Wen, Jie; Xu, Yong; Li, Zuoyong; Ma, Zhongli; Xu, Yuanrong

2018-06-01

Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero-one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero-one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multiclass screening method based on solvent extraction and liquid chromatography-tandem mass spectrometry for the determination of antimicrobials and mycotoxins in egg.

PubMed

Capriotti, Anna Laura; Cavaliere, Chiara; Piovesana, Susy; Samperi, Roberto; Laganà, Aldo

2012-12-14

A QuEChERS (Quick Easy Cheap Effective Rugged Safe)-like extraction method was developed for the simultaneous analysis of veterinary drugs and mycotoxins in hen eggs by liquid chromatography-tandem mass spectrometry (LC-MS/MS) with electrospray (ESI) source. Various classes of antimicrobials (tetracyclines, ionophores, coccidiostats, penicillins, cephalosporins, fluoroquinolones, sulfonamides) and mycotoxins (enniatins, beauvericin, ochratoxins, aflatoxins) were considered for the development of this method. Particular attention was devoted to extraction optimization: different solvents (acetone, acetonitrile and methanol), different pH values and different sample to extracting volume ratios were tested and evaluated in terms of recovery, relative standard deviation (RSD) and ESI signal suppression due to matrix effect. Chromatographic and mass spectrometric conditions were optimized to obtain the best instrumental performances for most of the analytes. Quantitative analysis was performed by means of matrix-matched calibration, in a range that varied depending on the analyte and its established maximum limit, when there was one. Recoveries at 100 μg kg(-1) spiking level were >62% (3

[Change in the production of key cytokines for regulation eosinophilic granulocytes in Opisthorchis felineus invasion].

PubMed

Novitskiĭ, V V; Riazantseva, N V; Litvinova, L S; Tkachenko, S B; Kolobovnikova, Iu V; Lepekhin, A V; Chernyshova, N P; Grigor'eva, E S; Suvorova, E V; Zima, A P

2008-02-01

Opisthorchis invasion is accompanied by the imbalanced lymphocytic subpopulational composition manifested itself as induction of the B-link and, on the contrary, depression of T-lymphocytic populations (CD4+, CD8+), with their weaker helper-suppressor association. The immunocompetent cells were ascertained to show a higher production of TH2 cytokines that had an eosinophil-stumulating effect.

China Report, Science & Technology

DTIC Science & Technology

1987-04-30

Distribution of Scientists, Engineers Discussed (CHINA DAILY, various dates) 37 Little Freedom of Movement , by Niu Qiuxia 37 ’Imbalanced...the masses to monitor foreshocks. "A few years ago we had such concoctions as ’indigenous telluric electricity,’ ’indigenous geomagnetism,’ and...skills. It is easier and more feasible than movements of skilled personnel. One might say that it is using "nearby water to slake nearby thirst

Student-Staff Partnerships as Transformational: The "Students as Learners and Teachers" Program as a Case Study in Changing Higher Education

ERIC Educational Resources Information Center

Cook-Sather, Alison

2014-01-01

In this article the author offers an example of a student-staff partnership program based in a higher education context in the United States. This program positions undergraduate students as pedagogical consultants to academic staff. The goal of the program is to counter traditional hierarchies and imbalanced power relations and foster a shift in…

THE CASE AGAINST DE FACTO SEGREGATION EDUCATION IN THE NORTH AND WEST--A CONTEMPORARY CASE STUDY.

ERIC Educational Resources Information Center

TILLMAN, JAMES A., JR.

IT IS THE RESPONSIBILITY OF THE LOCAL GOVERNMENT TO PROVIDE EDUCATION WHICH ENABLES CHILDREN TO ADJUST NORMALLY TO AMERICAN DEMOCRACY. THIS CANNOT BE DONE IN A SCHOOL SYSTEM THAT IS RACIALLY IMBALANCED. THE MINNEAPOLIS HUMAN RELATIONS TASK FORCE FEELS THAT THE BOARD OF EDUCATION MUST NOT WAIT FOR A SOUND COMMUNITY BUT MUST TAKE POSITIVE STEPS TO…

The Six-District Plan. Integration of the Springfield, Mass., Elementary Schools. A Report of the Massachusetts Advisory Committee to the United States Commission on Civil Rights.

ERIC Educational Resources Information Center

Chin, Laura, Ed.

This report reviews the first year of integration, under the implementation of the Six-District Plan, of the elementary schools in Springfield, Massachusetts. Through this plan the school department changed the racial composition in five previously imbalanced elementary schools and integrated the elementary school system. Redistricting, the…

Strongly Interacting Fermi Gases In Two Dimensions

DTIC Science & Technology

2012-01-03

Correlated Quantum Fluids: From Ultracold Quantum Gases to QCD Plasmas. Figure 2 Spin Transport in Spin-Imbalanced, strongly interacting...atoms becomes confined to a stack of two-dimensional layers formed by a one-dimensional optical lattice . Decreasing the dimensionality leads to the...opening of a gap in radiofrequency spectra, even on the BCS-side of a Feshbach resonance. With increasing lattice depth, the measured binding energy

Gene expression profiling of flax (Linum usitatissimum L.) under edaphic stress.

PubMed

Dmitriev, Alexey A; Kudryavtseva, Anna V; Krasnov, George S; Koroban, Nadezhda V; Speranskaya, Anna S; Krinitsina, Anastasia A; Belenikin, Maxim S; Snezhkina, Anastasiya V; Sadritdinova, Asiya F; Kishlyan, Natalya V; Rozhmina, Tatiana A; Yurkevich, Olga Yu; Muravenko, Olga V; Bolsheva, Nadezhda L; Melnikova, Nataliya V

2016-11-16

Cultivated flax (Linum usitatissimum L.) is widely used for production of textile, food, chemical and pharmaceutical products. However, various stresses decrease flax production. Search for genes, which are involved in stress response, is necessary for breeding of adaptive cultivars. Imbalanced concentration of nutrient elements in soil decrease flax yields and also results in heritable changes in some flax lines. The appearance of Linum Insertion Sequence 1 (LIS-1) is the most studied modification. However, LIS-1 function is still unclear. High-throughput sequencing of transcriptome of flax plants grown under normal (N), phosphate deficient (P), and nutrient excess (NPK) conditions was carried out using Illumina platform. The assembly of transcriptome was performed, and a total of 34924, 33797, and 33698 unique transcripts for N, P, and NPK sequencing libraries were identified, respectively. We have not revealed any LIS-1 derived mRNA in our sequencing data. The analysis of high-throughput sequencing data allowed us to identify genes with potentially differential expression under imbalanced nutrition. For further investigation with qPCR, 15 genes were chosen and their expression levels were evaluated in the extended sampling of 31 flax plants. Significant expression alterations were revealed for genes encoding WRKY and JAZ protein families under P and NPK conditions. Moreover, the alterations of WRKY family genes differed depending on LIS-1 presence in flax plant genome. Besides, we revealed slight and LIS-1 independent mRNA level changes of KRP2 and ING1 genes, which are adjacent to LIS-1, under nutrition stress. Differentially expressed genes were identified in flax plants, which were grown under phosphate deficiency and excess nutrition, on the basis of high-throughput sequencing and qPCR data. We showed that WRKY and JAS gene families participate in flax response to imbalanced nutrient content in soil. Besides, we have not identified any mRNA, which could be derived from LIS-1, in our transcriptome sequencing data. Expression of LIS-1 flanking genes, ING1 and KRP2, was suggested not to be nutrient stress-induced. Obtained results provide new insights into edaphic stress response in flax and the role of LIS-1 in these process.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

"When 'Bad' is 'Good'": Identifying Personal Communication and Sentiment in Drug-Related Tweets.

PubMed

Daniulaityte, Raminta; Chen, Lu; Lamy, Francois R; Carlson, Robert G; Thirunarayan, Krishnaprasad; Sheth, Amit

2016-10-24

To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant. In multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045). The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid-related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions.

Fast Multiclass Segmentation using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2013-02-01

000 28 × 28 images of handwritten digits 0 through 9. Examples of entries can be found in Figure 6. The task is to classify each of the images into the...database of handwritten digits .” [Online]. Available: http://yann.lecun.com/exdb/mnist/ [36] J. Lellmann, J. H. Kappes, J. Yuan, F. Becker, and C...corresponding digit . The images include digits from 0 to 9; thus, this is a 10 class segmentation problem. To construct the weight matrix, we used N

Specifications of a Simulation Model for a Local Area Network Design in Support of Stock Point Logistics Integrated Communications Environment (SPLICE).

DTIC Science & Technology

1982-10-01

class queueing system with a preemptive -resume priority service discipline, as depicted in Figure 4.2. Concerning a SPLICLAN configuration a node can...processor can be modeled as a single resource, multi-class queueing system with a preemptive -resume priority structure as the one given in Figure 4.2. An...LOCAL AREA NETWORK DESIGN IN SUPPORT OF STOCK POINT LOGISTICS INTEGRATED COMMUNICATIONS ENVIRONMENT (SPLICE) by Ioannis Th. Mastrocostopoulos October

Towards a smart glove: arousal recognition based on textile Electrodermal Response.

PubMed

Valenza, Gaetano; Lanata, Antonio; Scilingo, Enzo Pasquale; De Rossi, Danilo

2010-01-01

This paper investigates the possibility of using Electrodermal Response, acquired by a sensing fabric glove with embedded textile electrodes, as reliable means for emotion recognition. Here, all the essential steps for an automatic recognition system are described, from the recording of physiological data set to a feature-based multiclass classification. Data were collected from 35 healthy volunteers during arousal elicitation by means of International Affective Picture System (IAPS) pictures. Experimental results show high discrimination after twenty steps of cross validation.

Symbology Sourcebook for Military Applications

DTIC Science & Technology

1986-04-01

automated vs ,°- ,. -° -. "’ao CA:I- a- 0d 926. - 3 1 - 0~ u 4A ’I6 catalog of military symbols (TACSYM). The development of TACSYM induced a second...Airborne 124 DemolItion 37 ADP Central 61 Antiaircraft 125 Fence 36 Elec. Navig. Aid Antitank 126 Data Processing Unit 39 Microphones 83 Armour 127...130 Medical 43 Animal 87 FA 131 Hospital 44 Armoured 88 Construction 132 Medical Supply 18 SYMBOL CONCEPTS 133 Mines 134 Missile Supply 135 Multi-Class

The decision tree approach to classification

NASA Technical Reports Server (NTRS)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Design and Evaluation of a Real-Time Video Conferencing Environment for Support Teaching: An Attempt to Promote Equality of K-12 Education in China

ERIC Educational Resources Information Center

Xiong, Caiping; Ge, Jun; Wang, Qiyun; Wang, Xuejun

2017-01-01

Imbalanced allocation of teacher resources is a major factor causing inequality in K-12 education in China. Traditional ways to solve this problem, such as on-site support teaching and recorded lectures of quality teachers, have posed many defects. It is suggested that information and communication technology should be employed to deal with the…

Influence of cross-phase modulation in SPM-based nonlinear optical loop mirror

NASA Astrophysics Data System (ADS)

Pitois, Stéphane

2005-09-01

We study the role of cross-phase modulation (CPM) occurring between the two counter-propagating parts of a signal wave in a standard SPM-based nonlinear optical fiber loop mirror (NOLM). For pulse train with high duty-cycle, we experimentally observe the influence of cross-phase modulation on NOLM transmittivity. Finally, we propose a solution based on properly designed dispersion imbalanced NOLM to overcome undesirable CPM effects.

Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach

NASA Astrophysics Data System (ADS)

Du, Shihong; Zhang, Fangli; Zhang, Xiuyuan

2015-07-01

While most existing studies have focused on extracting geometric information on buildings, only a few have concentrated on semantic information. The lack of semantic information cannot satisfy many demands on resolving environmental and social issues. This study presents an approach to semantically classify buildings into much finer categories than those of existing studies by learning random forest (RF) classifier from a large number of imbalanced samples with high-dimensional features. First, a two-level segmentation mechanism combining GIS and VHR image produces single image objects at a large scale and intra-object components at a small scale. Second, a semi-supervised method chooses a large number of unbiased samples by considering the spatial proximity and intra-cluster similarity of buildings. Third, two important improvements in RF classifier are made: a voting-distribution ranked rule for reducing the influences of imbalanced samples on classification accuracy and a feature importance measurement for evaluating each feature's contribution to the recognition of each category. Fourth, the semantic classification of urban buildings is practically conducted in Beijing city, and the results demonstrate that the proposed approach is effective and accurate. The seven categories used in the study are finer than those in existing work and more helpful to studying many environmental and social problems.

Range wise busy checking 2-way imbalanced algorithm for cloudlet allocation in cloud environment

NASA Astrophysics Data System (ADS)

Alanzy, Mohammed; Latip, Rohaya; Muhammed, Abdullah

2018-05-01

Cloud computing considers as a new business paradigm and a popular platform over the last few years. Many organizations, agencies, and departments consider responsible tasks time and tasks needed to be accomplished as soon as possible. These agencies counter IT issues due to the massive arise of data, applications, and solution scopes. Currently, the main issue related with the cloud is the way of making the environment of the cloud computing more qualified, and this way needs a competent allocation strategy of the cloudlet, Thus, there are huge number of studies conducted with regards to this matter that sought to assign the cloudlets to VMs or resources by variety of strategies. In this paper we have proposed range wise busy checking 2-way imbalanced Algorithm in cloud computing. Compare to other methods, it decreases the completion time to finish tasks’ execution, it is considered the fundamental part to enhance the system performance such as the makespan. This algorithm was simulated using Cloudsim to give more opportunity to the higher VM speed to accommodate more Cloudlets in its local queue without considering the threshold balance condition. The simulation result shows that the average makespan time is lesser compare to the previous cloudlet allocation strategy.

Effect of playing tactics on achieving score-box possessions in a random series of team possessions from Norwegian professional soccer matches.

PubMed

Tenga, Albin; Holme, Ingar; Ronglan, Lars Tore; Bahr, Roald

2010-02-01

Methods of analysis that include an assessment of opponent interactions are thought to provide a more valid means of team match performance. The purpose of this study was to examine the effect of playing tactics on achieving score-box possession by assessing opponent interactions in Norwegian elite soccer matches. We analysed a random series of 1703 team possessions from 163 of 182 (90%) matches played in the professional men's league during the 2004 season. Multidimensional qualitative data obtained from ten ordered categorical variables were used. Offensive tactics were more effective in producing score-box possessions when playing against an imbalanced defence (28.5%) than against a balanced defence (6.5%) (P < 0.001). Multiple logistic regression found that, for the main variable "team possession type", counterattacks were more effective than elaborate attacks when playing against an imbalanced defence (odds ratio: 2.69; 95% confidence interval: 1.64 to 4.43) but not against a balanced defence (odds ratio: 1.14; 95% confidence interval: 0.47 to 2.76). Assessment of opponent interactions is critical to evaluate the effectiveness of offensive playing tactics on producing score-box possessions, and improves the validity of team match-performance analysis in soccer.

A correlation between macronutrient balancing and insect host-plant range: evidence from the specialist caterpillar Spodoptera exempta (Walker).

PubMed

Lee, Kwang Pum; Raubenheimer, David; Behmer, Spencer T; Simpson, Stephen J

2003-12-01

In an earlier study, we showed that the ingestive responses of the generalist caterpillar Spodoptera littoralis to foods imbalanced in their protein:carbohydrate content is similar to generalist locusts, but differs from that of specialist-feeding locusts. Here we further pursued the comparison by repeating the experiments using a closely related specialist caterpillar, Spodoptera exempta. First, caterpillars were allowed to self-compose a diet of preferred protein:carbohydrate balance by mixing between nutritionally complementary foods. Then, they were confined to one of five imbalanced foods, in which we measured the trade-off between over- and under-ingesting the two nutrients. On complementary foods, the caterpillars actively regulated their protein and carbohydrate intake. In the no-choice experiment, those fed excess-protein foods ingested small surpluses of protein compared with generalist feeders, thus showing a pattern of nutrient balancing similar to that observed in specialist locusts. Utilisation data indicated that ingested excesses and deficits were to some extent offset by differential utilisation. Evidence also showed that post-ingestive responses of the specialist S. exempta were less flexible than those observed in the generalist S. littoralis, a pattern which is again in accordance with comparisons of acridids differing in their host-plant range.

Unbiased Taxonomic Annotation of Metagenomic Samples

PubMed Central

Fosso, Bruno; Pesole, Graziano; Rosselló, Francesc

2018-01-01

Abstract The classification of reads from a metagenomic sample using a reference taxonomy is usually based on first mapping the reads to the reference sequences and then classifying each read at a node under the lowest common ancestor of the candidate sequences in the reference taxonomy with the least classification error. However, this taxonomic annotation can be biased by an imbalanced taxonomy and also by the presence of multiple nodes in the taxonomy with the least classification error for a given read. In this article, we show that the Rand index is a better indicator of classification error than the often used area under the receiver operating characteristic (ROC) curve and F-measure for both balanced and imbalanced reference taxonomies, and we also address the second source of bias by reducing the taxonomic annotation problem for a whole metagenomic sample to a set cover problem, for which a logarithmic approximation can be obtained in linear time and an exact solution can be obtained by integer linear programming. Experimental results with a proof-of-concept implementation of the set cover approach to taxonomic annotation in a next release of the TANGO software show that the set cover approach further reduces ambiguity in the taxonomic annotation obtained with TANGO without distorting the relative abundance profile of the metagenomic sample. PMID:29028181

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

PubMed

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

2017-07-24

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

Modeling the response of a monopulse radar to impulsive jamming signals using the Block Oriented System Simulator (BOSS)

NASA Astrophysics Data System (ADS)

Long, Jeffrey K.

1989-09-01

This theses developed computer models of two types of amplitude comparison monopulse processors using the Block Oriented System Simulation (BOSS) software package and to determine the response to these models to impulsive input signals. This study was an effort to determine the susceptibility of monopulse tracking radars to impulsing jamming signals. Two types of amplitude comparison monopulse receivers were modeled, one using logarithmic amplifiers and the other using automatic gain control for signal normalization. Simulations of both types of systems were run under various conditions of gain or frequency imbalance between the two receiver channels. The resulting errors from the imbalanced simulations were compared to the outputs of similar, baseline simulations which had no electrical imbalances. The accuracy of both types of processors was directly affected by gain or frequency imbalances in their receiver channels. In most cases, it was possible to generate both positive and negative angular errors, dependent upon the type and degree of mismatch between the channels. The system most susceptible to induced errors was a frequency imbalanced processor which used AGC circuitry. Any errors introduced will be a function of the degree of mismatch between the channels and therefore would be difficult to exploit reliably.

Discriminative Relational Topic Models.

PubMed

Chen, Ning; Zhu, Jun; Xia, Fei; Zhang, Bo

2015-05-01

Relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents for document networks, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving prediction performance.

Endogenous cortisol levels are associated with an imbalanced striatal sensitivity to monetary versus non-monetary cues in pathological gamblers

PubMed Central

Li, Yansong; Sescousse, Guillaume; Dreher, Jean-Claude

2014-01-01

Pathological gambling is a behavioral addiction characterized by a chronic failure to resist the urge to gamble. It shares many similarities with drug addiction. Glucocorticoid hormones including cortisol are thought to play a key role in the vulnerability to addictive behaviors, by acting on the mesolimbic reward pathway. Based on our previous report of an imbalanced sensitivity to monetary versus non-monetary incentives in the ventral striatum of pathological gamblers (PGs), we investigated whether this imbalance was mediated by individual differences in endogenous cortisol levels. We used functional magnetic resonance imaging (fMRI) and examined the relationship between cortisol levels and the neural responses to monetary versus non-monetary cues, while PGs and healthy controls were engaged in an incentive delay task manipulating both monetary and erotic rewards. We found a positive correlation between cortisol levels and ventral striatal responses to monetary versus erotic cues in PGs, but not in healthy controls. This indicates that the ventral striatum is a key region where cortisol modulates incentive motivation for gambling versus non-gambling related stimuli in PGs. Our results extend the proposed role of glucocorticoid hormones in drug addiction to behavioral addiction, and help understand the impact of cortisol on reward incentive processing in PGs. PMID:24723862

Fatal attraction in glycolysis: how Saccharomyces cerevisiae manages sudden transitions to high glucose

PubMed Central

Heerden, Johan H. v.; Wortel, Meike T.; Bruggeman, Frank J.; Heijnen, Joseph J.; Bollen, Yves J.; Planqué, Robert; Hulshof, Josephus; O’Toole, Tom G.; Wahl, S. A.; Teusink, Bas

2014-01-01

In the model eukaryote Saccharomyces cerevisiae, it has long been known that a functional trehalose pathway is indispensable for transitions to high glucose conditions. Upon addition of glucose, cells with a defect in trehalose 6-phosphate synthase (Tps1), the first committed step in the trehalose pathway, display what we have termed an imbalanced glycolytic state; in this state the flux through the upper part of glycolysis outpaces that through the lower part of glycolysis. As a consequence, the intermediate fructose 1,6-bisphosphate (FBP) accumulates at low concentrations of ATP and inorganic phosphate (Pi). Despite significant research efforts, a satisfactory understanding of the regulatory role that trehalose metabolism plays during such transitions has remained infamously unresolved. In a recent study, we demonstrate that the startup of glycolysis exhibits two dynamic fates: a proper, functional, steady state or the imbalanced state described above. Both states are stable, attracting states, and the probability distribution of initial states determines the fate of a yeast cell exposed to glucose. Trehalose metabolism steers the dynamics of glycolysis towards the proper functional state through its ATP hydrolysis activity; a mechanism that ensures that the demand and supply of ATP is balanced with Pi availability under dynamic conditions. [van Heerden et al. Science (2014), DOI: 10.1126/science.1245114.] PMID:28357229

Collateral consequences: implications of male incarceration rates, imbalanced sex ratios and partner availability for heterosexual Black women.

PubMed

Dauria, Emily F; Oakley, Lisa; Arriola, Kimberly Jacob; Elifson, Kirk; Wingood, Gina; Cooper, Hannah L F

2015-01-01

While studies have found correlations between rates of incarceration and sexually transmitted infections (STIs), few studies have explored the mechanisms linking these phenomena. This qualitative study examines how male incarceration rates and sex ratios influence perceived partner availability and sexual partnerships for heterosexual Black women. Semi-structured interviews were conducted with 33 Black women living in two US neighbourhoods, one with a high male incarceration rate and an imbalanced sex ratio (referred to as 'Allentown') and one with a low male incarceration rate and an equitable sex ratio (referred to as 'Blackrock'). Data were analysed using grounded theory. In Allentown, male incarceration reduced the number of available men, and participants largely viewed men available for partnerships as being of an undesirable quality. The number and desirability of men impacted on the nature of partnerships such that they were shorter, focused on sexual activity and may be with higher-risk sexual partners (e.g. transactional sex partners). In Blackrock, marriage rates contributed to the shortage of desirable male partners. By highlighting the role that the quantity and quality of male partners has on shaping sexual partnerships, this study advances current understandings of how incarceration and sex ratios shape HIV- and STI-related risk.

Robust BMPM training based on second-order cone programming and its application in medical diagnosis.

PubMed

Peng, Xiang; King, Irwin

2008-01-01

The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. It provides a worst-case bound on the probability of misclassification of future data points based on reliable estimates of means and covariance matrices of the classes from the training data samples, and achieves promising performance. In this paper, we develop a novel yet critical extension training algorithm for BMPM that is based on Second-Order Cone Programming (SOCP). Moreover, we apply the biased classification model to medical diagnosis problems to demonstrate its usefulness. By removing some crucial assumptions in the original solution to this model, we make the new method more accurate and robust. We outline the theoretical derivatives of the biased classification model, and reformulate it into an SOCP problem which could be efficiently solved with global optima guarantee. We evaluate our proposed SOCP-based BMPM (BMPMSOCP) scheme in comparison with traditional solutions on medical diagnosis tasks where the objectives are to focus on improving the sensitivity (the accuracy of the more important class, say "ill" samples) instead of the overall accuracy of the classification. Empirical results have shown that our method is more effective and robust to handle imbalanced classification problems than traditional classification approaches, and the original Fractional Programming-based BMPM (BMPMFP).

Homeostatic regulation of protein intake: in search of a mechanism

PubMed Central

Reed, Scott D.; Henagan, Tara M.

2012-01-01

Free-living organisms must procure adequate nutrition by negotiating an environment in which both the quality and quantity of food vary markedly. Recent decades have seen marked progress in our understanding of neural regulation of feeding behavior. However, this progress has occurred largely in the context of energy intake, despite the fact that food intake is influenced by more than just the energy content of the diet. A large number of behavioral studies indicate that both the quantity and quality of dietary protein can markedly influence food intake. High-protein diets tend to reduce intake, low-protein diets tend to increase intake, and rodent models seem to self-select between diets in order to meet protein requirements and avoid diets that are imbalanced in amino acids. Recent work suggests that the amino acid leucine regulates food intake by altering mTOR and AMPK signaling in the hypothalamus, while activation of GCN2 within the anterior piriform cortex contributes to the detection and avoidance of amino acid-imbalanced diets. This review focuses on the role that these and other signaling systems may play in mediating the homeostatic regulation of protein balance, and in doing so, highlights our lack of knowledge regarding the physiological and neurobiological mechanisms that might underpin such a regulatory phenomenon. PMID:22319049

Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2015-12-01

Objective. The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach. Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results. VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance. These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.

Solid-phase extraction of multi-class pharmaceuticals from environmental water samples onto modified multi-walled carbon nanotubes followed by LC-MS/MS.

PubMed

Lalović, Bojana; Đurkić, Tatjana; Vukčević, Marija; Janković-Častvan, Ivona; Kalijadis, Ana; Laušević, Zoran; Laušević, Mila

2017-09-01

In this paper, pristine and chemically treated multi-walled carbon nanotubes (MWCNTs) were employed as solid-phase extraction sorbents for the isolation and enrichment of multi-class pharmaceuticals from the surface water and groundwater, prior to liquid chromatography-tandem mass spectrometry analysis. Thirteen pharmaceuticals that belong to different therapeutical classes (erythromycin, azithromycin, sulfamethoxazole, diazepam, lorazepam, carbamazepine, metoprolol, bisoprolol, enalapril, cilazapril, simvastatin, clopidogrel, diclofenac) and two metabolites of metamizole (4-acetylaminoantipyrine and 4-formylaminoantipyrine) were selected for this study. The influence of chemical treatment on MWCNT surface characteristics and extraction efficiency was studied, and it was shown that HCl treatment of MWCNT leads to a decrease in the amount of surface oxygen groups and at the same time favorably affects the efficiency toward extraction of selected pharmaceuticals. After the optimization of the SPE procedure, the following conditions were chosen: 50 mg of HCl-treated MCWNT as a sorbent, 100 mL of water sample at pH 6, and 15 mL of the methanol-dichloromethane mixture (1:1, v/v) as eluent. Under optimal conditions, high recoveries (79-119%), as well as low detection (0.2 to 103 ng L -1 ) and quantitation (0.5-345 ng L -1 ) limits, were obtained. The optimized method was applied to the analysis of five surface water and two groundwater samples, and three pharmaceuticals were detected, the antiepileptic drug carbamazepine and two metabolites of antipyretic metamizole.

Behavior of Multiclass Pesticide Residue Concentrations during the Transformation from Rose Petals to Rose Absolute.

PubMed

Tascone, Oriane; Fillâtre, Yoann; Roy, Céline; Meierhenrich, Uwe J

2015-05-27

This study investigates the concentrations of 54 multiclass pesticides during the transformation processes from rose petal to concrete and absolute using roses spiked with pesticides as a model. The concentrations of the pesticides were followed during the process of transforming the spiked rose flowers from an organic field into concrete and then into absolute. The rose flowers, the concrete, and the absolute, as well as their transformation intermediates, were analyzed for pesticide content using gas chromatography/tandem mass spectrometry. We observed that all the pesticides were extracted and concentrated in the absolute, with the exception of three molecules: fenthion, fenamiphos, and phorate. Typical pesticides were found to be concentrated by a factor of 100-300 from the rose flowers to the rose absolute. The observed effect of pesticide enrichment was also studied in roses and their extracts from four classically phytosanitary treated fields. Seventeen pesticides were detected in at least one of the extracts. Like the case for the spiked samples in our model, the pesticides present in the rose flowers from Turkey were concentrated in the absolute. Two pesticides, methidathion and chlorpyrifos, were quantified in the rose flowers at approximately 0.01 and 0.01-0.05 mg kg(-1), respectively, depending on the treated field. The concentrations determined for the corresponding rose absolutes were 4.7 mg kg(-1) for methidathion and 0.65-27.25 mg kg(-1) for chlorpyrifos.

A Simple and Fast Extraction Method for the Determination of Multiclass Antibiotics in Eggs Using LC-MS/MS.

PubMed

Wang, Kun; Lin, Kunde; Huang, Xinwen; Chen, Meng

2017-06-21

The purpose of this study was to develop and validate a simple, fast, and specific extraction method for the analysis of 64 antibiotics from nine classes (including sulfonamides, quinolones, tetracyclines, macrolides, lincosamide, nitrofurans, β-lactams, nitromidazoles, and cloramphenicols) in chicken eggs. Briefly, egg samples were simply extracted with a mixture of acetonitrile-water (90:10, v/v) and 0.1 mol·L -1 Na 2 EDTA solution assisted with ultrasonic. The extract was centrifuged, condensed, and directly analyzed on a liquid chromatography coupled to tandem mass spectrometry. Compared with conventional cleanup methods (passing through solid phase extract cartridges), the established method demonstrated comparable efficiencies in eliminating matrix effects and higher or equivalent recoveries for most of the target compounds. Typical validation parameters including specificity, linearity, matrix effect, limits of detection (LODs) and quantification (LOQs), the decision limit, detection capability, trueness, and precision were evaluated. The recoveries of target compounds ranged from 70.8% to 116.1% at three spiking levels (5, 20, and 50 μg·kg -1 ), with relative standard deviations less than 14%. LODs and LOQs were in the ranges of 0.005-2.00 μg·kg -1 and 0.015-6.00 μg·kg -1 for all of the antibiotics, respectively. A total of five antibiotics were successfully detected in 22 commercial eggs from local markets. This work suggests that the method is suitable for the analysis of multiclass antibiotics in eggs.

A simple, fast and cheap non-SPE screening method for antibacterial residue analysis in milk and liver using liquid chromatography-tandem mass spectrometry.

PubMed

Martins, Magda Targa; Melo, Jéssica; Barreto, Fabiano; Hoff, Rodrigo Barcellos; Jank, Louise; Bittencourt, Michele Soares; Arsand, Juliana Bazzan; Schapoval, Elfrides Eva Scherman

2014-11-01

In routine laboratory work, screening methods for multiclass analysis can process a large number of samples in a short time. The main challenge is to develop a methodology to detect as many different classes of residues as possible, combined with speed and low cost. An efficient technique for the analysis of multiclass antibacterial residues (fluoroquinolones, tetracyclines, sulfonamides and trimethoprim) was developed based on simple, environment-friendly extraction for bovine milk, cattle and poultry liver. Acidified ethanol was used as an extracting solvent for milk samples. Liver samples were treated using EDTA-washed sand for cell disruption, methanol:water and acidified acetonitrile as extracting solvent. A total of 24 antibacterial residues were detected and confirmed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), at levels between 10, 25 and 50% of the maximum residue limit (MRL). For liver samples a metabolite (sulfaquinoxaline-OH) was also monitored. A validation procedure was conducted for screening purposes in accordance with European Union requirements (2002/657/EC). The detection capability (CCβ) false compliant rate was less than 5% at the lowest level for each residue. Specificity and ruggedness were also discussed. Incurred and routine samples were analyzed and the method was successfully applied. The results proved that this method can be an important tool in routine analysis, since it is very fast and reliable. Copyright © 2014. Published by Elsevier B.V.

Machine vision system for measuring conifer seedling morphology

NASA Astrophysics Data System (ADS)

Rigney, Michael P.; Kranzler, Glenn A.

1995-01-01

A PC-based machine vision system providing rapid measurement of bare-root tree seedling morphological features has been designed. The system uses backlighting and a 2048-pixel line- scan camera to acquire images with transverse resolutions as high as 0.05 mm for precise measurement of stem diameter. Individual seedlings are manually loaded on a conveyor belt and inspected by the vision system in less than 0.25 seconds. Designed for quality control and morphological data acquisition by nursery personnel, the system provides a user-friendly, menu-driven graphical interface. The system automatically locates the seedling root collar and measures stem diameter, shoot height, sturdiness ratio, root mass length, projected shoot and root area, shoot-root area ratio, and percent fine roots. Sample statistics are computed for each measured feature. Measurements for each seedling may be stored for later analysis. Feature measurements may be compared with multi-class quality criteria to determine sample quality or to perform multi-class sorting. Statistical summary and classification reports may be printed to facilitate the communication of quality concerns with grading personnel. Tests were conducted at a commercial forest nursery to evaluate measurement precision. Four quality control personnel measured root collar diameter, stem height, and root mass length on each of 200 conifer seedlings. The same seedlings were inspected four times by the machine vision system. Machine stem diameter measurement precision was four times greater than that of manual measurements. Machine and manual measurements had comparable precision for shoot height and root mass length.

Emotion recognition based on physiological changes in music listening.

PubMed

Kim, Jonghwa; André, Elisabeth

2008-12-01

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.

A Novel Wearable Device for Food Intake and Physical Activity Recognition

PubMed Central

Farooq, Muhammad; Sazonov, Edward

2016-01-01

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure. PMID:27409622

A Novel Wearable Device for Food Intake and Physical Activity Recognition.

PubMed

Farooq, Muhammad; Sazonov, Edward

2016-07-11

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

[Correction of schoolchildren's diets with biologically active additives].

PubMed

Diudiakov, A A; Rakhmanov, R S; Korotunov, Iu V; Gruzdeva, A E

2002-01-01

The actual nutrition of schoolchildren in the Nizni-Novgorod district is imbalanced due to the deficiency of protein and vitamins and to the high contents of fats and carbohydrates. The authors provide evidence for a combined preparation to correct the children's diets, which incorporates animal protein, biologically active plant additives, and egg-shell calcium. The use of the preparation in combination with liquid bifidumbacterin contributes to increases in morphofunctional parameters in adolescents.

Managing Quality and Productivity in Aerospace and Defense

DTIC Science & Technology

1989-11-01

subject of The VPC at Virginia Tech. The Maryland excellence. We hope they will be of value to Center for Productivity and Quality of Worklife your...Budget drives the plan • Plan drives the budget I nvolves only top management * Participation at all levels * Finance and operations imbalanced * Balance ...to create a better tend to cause us to drive the equation left-to- balance . The Planning Process discussed in right, focusing on the levels of

Quantum Phases of Matter in Optical Lattices

DTIC Science & Technology

2015-06-30

doi: 10.1103/PhysRevA.89.013625 Hyungwon Kim, David A. Huse. Ballistic Spreading of Entanglement in a Diffusive Nonintegrable System, Physical...Review B, (07 2013): 0. doi: 10.1103/PhysRevB.88.014206 Lin Dong, Lei Jiang, Han Pu. Fulde–Ferrell pairing instability in spin–orbit coupled Fermi...PhysRevA.87.051603 Kuei Sun, C. J. Bolech. Pair tunneling, phase separation, and dimensional crossover in imbalanced fermionic superfluids in a coupled

Walking pattern analysis and SVM classification based on simulated gaits.

PubMed

Mao, Yuxiang; Saito, Masaru; Kanno, Takehiro; Wei, Daming; Muroi, Hiroyasu

2008-01-01

Three classes of walking patterns, normal, caution and danger, were simulated by tying elastic bands to joints of lower body. In order to distinguish one class from another, four local motions suggested by doctors were investigated stepwise, and differences between levels were evaluated using t-tests. The human adaptability in the tests was also evaluated. We improved average classification accuracy to 84.50% using multiclass support vector machine classifier and concluded that human adaptability is a factor that can cause obvious bias in contiguous data collections.

Multiclass Bayes error estimation by a feature space sampling technique

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.

1979-01-01

A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Prediction of body mass index status from voice signals based on machine learning for automated medical applications.

PubMed

Lee, Bum Ju; Kim, Keun Ho; Ku, Boncho; Jang, Jun-Su; Kim, Jong Yeol

2013-05-01

The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. Our results could support the development of BMI diagnosis tools for real-time monitoring; such tools are considered helpful in improving automated BMI status diagnosis in remote healthcare or telemedicine and are expected to have applications in forensic and medical science. Copyright © 2013 Elsevier B.V. All rights reserved.

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

PubMed

Richardson, Alice M; Lidbury, Brett A

2017-08-14

Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Random forests (RFs) for predictor variable selection, and data reshaping to overcome a large imbalance of negative to positive test results in relation to HBV and HCV immunoassay results, are examined. The methodology is illustrated using data from ACT Pathology (Canberra, Australia), consisting of laboratory test records from 18,625 individuals who underwent hepatitis virus testing over the decade from 1997 to 2007. Overall, the prediction of HCV test results by immunoassay was more accurate than for HBV immunoassay results associated with identical routine pathology predictor variable data. HBV and HCV negative results were vastly in excess of positive results, so three approaches to handling the negative/positive data imbalance were compared. Generating datasets by the Synthetic Minority Oversampling Technique (SMOTE) resulted in significantly more accurate prediction than single downsizing or multiple downsizing (MDS) of the dataset. For downsized data sets, applying a RF for predictor variable selection had a small effect on the performance, which varied depending on the virus. For SMOTE, a RF had a negative effect on performance. An analysis of variance of the performance across settings supports these findings. Finally, age and assay results for alanine aminotransferase (ALT), sodium for HBV and urea for HCV were found to have a significant impact upon laboratory diagnosis of HBV or HCV infection using an optimised SVM model. Laboratories looking to include machine learning via SVM as part of their decision support need to be aware that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied. This awareness should lead to careful use of existing machine learning methods, thus improving the quality of laboratory diagnosis.

Monsoon Forecasting based on Imbalanced Classification Techniques

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Troncoso, Alicia; Asencio-Cortes, Gualberto; Vega, Inmaculada; Gallego, David

2017-04-01

Monsoonal systems are quasiperiodic processes of the climatic system that control seasonal precipitation over different regions of the world. The Western North Pacific Summer Monsoon (WNPSM) is one of those monsoons and it is known to have a great impact both over the global climate and over the total precipitation of very densely populated areas. The interannual variability of the WNPSM along the last 50-60 years has been related to different climatic indices such as El Niño, El Niño Modoki, the Indian Ocean Dipole or the Pacific Decadal Oscillation. Recently, a new and longer series characterizing the monthly evolution of the WNPSM, the WNP Directional Index (WNPDI), has been developed, extending its previous length from about 50 years to more than 100 years (1900-2007). Imbalanced classification techniques have been applied to the WNPDI in order to check the capability of traditional climate indices to capture and forecast the evolution of the WNPSM. The problem of forecasting has been transformed into a binary classification problem, in which the positive class represents the occurrence of an extreme monsoon event. Given that the number of extreme monsoons is much lower than the number of non-extreme monsoons, the resultant classification problem is highly imbalanced. The complete dataset is composed of 1296 instances, where only 71 (5.47%) samples correspond to extreme monsoons. Twenty predictor variables based on the cited climatic indices have been proposed, and namely, models based on trees, black box models such as neural networks, support vector machines and nearest neighbors, and finally ensemble-based techniques as random forests have been used in order to forecast the occurrence of extreme monsoons. It can be concluded that the methodology proposed here reports promising results according to the quality parameters evaluated and predicts extreme monsoons for a temporal horizon of a month with a high accuracy. From a climatological point of view, models based on trees show that the index of the El Niño Modoki in the months previous to an extreme monsoon acts as its best predictor. In most cases, the value of the Indian Ocean Dipole index acts as a second order classifier. But El Niño index, more frequently, or the Pacific Decadal Oscillation index, only in one case, do also modulate the intensity of the WNPSM in some cases.

Enhanced Excitatory Connectivity and Disturbed Sound Processing in the Auditory Brainstem of Fragile X Mice.

PubMed

Garcia-Pino, Elisabet; Gessele, Nikodemus; Koch, Ursula

2017-08-02

Hypersensitivity to sounds is one of the prevalent symptoms in individuals with Fragile X syndrome (FXS). It manifests behaviorally early during development and is often used as a landmark for treatment efficacy. However, the physiological mechanisms and circuit-level alterations underlying this aberrant behavior remain poorly understood. Using the mouse model of FXS ( Fmr1 KO ), we demonstrate that functional maturation of auditory brainstem synapses is impaired in FXS. Fmr1 KO mice showed a greatly enhanced excitatory synaptic input strength in neurons of the lateral superior olive (LSO), a prominent auditory brainstem nucleus, which integrates ipsilateral excitation and contralateral inhibition to compute interaural level differences. Conversely, the glycinergic, inhibitory input properties remained unaffected. The enhanced excitation was the result of an increased number of cochlear nucleus fibers converging onto one LSO neuron, without changing individual synapse properties. Concomitantly, immunolabeling of excitatory ending markers revealed an increase in the immunolabeled area, supporting abnormally elevated excitatory input numbers. Intrinsic firing properties were only slightly enhanced. In line with the disturbed development of LSO circuitry, auditory processing was also affected in adult Fmr1 KO mice as shown with single-unit recordings of LSO neurons. These processing deficits manifested as an increase in firing rate, a broadening of the frequency response area, and a shift in the interaural level difference function of LSO neurons. Our results suggest that this aberrant synaptic development of auditory brainstem circuits might be a major underlying cause of the auditory processing deficits in FXS. SIGNIFICANCE STATEMENT Fragile X Syndrome (FXS) is the most common inheritable form of intellectual impairment, including autism. A core symptom of FXS is extreme sensitivity to loud sounds. This is one reason why individuals with FXS tend to avoid social interactions, contributing to their isolation. Here, a mouse model of FXS was used to investigate the auditory brainstem where basic sound information is first processed. Loss of the Fragile X mental retardation protein leads to excessive excitatory compared with inhibitory inputs in neurons extracting information about sound levels. Functionally, this elevated excitation results in increased firing rates, and abnormal coding of frequency and binaural sound localization cues. Imbalanced early-stage sound level processing could partially explain the auditory processing deficits in FXS. Copyright © 2017 the authors 0270-6474/17/377403-17$15.00/0.

KernelADASYN: Kernel Based Adaptive Synthetic Data Generation for Imbalanced Learning

DTIC Science & Technology

2015-08-17

eases [35], Indian liver patient dataset (ILPD) [36], Parkinsons dataset [37], Vertebral Column dataset [38], breast cancer dataset [39], breast tissue...Both the data set of Breast Cancer and Breast Tissue aim to predict the patient is normal or abnormal according to the measurements. The data set SPECT...9, pp. 1263–1284, 2009. [3] M. Elter, R. Schulz-Wendtland, and T. Wittenberg, “The prediction of breast cancer biopsy outcomes using two cad

Modeling imbalanced economic recovery following a natural disaster using input-output analysis.

PubMed

Li, Jun; Crawford-Brown, Douglas; Syddall, Mark; Guan, Dabo

2013-10-01

Input-output analysis is frequently used in studies of large-scale weather-related (e.g., Hurricanes and flooding) disruption of a regional economy. The economy after a sudden catastrophe shows a multitude of imbalances with respect to demand and production and may take months or years to recover. However, there is no consensus about how the economy recovers. This article presents a theoretical route map for imbalanced economic recovery called dynamic inequalities. Subsequently, it is applied to a hypothetical postdisaster economic scenario of flooding in London around the year 2020 to assess the influence of future shocks to a regional economy and suggest adaptation measures. Economic projections are produced by a macro econometric model and used as baseline conditions. The results suggest that London's economy would recover over approximately 70 months by applying a proportional rationing scheme under the assumption of initial 50% labor loss (with full recovery in six months), 40% initial loss to service sectors, and 10-30% initial loss to other sectors. The results also suggest that imbalance will be the norm during the postdisaster period of economic recovery even though balance may occur temporarily. Model sensitivity analysis suggests that a proportional rationing scheme may be an effective strategy to apply during postdisaster economic reconstruction, and that policies in transportation recovery and in health care are essential for effective postdisaster economic recovery. © 2013 Society for Risk Analysis.

Analysis of classifiers performance for classification of potential microcalcification

NASA Astrophysics Data System (ADS)

M. N., Arun K.; Sheshadri, H. S.

2013-07-01

Breast cancer is a significant public health problem in the world. According to the literature early detection improve breast cancer prognosis. Mammography is a screening tool used for early detection of breast cancer. About 10-30% cases are missed during the routine check as it is difficult for the radiologists to make accurate analysis due to large amount of data. The Microcalcifications (MCs) are considered to be important signs of breast cancer. It has been reported in literature that 30% - 50% of breast cancer detected radio graphically show MCs on mammograms. Histologic examinations report 62% to 79% of breast carcinomas reveals MCs. MC are tiny, vary in size, shape, and distribution, and MC may be closely connected to surrounding tissues. There is a major challenge using the traditional classifiers in the classification of individual potential MCs as the processing of mammograms in appropriate stage generates data sets with an unequal amount of information for both classes (i.e., MC, and Not-MC). Most of the existing state-of-the-art classification approaches are well developed by assuming the underlying training set is evenly distributed. However, they are faced with a severe bias problem when the training set is highly imbalanced in distribution. This paper addresses this issue by using classifiers which handle the imbalanced data sets. In this paper, we also compare the performance of classifiers which are used in the classification of potential MC.

Mass-imbalanced ionic Hubbard chain

NASA Astrophysics Data System (ADS)

Sekania, Michael; Baeriswyl, Dionys; Jibuti, Luka; Japaridze, George I.

2017-07-01

A repulsive Hubbard model with both spin-asymmetric hopping (t↑≠t↓ ) and a staggered potential (of strength Δ ) is studied in one dimension. The model is a compound of the mass-imbalanced (t↑≠t↓ ,Δ =0 ) and ionic (t↑=t↓ ,Δ >0 ) Hubbard models, and may be realized by cold atoms in engineered optical lattices. We use mostly mean-field theory to determine the phases and phase transitions in the ground state for a half-filled band (one particle per site). We find that a period-two modulation of the particle (or charge) density and an alternating spin density coexist for arbitrary Hubbard interaction strength, U ≥0 . The amplitude of the charge modulation is largest at U =0 , decreases with increasing U and tends to zero for U →∞ . The amplitude for spin alternation increases with U and tends to saturation for U →∞ . Charge order dominates below a value Uc, whereas magnetic order dominates above. The mean-field Hamiltonian has two gap parameters, Δ↑ and Δ↓, which have to be determined self-consistently. For U Uc they have different signs, and for U =Uc one gap parameter jumps from a positive to a negative value. The weakly first-order phase transition at Uc can be interpreted in terms of an avoided criticality (or metallicity). The system is reluctant to restore a symmetry that has been broken explicitly.

Maternal Dietary Nutrient Intake and Its Association  with Preterm Birth: A Case-control Study in Beijing,  China.

PubMed

Zhang, Yan; Zhou, Hong; Perkins, Anthony; Wang, Yan; Sun, Jing

2017-03-01

This study aimed to evaluate dietary nutrient intake among Chinese pregnant women by comparison with Chinese Dietary Reference Intakes (DRIs) and to explore the association between dietary nutrients and preterm birth. A case-control design was conducted in Beijing with 130 preterm delivery mothers in case group and 381 term delivery mothers in control group. Information on mothers' diet was collected using a food frequency questionnaire, and nutrients and energy intakes were subsequently calculated based on DRIs. Multivariate analysis of variance was used to compare the differences between term and preterm groups in relation to dietary nutrients. Dietary nutrient intakes were imbalanced in both groups compared with Chinese DRIs. Preterm delivery mothers had a lower level of fat and vitamin E intake than term delivery mothers (p < 0.05). Multivariate analysis showed lower vitamin E intake in preterm delivery mothers with a prepregnancy BMI < 18.5 kg/m2 (p < 0.05) and higher carbohydrate intake in preterm delivery mothers with prepregnancy BMI ≥ 24 kg/m2 (p < 0.05). An imbalanced diet in both groups and low level of dietary intakes of fat and vitamin E in preterm group suggest health education measures should be taken to improve the dietary quality of pregnant women, especially for those with an abnormal prepregnancy BMI.

Update on imbalanced distribution of endodontists: 1995-2006.

PubMed

Waldman, H Barry; Bruder, George A

2009-05-01

Past studies on the number of endodontists in the United States indicated an imbalanced distribution of private practice endodontic practitioners in most regions, states, counties, and zip code areas of the country. The availability of more recent studies by the American Dental Association (ADA) provides an opportunity to follow up on these previous studies. The 2006 and past ADA surveys on the Distribution of Dentists in the United States, Advanced Dental Education, and Dental Practice were used to evaluate the number of graduates from advanced education programs in endodontics, the changing number and distribution of endodontists, full-time and part-time work patterns, and the income of private practicing endodontists. A gradual increase in the number of graduates from advanced education programs in endodontics is reflected in a continuing increase in the overall number of private practicing endodontists, but with ongoing differences in endodontists-to-population ratios at the regional and state levels. The findings follow previous study results confirming the increasing numbers of endodontists and continuing differences in the endodontists-to-population ratios at both the regional and state levels. Concerns about the distribution of endodontists in the future need to be considered in terms of evolving dental disease patterns, changing demands for services, evolving third-party mechanisms, and the increased number of female practitioners (with fewer reported working hours than their male counterparts).

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer.

PubMed

Liu, Maolin; Li, Huaiyu; Wang, Yuan; Li, Fei; Chen, Xiuwan

2018-04-01

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer's application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

Lifelong imbalanced LA/ALA intake impairs emotional and cognitive behavior via changes in brain endocannabinoid system

PubMed Central

Zamberletti, Erica; Piscitelli, Fabiana; De Castro, Valentina; Murru, Elisabetta; Gabaglio, Marina; Colucci, Paola; Fanali, Chiara; Prini, Pamela; Bisogno, Tiziana; Maccarrone, Mauro; Campolongo, Patrizia; Banni, Sebastiano; Rubino, Tiziana; Parolaro, Daniela

2017-01-01

Imbalanced dietary n-3 and n-6 PUFA content has been associated with a number of neurological conditions. Endocannabinoids are n-6 PUFA derivatives, whose brain concentrations are sensitive to modifications of fatty acid composition of the diet and play a central role in the regulation of mood and cognition. As such, the endocannabinoid system appears to be an ideal candidate for mediating the effects of dietary fatty acids on mood and cognition. Lifelong administration of isocaloric α-linolenic acid (ALA)-deficient and -enriched diets induced short-term memory deficits, whereas only dietary ALA enrichment altered emotional reactivity in adult male rats compared with animals fed a standard diet that was balanced in ALA/linoleic acid (LA) ratio. In the prefrontal cortex, both diets reduced 2-AG levels and increased MAG lipase expression, whereas only the enriched diet reduced AEA levels, simultaneously increasing FAAH expression. In the hippocampus, an ALA-enriched diet decreased AEA content and NAPE-PLD expression, and reduced 2-AG content while increasing MAG lipase expression. These findings highlight the importance of a diet balanced in fatty acid content for normal brain functions and to support a link between dietary ALA, the brain endocannabinoid system, and behavior, which indicates that dietary ALA intake is a sufficient condition for altering the endocannabinoid system in brain regions modulating mood and cognition. PMID:27903595

Both chronic treatments by epothilone D and fluoxetine increase the short-term memory and differentially alter the mood status of STOP/MAP6 KO mice.

PubMed

Fournet, Vincent; de Lavilléon, Gaetan; Schweitzer, Annie; Giros, Bruno; Andrieux, Annie; Martres, Marie-Pascale

2012-12-01

Recent evidence underlines the crucial role of neuronal cytoskeleton in the pathophysiology of psychiatric diseases. In this line, the deletion of STOP/MAP6 (Stable Tubule Only Polypeptide), a microtubule-stabilizing protein, triggers various neurotransmission and behavioral defects, suggesting that STOP knockout (KO) mice could be a relevant experimental model for schizoaffective symptoms. To establish the predictive validity of such a mouse line, in which the brain serotonergic tone is dramatically imbalanced, the effects of a chronic fluoxetine treatment on the mood status of STOP KO mice were characterized. Moreover, we determined the impact, on mood, of a chronic treatment by epothilone D, a taxol-like microtubule-stabilizing compound that has previously been shown to improve the synaptic plasticity deficits of STOP KO mice. We demonstrated that chronic fluoxetine was either antidepressive and anxiolytic, or pro-depressive and anxiogenic, depending on the paradigm used to test treated mutant mice. Furthermore, control-treated STOP KO mice exhibited paradoxical behaviors, compared with their clear-cut basal mood status. Paradoxical fluoxetine effects and control-treated STOP KO behaviors could be because of their hyper-reactivity to acute and chronic stress. Interestingly, both epothilone D and fluoxetine chronic treatments improved the short-term memory of STOP KO mice. Such treatments did not affect the serotonin and norepinephrine transporter densities in cerebral areas of mice. Altogether, these data demonstrated that STOP KO mice could represent a useful model to study the relationship between cytoskeleton, mood, and stress, and to test innovative mood treatments, such as microtubule-stabilizing compounds. © 2012 The Authors Journal of Neurochemistry © 2012 International Society for Neurochemistry.

An aberrant NOTCH2-BCR signaling axis in B cells from patients with chronic GVHD.

PubMed

Poe, Jonathan C; Jia, Wei; Su, Hsuan; Anand, Sarah; Rose, Jeremy J; Tata, Prasanthi V; Suthers, Amy N; Jones, Corbin D; Kuan, Pei Fen; Vincent, Benjamin G; Serody, Jonathan S; Horwitz, Mitchell E; Ho, Vincent T; Pavletic, Steven Z; Hakim, Frances T; Owzar, Kouros; Zhang, Dadong; Blazar, Bruce R; Siebel, Christian W; Chao, Nelson J; Maillard, Ivan; Sarantopoulos, Stefanie

2017-11-09

B-cell receptor (BCR)-activated B cells contribute to pathogenesis in chronic graft-versus-host disease (cGVHD), a condition manifested by both B-cell autoreactivity and immune deficiency. We hypothesized that constitutive BCR activation precluded functional B-cell maturation in cGVHD. To address this, we examined BCR-NOTCH2 synergy because NOTCH has been shown to increase BCR responsiveness in normal mouse B cells. We conducted ex vivo activation and signaling assays of 30 primary samples from hematopoietic stem cell transplantation patients with and without cGVHD. Consistent with a molecular link between pathways, we found that BCR-NOTCH activation significantly increased the proximal BCR adapter protein BLNK. BCR-NOTCH activation also enabled persistent NOTCH2 surface expression, suggesting a positive feedback loop. Specific NOTCH2 blockade eliminated NOTCH-BCR activation and significantly altered NOTCH downstream targets and B-cell maturation/effector molecules. Examination of the molecular underpinnings of this "NOTCH2-BCR axis" in cGVHD revealed imbalanced expression of the transcription factors IRF4 and IRF8 , each critical to B-cell differentiation and fate. All- trans retinoic acid (ATRA) increased IRF4 expression, restored the IRF4 -to- IRF8 ratio, abrogated BCR-NOTCH hyperactivation, and reduced NOTCH2 expression in cGVHD B cells without compromising viability. ATRA-treated cGVHD B cells had elevated TLR9 and PAX5 , but not BLIMP1 (a gene-expression pattern associated with mature follicular B cells) and also attained increased cytosine guanine dinucleotide responsiveness. Together, we reveal a mechanistic link between NOTCH2 activation and robust BCR responses to otherwise suboptimal amounts of surrogate antigen. Our findings suggest that peripheral B cells in cGVHD patients can be pharmacologically directed from hyperactivation toward maturity.

Trib3 Is Developmentally and Nutritionally Regulated in the Brain but Is Dispensable for Spatial Memory, Fear Conditioning and Sensing of Amino Acid-Imbalanced Diet

PubMed Central

Örd, Tiit; Innos, Jürgen; Lilleväli, Kersti; Tekko, Triin; Sütt, Silva; Örd, Daima; Kõks, Sulev; Vasar, Eero; Örd, Tõnis

2014-01-01

Tribbles homolog 3 (TRIB3) is a mammalian pseudokinase that is induced in neuronal cell cultures in response to cell death-inducing stresses, including neurotrophic factor deprivation. TRIB3 is an inhibitor of activating transcription factor 4 (ATF4), the central transcriptional regulator in the eukaryotic translation initiation factor 2α (eIF2α) phosphorylation pathway that is involved in the cellular stress response and behavioral processes. In this article, we study the expression of Trib3 in the mouse brain, characterize the brain morphology of mice with a genetic ablation of Trib3 and investigate whether Trib3 deficiency alters eIF2α-dependent cognitive abilities. Our data show that the consumption of a leucine-deficient diet induces Trib3 expression in the anterior piriform cortex, the brain region responsible for detecting essential amino acid intake imbalance. However, the aversive response to leucine-devoid diet does not differ in Trib3 knockout and wild type mice. Trib3 deletion also does not affect long-term spatial memory and reversal learning in the Morris water maze and auditory or contextual fear conditioning. During embryonic development, Trib3 expression increases in the brain and persists in the early postnatal stadium. Neuroanatomical characterization of mice lacking Trib3 revealed enlarged lateral ventricles. Thus, although the absence of Trib3 does not alter the eIF2α pathway-dependent cognitive functions of several areas of the brain, including the hippocampus, amygdala and anterior piriform cortex, Trib3 may serve a role in other central nervous system processes and molecular pathways. PMID:24732777

Simultaneous in-cell derivatization pressurized liquid extraction for the determination of multiclass preservatives in leave-on cosmetics.

PubMed

Sanchez-Prado, Lucia; Lamas, J Pablo; Lores, Marta; Garcia-Jares, Carmen; Llompart, Maria

2010-11-15

An effective one-step sample preparation methodology for the determination of multiclass preservatives in cosmetics has been developed, applying, for the first time to this kind of matrix, pressurized liquid extraction (PLE) and a very simple, cheap, and fast derivatization procedure: acetylation with acetic anhydride and pyridine. A multifactorial experimental design has been used to evaluate and optimize the main experimental parameters potentially affecting the extraction process. In the final conditions the sample was mixed with Florisil as the dispersing sorbent and extracted with ethyl acetate for 15 min at 120 °C. One of the main goals of this work was to demonstrate the possibility of carrying out direct cosmetic preservative acetylation by simply adding the derivatization reagents into the PLE cell. The extract was then analyzed by GC/MS without any further cleanup or concentration step. The accuracy, precision, linearity, and detection limits (LODs) were evaluated to assess the performance of the proposed method. Quantitative recoveries were obtained, and relative standard deviation values were lower than 10% in all cases. The obtained LODs ranged from 0.000004% to 0.0001% (w/w), values far below the established restrictions in the European Cosmetics Regulation, making this multicomponent analytical method suitable for routine control. Finally, several cosmetic products such as moisturizing and antiwrinkle creams and lotions, hand creams, sunscreen and after-sun creams, baby lotions, and hair care products were analyzed. All the samples contained several of the target cosmetic ingredients, in some cases at quite high concentrations, although the actual European Cosmetics Regulation was fulfilled in all cases.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

PubMed

Liu, Zhenqiu; Hsiao, William; Cantarel, Brandi L; Drábek, Elliott Franco; Fraser-Liggett, Claire

2011-12-01

Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. zliu@umm.edu Supplementary data are available at Bioinformatics online.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Attribute-based Decision Graphs: A framework for multiclass data classification.

PubMed

Bertini, João Roberto; Nicoletti, Maria do Carmo; Zhao, Liang

2017-01-01

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph-AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme

PubMed Central

Abou Zeid, Elias; Rezazadeh Sereshkeh, Alborz; Schultz, Benjamin; Chau, Tom

2017-01-01

In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have mainly attempted binary single-trial classification of RP. An RP-based BCI with three or more states would expand the options for functional control. Here, we propose a ternary BCI based on single-trial RPs. This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement. A pipeline of spatio-temporal filtering with per participant parameter optimization was used for feature extraction. The ternary classification was decomposed into binary classifications using a decision-directed acyclic graph (DDAG). For each class pair in the DDAG structure, an ordered diversified classifier system (ODCS-DDAG) was used to select the best among various classification algorithms or to combine the results of different classification algorithms. Using EEG data from 14 participants performing self-initiated left or right key presses, punctuated with rest periods, we compared the performance of ODCS-DDAG to a ternary classifier and four popular multiclass decomposition methods using only a single classification algorithm. ODCS-DDAG had the highest performance (0.769 Cohen's Kappa score) and was significantly better than the ternary classifier and two of the four multiclass decomposition methods. Our work supports further study of RP-based BCI for intuitive asynchronous environmental control or augmentative communication. PMID:28596725

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

Development of multiclass methods for drug residues in eggs: hydrophilic solid-phase extraction cleanup and liquid chromatography/tandem mass spectrometry analysis of tetracycline, fluoroquinolone, sulfonamide, and beta-lactam residues.

PubMed

Heller, David N; Nochetto, Cristina B; Rummel, Nathan G; Thomas, Michael H

2006-07-26

A method was developed for detection of a variety of polar drug residues in eggs via liquid chromatography/tandem mass spectrometry (LC/MS/MS) with electrospray ionization (ESI). A total of twenty-nine target analytes from four drug classes-sulfonamides, tetracyclines, fluoroquinolones, and beta-lactams-were extracted from eggs using a hydrophilic-lipophilic balance polymer solid-phase extraction (SPE) cartridge. The extraction technique was developed for use at a target concentration of 100 ng/mL (ppb), and it was applied to eggs containing incurred residues from dosed laying hens. The ESI source was tuned using a single, generic set of tuning parameters, and analytes were separated with a phenyl-bonded silica cartridge column using an LC gradient. In a related study, residues of beta-lactam drugs were not found by LC/MS/MS in eggs from hens dosed orally with beta-lactam drugs. LC/MS/MS performance was evaluated on two generations of ion trap mass spectrometers, and key operational parameters were identified for each instrument. The ion trap acquisition methods could be set up for screening (a single product ion) or confirmation (multiple product ions). The lower limit of detection for screening purposes was 10-50 ppb (sulfonamides), 10-20 ppb (fluoroquinolones), and 10-50 ppb (tetracyclines), depending on the drug, instrument, and acquisition method. Development of this method demonstrates the feasibility of generic SPE, LC, and MS conditions for multiclass LC/MS residue screening.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Wang, Yijun; Zhang, Shangen; Gao, Shangkai; Hu, Yong; Gao, Xiaorong

2017-04-01

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted. Approach. This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based ‘on/off’ stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis. Main results. SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance. Significance. These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.

Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

PubMed Central

Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

2016-01-01

A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909–0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06–2 ng/L and 0.2–6 ng/L for OCPs and 0.02–3 ng/L and 0.06–7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65–9.89% for OCPs and 0.98–13.99% for PYPs, respectively. Average recoveries were in the range of 47.74–120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67–31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis. PMID:26883080

Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

NASA Astrophysics Data System (ADS)

Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

2016-02-01

A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909-0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06-2 ng/L and 0.2-6 ng/L for OCPs and 0.02-3 ng/L and 0.06-7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65-9.89% for OCPs and 0.98-13.99% for PYPs, respectively. Average recoveries were in the range of 47.74-120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67-31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis.

Dilute-and-shoot coupled to nanoflow liquid chromatography high resolution mass spectrometry for the determination of drugs of abuse and sport drugs in human urine.

PubMed

Alcántara-Durán, Jaime; Moreno-González, David; Beneito-Cambra, Miriam; García-Reyes, Juan F

2018-05-15

In this work, a sensitive nanoflow liquid chromatography high-resolution mass spectrometry screening method has been developed for the determination of multiclass drugs of abuse and sport drugs in human urine. 81 drugs belonging to different multiclass pharmaceuticals were targeted. The method is based on the use of a nanoLC column (75 µm × 150 mm, 3 µm particle size and 100 Å pore) with the nanospray emitter tip integrated so that dead volumes are significantly minimized. Data acquisition method included both full-scan and all ion fragmentation experiments using an Orbitrap analyser (Q-Exactive) operated in the positive ionization mode. To increase laboratory throughput, a dilute-and-shoot methodology has been tested and proposed, based solely on direct urine dilution without further sample workup. Matrix effects were evaluated, showing a negligible effect for all studied compounds when a dilution 1:50 was implemented. Despite this high-dilution factor, limits of quantification were still satisfactory, with values below 5 µg L -1 in most cases, being lower than their minimum required performance limits correspond established by the World Anti-Doping Agency. Therefore, the use of the dilute-and-shoot method with the enhanced sensitivity provided by nanoflow LC setup could be useful tool for the determination of studied compounds in drug testing, thus increasing laboratory performance, because a minimum sample treatment steps are required. Copyright © 2018 Elsevier B.V. All rights reserved.

A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.

PubMed

Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Mrachacz-Kersting, Natalie; Zhu, Xiangyang; Farina, Dario

2017-09-01

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain-computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems. Thirty two subjects were randomly divided into a Control-Group and a Hybrid-Group. In the Control-Group, the subjects performed left and right hand motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the subjects performed the four mental tasks (i.e., L-MI, R-MI, L-SAO, and R-SAO). The results indicate that combining two of the tasks in a hybrid manner (such as L-SAO and R-MI) resulted in a significantly greater classification accuracy than when using two MI tasks. The hybrid modality reached 86.1% classification accuracy on average, with a 7.70% increase with respect to MI ( ), and 7.21% to SAO ( ) alone. Moreover, all 16 subjects in the hybrid modality reached at least 70% accuracy, which is considered the threshold for BCI illiteracy. In addition to the two-class results, the classification accuracy was 68.1% and 54.1% for the three-class and four-class hybrid BCI. Combining the induced brain signals from motor and somatosensory cortex, the proposed stimulus-independent hybrid BCI has shown improved performance with respect to individual modalities, reducing the portion of BCI-illiterate subjects, and provided novel types of multi-class BCIs.

Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.

PubMed

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, H A

2007-12-15

Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is approximately 8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Dataset and stand-alone program are available upon request.

Modeling self-consistent multi-class dynamic traffic flow

NASA Astrophysics Data System (ADS)

Cho, Hsun-Jung; Lo, Shih-Ching

2002-09-01

In this study, we present a systematic self-consistent multiclass multilane traffic model derived from the vehicular Boltzmann equation and the traffic dispersion model. The multilane domain is considered as a two-dimensional space and the interaction among vehicles in the domain is described by a dispersion model. The reason we consider a multilane domain as a two-dimensional space is that the driving behavior of road users may not be restricted by lanes, especially motorcyclists. The dispersion model, which is a nonlinear Poisson equation, is derived from the car-following theory and the equilibrium assumption. Under the concept that all kinds of users share the finite section, the density is distributed on a road by the dispersion model. In addition, the dynamic evolution of the traffic flow is determined by the systematic gas-kinetic model derived from the Boltzmann equation. Multiplying Boltzmann equation by the zeroth, first- and second-order moment functions, integrating both side of the equation and using chain rules, we can derive continuity, motion and variance equation, respectively. However, the second-order moment function, which is the square of the individual velocity, is employed by previous researches does not have physical meaning in traffic flow. Although the second-order expansion results in the velocity variance equation, additional terms may be generated. The velocity variance equation we propose is derived from multiplying Boltzmann equation by the individual velocity variance. It modifies the previous model and presents a new gas-kinetic traffic flow model. By coupling the gas-kinetic model and the dispersion model, a self-consistent system is presented.

Empirical Analysis and Automated Classification of Security Bug Reports

NASA Technical Reports Server (NTRS)

Tyo, Jacob P.

2016-01-01

With the ever expanding amount of sensitive data being placed into computer systems, the need for effective cybersecurity is of utmost importance. However, there is a shortage of detailed empirical studies of security vulnerabilities from which cybersecurity metrics and best practices could be determined. This thesis has two main research goals: (1) to explore the distribution and characteristics of security vulnerabilities based on the information provided in bug tracking systems and (2) to develop data analytics approaches for automatic classification of bug reports as security or non-security related. This work is based on using three NASA datasets as case studies. The empirical analysis showed that the majority of software vulnerabilities belong only to a small number of types. Addressing these types of vulnerabilities will consequently lead to cost efficient improvement of software security. Since this analysis requires labeling of each bug report in the bug tracking system, we explored using machine learning to automate the classification of each bug report as a security or non-security related (two-class classification), as well as each security related bug report as specific security type (multiclass classification). In addition to using supervised machine learning algorithms, a novel unsupervised machine learning approach is proposed. An ac- curacy of 92%, recall of 96%, precision of 92%, probability of false alarm of 4%, F-Score of 81% and G-Score of 90% were the best results achieved during two-class classification. Furthermore, an accuracy of 80%, recall of 80%, precision of 94%, and F-score of 85% were the best results achieved during multiclass classification.

Analysis of multi-class preservatives in leave-on and rinse-off cosmetics by matrix solid-phase dispersion.

PubMed

Sanchez-Prado, Lucia; Alvarez-Rivera, Gerardo; Lamas, J Pablo; Lores, Marta; Garcia-Jares, Carmen; Llompart, Maria

2011-12-01

Matrix solid-phase extraction has been successfully applied for the determination of multi-class preservatives in a wide variety of cosmetic samples including rinse-off and leave-on products. After extraction, derivatization with acetic anhydride, and gas chromatography-mass spectrometry analysis were performed. Optimization studies were done on real non-spiked and spiked leave-on and rinse-off cosmetic samples. The selection of the most suitable extraction conditions was made using statistical tools such as ANOVA, as well as factorial experimental designs. The final optimized conditions were common for both groups of cosmetics and included the dispersion of the sample with Florisil (1:4), and the elution of the MSPD column with 5 mL of hexane/acetone (1:1). After derivatization, the extract was analyzed without any further clean-up or concentration step. Accuracy, precision, linearity and detection limits were evaluated to assess the performance of the proposed method. The recovery studies on leave-on and rinse-off cosmetics gave satisfactory values (>78% for all analytes in all the samples) with an average relative standard deviation value of 4.2%. The quantification limits were well below those set by the international cosmetic regulations, making this multi-component analytical method suitable for routine control. The analysis of a broad range of cosmetics including body milk, moisturizing creams, anti-stretch marks creams, hand creams, deodorant, shampoos, liquid soaps, makeup, sun milk, hand soaps, among others, demonstrated the high use of most of the target preservatives, especially butylated hydroxytoluene, methylparaben, propylparaben, and butylparaben.

Education in the imbalance of Nature

NASA Astrophysics Data System (ADS)

Shlafman, L. M.; Kontar, V. A.

2013-12-01

There are two concepts understanding of the real Nature: balanced and imbalanced. The traditional balanced concept understanding of Nature was originated in prehistoric times to calm the frightened souls of prehistoric man and manage groups of people. The balanced concept presupposes that Nature is isotropic, balanced, etc. The balanced concept of understanding of Nature gradually has moved to science and technology. The balanced concept of understanding of Nature is dominating from the prehistoric time up to today. But always parallel and opposite was exists the concept imbalanced understanding of Nature, which presupposes that Nature is anisotropy, imbalanced, etc. The balanced concept is much simpler than Imbalanced. The balanced concept has given mankind a lot of rough description of Nature which helped to solve a lot of practical problems but with sufficient accuracy, i.e. approximately, but not with an absolute precision. While people were few, and a lot of resources, person could take from Nature only what Nature gave willingly. During this period, people feared and respected Nature and Nature was able easily compensate the activity of people. The high accuracy of the description of Nature was not needed when resources were plentiful and people were few. But now the situation is completely different. The population has become a very large and growing. Traditional resources are almost run out and the lack of resources escalates. People are not afraid of Nature and bravely try to take by force what Nature does not give voluntarily. People invaded into imbalance Nature, and Nature can no longer compensate activity of people. The era of global change is started, including those that man provokes. In the conditions of global changes is insufficiently of the approximate solutions of the traditional balanced concept. The balanced concept is exhausted, and increasingly misleads people. The balanced concept cannot solve the problems that arise in the global change. The Concept imbalance of Nature is more suitable to solve problems of global change. Many thousands of scientists and practical from around the world are working already in the field of imbalance of Nature. Now is a time when the concept imbalance of Nature should be included into the training programs at universities, colleges and schools. We have experts who can teach students in imbalance of Nature. We have a list of topics for Ph.D. dissertations in field of imbalance of Nature. Now we are preparing the fundamental scientific book on all major issues imbalance of Nature. On the basis of this fundamental scientific work will be prepared the textbooks for students of different levels, non-fiction books, will organized promotion of the imbalance of Nature in the media, social networks, etc. People need to understand the real Nature as it is. This new knowledge will help humanity make the right and safer decisions in the era of global change. We invite the universities, colleges, schools, community organizations, sponsors and just responsible people of any country in the world to take part in this noble and vital issue. Humanity has no choice. Or mankind will have time to learn how to live in the era of global change or perish. The concept imbalance of Nature gives humanity the chance to survive.

Generalized virial theorem and pressure relation for a strongly correlated Fermi gas

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

Tan, Shina

2008-12-15

For a two-component Fermi gas in the unitarity limit (i.e., with infinite scattering length), there is a well-known virial theorem, first shown by J.E. Thomas et al. A few people rederived this result, and extended it to few-body systems, but their results are all restricted to the unitarity limit. Here I show that there is a generalized virial theorem for FINITE scattering lengths. I also generalize an exact result concerning the pressure to the case of imbalanced populations.

Applications of physical methods in high-frequency futures markets

NASA Astrophysics Data System (ADS)

Bartolozzi, M.; Mellen, C.; Chan, F.; Oliver, D.; Di Matteo, T.; Aste, T.

2007-12-01

In the present work we demonstrate the application of different physical methods to high-frequency or tick-bytick financial time series data. In particular, we calculate the Hurst exponent and inverse statistics for the price time series taken from a range of futures indices. Additionally, we show that in a limit order book the relaxation times of an imbalanced book state with more demand or supply can be described by stretched exponential laws analogous to those seen in many physical systems.

Is the Army’s Reserve Component Imbalanced, Separate and Unequal?

DTIC Science & Technology

2012-03-02

USAR have divided their compositions in two ways, the ARNG is predominately a RC Maneuver Fires and Effects ( MFE ) provider to the US Army, and the...the Maneuver, Fires, & Effects ( MFE ), 27% in the Operational Support (OS), and 15% in Force Sustainment (FS).22 (See Figure 2).23 Current ARNG‟s...Army‟s RC primary MFE force provider. While the ARNG has flourished, the ARNG should balance their force ratio by adding enabler units in order to

Intelligent call admission control for multi-class services in mobile cellular networks

NASA Astrophysics Data System (ADS)

Ma, Yufeng; Hu, Xiulin; Zhang, Yunyu

2005-11-01

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in mobile cellular networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities of each service class. Simulation compares the proposed fuzzy scheme with complete sharing and guard channel policies. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

DTIC Science & Technology

1984-10-01

O M coF=F;; UMBER2. GOVT ACCE5SION NO.1 3 . REC,PINS :A7AL:,G NUMBER ( ’eneration and Terminat_,on :)f Binary D-ecision jC j ik; Trees for Nonnararetrc...1-I . v)IAMO 0~I4 EDvt" O F I 00 . 3 15I OR%.OL.ETL - S-S OCTOBER 1984 LIDS-P-1411 GENERATION AND TERMINATION OF BINARY DECISION TREES FOR...minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. 0 0 0/ 6 0¢ A 3 I. Introduction We state

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.

Registration of segmented histological images using thin plate splines and belief propagation

NASA Astrophysics Data System (ADS)

Kybic, Jan

2014-03-01

We register images based on their multiclass segmentations, for cases when correspondence of local features cannot be established. A discrete mutual information is used as a similarity criterion. It is evaluated at a sparse set of location on the interfaces between classes. A thin-plate spline regularization is approximated by pairwise interactions. The problem is cast into a discrete setting and solved efficiently by belief propagation. Further speedup and robustness is provided by a multiresolution framework. Preliminary experiments suggest that our method can provide similar registration quality to standard methods at a fraction of the computational cost.

Protein classification using sequential pattern mining.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2006-01-01

Protein classification in terms of fold recognition can be employed to determine the structural and functional properties of a newly discovered protein. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. One of the most efficient SPM algorithms, cSPADE, is employed for protein primary structure analysis. Then a classifier uses the extracted sequential patterns for classifying proteins of unknown structure in the appropriate fold category. The proposed methodology exhibited an overall accuracy of 36% in a multi-class problem of 17 candidate categories. The classification performance reaches up to 65% when the three most probable protein folds are considered.

Soft Computing Application in Fault Detection of Induction Motor

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

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

Multiclass Continuous Correspondence Learning

NASA Technical Reports Server (NTRS)

Bue, Brian D,; Thompson, David R.

2011-01-01

We extend the Structural Correspondence Learning (SCL) domain adaptation algorithm of Blitzer er al. to the realm of continuous signals. Given a set of labeled examples belonging to a 'source' domain, we select a set of unlabeled examples in a related 'target' domain that play similar roles in both domains. Using these 'pivot samples, we map both domains into a common feature space, allowing us to adapt a classifier trained on source examples to classify target examples. We show that when between-class distances are relatively preserved across domains, we can automatically select target pivots to bring the domains into correspondence.

A Features Selection for Crops Classification

NASA Astrophysics Data System (ADS)

Liu, Yifan; Shao, Luyi; Yin, Qiang; Hong, Wen

2016-08-01

The components of the polarimetric target decomposition reflect the differences of target since they linked with the scattering properties of the target and can be imported into SVM as the classification features. The result of decomposition usually concentrate on part of the components. Selecting a combination of components can reduce the features that importing into the SVM. The features reduction can lead to less calculation and targeted classification of one target when we classify a multi-class area. In this research, we import different combinations of features into the SVM and find a better combination for classification with a data of AGRISAR.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Decoupling of mass transport mechanisms in the stagewise swelling of multiple emulsions.

PubMed

Bahtz, Jana; Gunes, Deniz Z; Hughes, Eric; Pokorny, Lea; Riesch, Francesca; Syrbe, Axel; Fischer, Peter; Windhab, Erich J

2015-05-19

This contribution reports on the mass transport kinetics of osmotically imbalanced water-in-oil-in-water (W1/O/W2) emulsions. Although frequently studied, the control of mass transport in W1/O/W2 emulsions is still challenging. We describe a microfluidics-based method to systematically investigate the impact of various parameters, such as osmotic pressure gradient, oil phase viscosity, and temperature, on the mass transport. Combined with optical microscopy analyses, we are able to identify and decouple the various mechanisms, which control the dynamic droplet size of osmotically imbalanced W1/O/W2 emulsions. So, swelling kinetics curves with a very high accuracy are generated, giving a basis for quantifying the kinetic aspects of transport. Two sequential swelling stages, i.e., a lag stage and an osmotically dominated stage, with different mass transport mechanisms are identified. The determination and interpretation of the different stages are the prerequisite to control and trigger the swelling process. We show evidence that both mass transport mechanisms can be decoupled from each other. Rapid osmotically driven mass transport only takes place in a second stage induced by structural changes of the oil phase in a lag stage, which allow an osmotic exchange between both water phases. Such structural changes are strongly facilitated by spontaneous water-in-oil emulsification. The duration of the lag stage is pressure-independent but significantly influenced by the oil phase viscosity and temperature.

Fas/FasL interaction mediates imbalanced cytokine/cytotoxicity responses of iNKT cells against Jurkat cells.

PubMed

Dou, Rui; Hong, Zhenya; Tan, Xiaosheng; Hu, Fenfen; Ding, Yajie; Wang, Wei; Liang, Zhihui; Zhong, Rongrong; Wu, Xiongwen; Weng, Xiufang

2018-07-01

The rapid antitumor cytokine production and direct cytotoxicity confer invariant NKT (iNKT) cells ideal candidates for cancer therapy. However, the therapeutic potential of iNKT cells in T-cell malignant diseases remains elusive, as antigen presentation by T cells (T-T presentation) has been suggested to induce hyporesponsiveness of iNKT cells. In this study, we found discrepancies in iNKT cell responses against two T cell-origin cell lines (Jurkat and Molt-4). Human iNKT cells exhibited more intensive cytotoxicity and less efficient cytokine production in response to Fas-bearing Jurkat cells than those to the Fas-negative tumor cells (Molt-4 and myeloid-derived K562). The imbalanced cytokine/cytotoxicity responses of iNKT cells against Jurkat cells were CD1d-dependent and relied mostly on Fas/FasL interaction. The impairment in cytokine production could be overcome by Fas/FasL blocking antibodies and exogenous IL-2. Elevated CD1d levels as well as CD1d and Fas co-localization were found in T-cell lymphomas. However, defects in frequency and function of circulating iNKT cells were observed in the patients, which could be partly rescued by exogenous IL-2. Collectively, the Fas/FasL-dependent aberrant iNKT cell responses and the reversibility of the defects suggest the distinct iNKT cell manipulation in CD1d- and Fas-bearing T cell malignancies. Copyright © 2018. Published by Elsevier Ltd.

Lifelong imbalanced LA/ALA intake impairs emotional and cognitive behavior via changes in brain endocannabinoid system.

PubMed

Zamberletti, Erica; Piscitelli, Fabiana; De Castro, Valentina; Murru, Elisabetta; Gabaglio, Marina; Colucci, Paola; Fanali, Chiara; Prini, Pamela; Bisogno, Tiziana; Maccarrone, Mauro; Campolongo, Patrizia; Banni, Sebastiano; Rubino, Tiziana; Parolaro, Daniela

2017-02-01

Imbalanced dietary n-3 and n-6 PUFA content has been associated with a number of neurological conditions. Endocannabinoids are n-6 PUFA derivatives, whose brain concentrations are sensitive to modifications of fatty acid composition of the diet and play a central role in the regulation of mood and cognition. As such, the endocannabinoid system appears to be an ideal candidate for mediating the effects of dietary fatty acids on mood and cognition. Lifelong administration of isocaloric α-linolenic acid (ALA)-deficient and -enriched diets induced short-term memory deficits, whereas only dietary ALA enrichment altered emotional reactivity in adult male rats compared with animals fed a standard diet that was balanced in ALA/linoleic acid (LA) ratio. In the prefrontal cortex, both diets reduced 2-AG levels and increased MAG lipase expression, whereas only the enriched diet reduced AEA levels, simultaneously increasing FAAH expression. In the hippocampus, an ALA-enriched diet decreased AEA content and NAPE-PLD expression, and reduced 2-AG content while increasing MAG lipase expression. These findings highlight the importance of a diet balanced in fatty acid content for normal brain functions and to support a link between dietary ALA, the brain endocannabinoid system, and behavior, which indicates that dietary ALA intake is a sufficient condition for altering the endocannabinoid system in brain regions modulating mood and cognition. Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed Central

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2014-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion. PMID:25422534

Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants in fish using fast, low-pressure gas chromatography-tandem mass spectrometry.

PubMed

Sapozhnikova, Yelena; Lehotay, Steven J

2013-01-03

A multi-class, multi-residue method for the analysis of 13 novel flame retardants, 18 representative pesticides, 14 polychlorinated biphenyl (PCB) congeners, 16 polycyclic aromatic hydrocarbons (PAHs), and 7 polybrominated diphenyl ether (PBDE) congeners in catfish muscle was developed and evaluated using fast low pressure gas chromatography triple quadrupole tandem mass spectrometry (LP-GC/MS-MS). The method was based on a QuEChERS (quick, easy, cheap, effective, rugged, safe) extraction with acetonitrile and dispersive solid-phase extraction (d-SPE) clean-up with zirconium-based sorbent prior to LP-GC/MS-MS analysis. The developed method was evaluated at 4 spiking levels and further validated by analysis of NIST Standard Reference Materials (SRMs) 1974B and 1947. Sample preparation for a batch of 10 homogenized samples took about 1h/analyst, and LP-GC/MS-MS analysis provided fast separation of multiple analytes within 9min achieving high throughput. With the use of isotopically labeled internal standards, recoveries of all but one analyte were between 70 and 120% with relative standard deviations less than 20% (n=5). The measured values for both SRMs agreed with certified/reference values (72-119% accuracy) for the majority of analytes. The detection limits were 0.1-0.5ng g(-1) for PCBs, 0.5-10ng g(-1) for PBDEs, 0.5-5ng g(-1) for select pesticides and PAHs and 1-10ng g(-1) for flame retardants. The developed method was successfully applied for analysis of catfish samples from the market. Published by Elsevier B.V.

Multi-class analysis of new psychoactive substances and metabolites in hair by pressurized liquid extraction coupled to HPLC-HRMS.

PubMed

Montesano, Camilla; Vannutelli, Gabriele; Massa, Maristella; Simeoni, Maria Chiara; Gregori, Adolfo; Ripani, Luigi; Compagnone, Dario; Curini, Roberta; Sergi, Manuel

2017-05-01

In this paper, an analytical method has been developed and validated for the analysis of new psychoactive substances (NPS) and metabolites in hair samples. The method was based on pressurized liquid extraction (PLE) followed by solid-phase extraction (SPE) clean-up and high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) analysis. To evaluate extraction efficiency and the applicability of the method, hair samples were fortified by soaking in order to obtain a good surrogate for drug users' hair; the amount of incorporated drugs related to their lipophilicity, similarly to in vivo drug incorporation. To the best of our knowledge, this is the first method that allowed for the analysis of both cathinones (5) and synthetic cannabinoids (7) in hair with a single extraction procedure and chromatographic run. A phenethylamine (2C-T-4), 4- fluorophenylpiperazine and methoxetamine were also included showing that PLE coupled to SPE clean-up was suitable for a multi-class analysis of NPS in hair. In addition, the use of PLE significantly reduced hair analysis time: decontamination, incubation, clean-up, and liquid chromatography-mass spectrometry (LC-MS) analysis were carried out in approximately 45 min. The method was fully validated according to Scientific Working Group for Forensic Toxicology (SWGTOX) and Society of Hair Testing (SoHT) guidelines. Limit of quantification (LOQ) values ranged from 8 to 50 pg mg -1 for cathinones, phenetylamines and piperazines, and from 9 to 40 pg mg -1 for synthetic cannabinoids (10 pg mg -1 for methoxetamine). Matrix effects were below 15% for all the analytes, demonstrating the effectiveness of the clean-up step. Inaccuracy was lower than 9% in terms of bias. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Simultaneous determination of seven multiclass veterinary antibiotics in surface water samples in the Republic of Korea using liquid chromatography with tandem mass spectrometry.

PubMed

Chung, Hyung Suk; Choi, Jeong-Heui; Abd El-Aty, A M; Lee, Young-Jun; Lee, Han Sol; Kim, Sangdon; Jung, Hee-Jung; Kang, Tae-Woo; Shin, Ho-Chul; Shim, Jae-Han

2016-12-01

A simultaneous determination method using solid-phase extraction and liquid chromatography with tandem mass spectrometry was developed to detect and quantify the presence of seven multiclass veterinary antibiotics (13 compounds in total) in surface water samples, which included the effluents of livestock wastewater and sewage treatment plants, as well as the reservoir drainage areas from dense animal farms. The pH of all water samples was adjusted to 2 or 6 before solid-phase extraction using Oasis HLB cartridges. The developed method was fully validated in terms of linearity, method detection limit, method quantitation limit, accuracy, and precision. The linearity of all tested drugs was good, with R 2 determination coefficients ≥ 0.9931. The method detection limits and method quantitation limits were 0.1-74.3 and 0.5-236.6 ng/L, respectively. Accuracy and precision values were 71-120 and 1-17%, respectively. The determination method was successfully applied for monitoring water samples obtained from the Yeongsan River in 2015. The most frequently detected antibiotics were lincomycin (96%), sulfamethazine (90%), sulfamethoxazole (88%), and sulfathiazole (50%); the maximum concentrations of which were 398.9, 1151.3, 533.1, and 307.4 ng/L, respectively. Overall, the greatest numbers and concentrations of detected antibiotics were found in samples from the effluents of livestock wastewater, sewage treatment plants, and reservoir drainage areas. Diverse veterinary antibiotics were present, and their presence was dependent upon the commercial sales and environmental properties of the analytes, the geographical positions of the sampling points, and the origin of the water. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number.

PubMed

Wong, Gerard; Leckie, Christopher; Kowalczyk, Adam

2012-01-15

Feature selection is a key concept in machine learning for microarray datasets, where features represented by probesets are typically several orders of magnitude larger than the available sample size. Computational tractability is a key challenge for feature selection algorithms in handling very high-dimensional datasets beyond a hundred thousand features, such as in datasets produced on single nucleotide polymorphism microarrays. In this article, we present a novel feature set reduction approach that enables scalable feature selection on datasets with hundreds of thousands of features and beyond. Our approach enables more efficient handling of higher resolution datasets to achieve better disease subtype classification of samples for potentially more accurate diagnosis and prognosis, which allows clinicians to make more informed decisions in regards to patient treatment options. We applied our feature set reduction approach to several publicly available cancer single nucleotide polymorphism (SNP) array datasets and evaluated its performance in terms of its multiclass predictive classification accuracy over different cancer subtypes, its speedup in execution as well as its scalability with respect to sample size and array resolution. Feature Set Reduction (FSR) was able to reduce the dimensions of an SNP array dataset by more than two orders of magnitude while achieving at least equal, and in most cases superior predictive classification performance over that achieved on features selected by existing feature selection methods alone. An examination of the biological relevance of frequently selected features from FSR-reduced feature sets revealed strong enrichment in association with cancer. FSR was implemented in MATLAB R2010b and is available at http://ww2.cs.mu.oz.au/~gwong/FSR.

Sorption and bioaccumulation behavior of multi-class hydrophobic organic contaminants in a tropical marine food web.

PubMed

Zhang, Hui; Kelly, Barry C

2018-05-01

While numerous studies have demonstrated the environmental behavior of legacy persistent organic pollutants (POPs), such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs), information regarding sorption and bioaccumulation potential of other widely used organic chemicals such as halogenated flame retardants (HFRs) is limited. This study involved a comprehensive field investigation of multi-class hydrophobic organic contaminants (HOCs) in environmental media and fish in Singapore Strait, an important tropical maritime strait in Southeast Asia. In total, 90 HOCs were analyzed, including HFRs, synthetic musks, PCBs, OCPs, as well as triclosan and methyl triclosan. The results show that the organic carbon normalized sediment-seawater distribution ratios (C SED /C WD ) of the studied compounds are comparable to the organic carbon-water partition coefficients (K OC ), over a log K OC range of approximately 4-11. The observed species-specific bioaccumulation factors (BAFs), biota-sediment accumulation factors (BSAFs), organism-environment media fugacity ratios (f FISH /f WD and f FISH /f SED ) and trophic magnification factors (TMFs) indicate that legacy POPs and PBDE 47 show bioaccumulation behavior in this tropical marine ecosystem, while triclosan, tonalide, dodecachlorodimethanodibenzocyclooctane stereoisomers (DDC-COs), and hexabromocyclododecanes (HBCDDs) do not. Methyl triclosan and galaxolide exhibit moderate biomagnification. Tetrabromobisphenol A (TBBPA) and 1,2-bis (2,4,6-tribromophenoxy)ethane (BTBPE) were detected in environmental media but not in any of the organisms, suggesting low bioaccumulation potential of these flame retardants. The apparently low bioaccumulation potential of the studied HFRs and synthetic musks is likely because of metabolic transformation and/or reduced bioavailability due to the hydrophobic nature of these compounds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications.

PubMed

Zhang, Yiyan; Xin, Yi; Li, Qin; Ma, Jianshe; Li, Shuai; Lv, Xiaodan; Lv, Weiqi

2017-11-02

Various kinds of data mining algorithms are continuously raised with the development of related disciplines. The applicable scopes and their performances of these algorithms are different. Hence, finding a suitable algorithm for a dataset is becoming an important emphasis for biomedical researchers to solve practical problems promptly. In this paper, seven kinds of sophisticated active algorithms, namely, C4.5, support vector machine, AdaBoost, k-nearest neighbor, naïve Bayes, random forest, and logistic regression, were selected as the research objects. The seven algorithms were applied to the 12 top-click UCI public datasets with the task of classification, and their performances were compared through induction and analysis. The sample size, number of attributes, number of missing values, and the sample size of each class, correlation coefficients between variables, class entropy of task variable, and the ratio of the sample size of the largest class to the least class were calculated to character the 12 research datasets. The two ensemble algorithms reach high accuracy of classification on most datasets. Moreover, random forest performs better than AdaBoost on the unbalanced dataset of the multi-class task. Simple algorithms, such as the naïve Bayes and logistic regression model are suitable for a small dataset with high correlation between the task and other non-task attribute variables. K-nearest neighbor and C4.5 decision tree algorithms perform well on binary- and multi-class task datasets. Support vector machine is more adept on the balanced small dataset of the binary-class task. No algorithm can maintain the best performance in all datasets. The applicability of the seven data mining algorithms on the datasets with different characteristics was summarized to provide a reference for biomedical researchers or beginners in different fields.

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2015-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion.

High Prevalence of HIV Drug Resistance Among Newly Diagnosed Infants Aged <18 Months: Results From a Nationwide Surveillance in Nigeria.

PubMed

Inzaule, Seth C; Osi, Samuels J; Akinbiyi, Gbenga; Emeka, Asadu; Khamofu, Hadiza; Mpazanje, Rex; Ilesanmi, Oluwafunke; Ndembi, Nicaise; Odafe, Solomon; Sigaloff, Kim C E; Rinke de Wit, Tobias F; Akanmu, Sulaimon

2018-01-01

WHO recommends protease-inhibitor-based first-line regimen in infants because of risk of drug resistance from failed prophylaxis used in prevention of mother-to-child transmission (PMTCT). However, cost and logistics impede implementation in sub-Saharan Africa, and >75% of children still receive nonnucleoside reverse transcriptase inhibitor-based regimen (NNRTI) used in PMTCT. We assessed the national pretreatment drug resistance prevalence of HIV-infected children aged <18 months in Nigeria, using WHO-recommended HIV drug resistance surveillance protocol. We used remnant dried blood spots collected between June 2014 and July 2015 from 15 early infant diagnosis facilities spread across all the 6 geopolitical regions of Nigeria. Sampling was through a probability proportional-to-size approach. HIV drug resistance was determined by population-based sequencing. Overall, in 48% of infants (205 of 430) drug resistance mutations (DRM) were detected, conferring resistance to predominantly NNRTIs (45%). NRTI and multiclass NRTI/NNRTI resistance were present at 22% and 20%, respectively, while resistance to protease inhibitors was at 2%. Among 204 infants with exposure to drugs for PMTCT, 57% had DRMs, conferring NNRTI resistance in 54% and multiclass NRTI/NNRTI resistance in 29%. DRMs were also detected in 34% of 132 PMTCT unexposed infants. A high frequency of PDR, mainly NNRTI-associated, was observed in a nationwide surveillance among newly diagnosed HIV-infected children in Nigeria. PDR prevalence was equally high in PMTCT-unexposed infants. Our results support the use of protease inhibitor-based first-line regimens in HIV-infected young children regardless of PMTCT history and underscore the need to accelerate implementation of the newly disseminated guideline in Nigeria.

Enhancing atlas based segmentation with multiclass linear classifiers

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

Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr

Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible localmore » registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.« less

Broad screening of illicit ingredients in cosmetics using ultra-high-performance liquid chromatography-hybrid quadrupole-Orbitrap mass spectrometry with customized accurate-mass database and mass spectral library.

PubMed

Meng, Xianshuang; Bai, Hua; Guo, Teng; Niu, Zengyuan; Ma, Qiang

2017-12-15

Comprehensive identification and quantitation of 100 multi-class regulated ingredients in cosmetics was achieved using ultra-high-performance liquid chromatography (UHPLC) coupled with hybrid quadrupole-Orbitrap high-resolution mass spectrometry (Q-Orbitrap HRMS). A simple, efficient, and inexpensive sample pretreatment protocol was developed using ultrasound-assisted extraction (UAE), followed by dispersive solid-phase extraction (dSPE). The cosmetic samples were analyzed by UHPLC-Q-Orbitrap HRMS under synchronous full-scan MS and data-dependent MS/MS (full-scan MS 1 /dd-MS 2 ) acquisition mode. The mass resolution was set to 70,000 FWHM (full width at half maximum) for full-scan MS 1 and 17,500 FWHM for dd-MS 2 stage with the experimentally measured mass deviations of less than 2ppm (parts per million) for quasi-molecular ions and 5ppm for characteristic fragment ions for each individual analyte. An accurate-mass database and a mass spectral library were built in house for searching the 100 target compounds. Broad screening was conducted by comparing the experimentally measured exact mass of precursor and fragment ions, retention time, isotopic pattern, and ionic ratio with the accurate-mass database and by matching the acquired MS/MS spectra against the mass spectral library. The developed methodology was evaluated and validated in terms of limits of detection (LODs), limits of quantitation (LOQs), linearity, stability, accuracy, and matrix effect. The UHPLC-Q-Orbitrap HRMS approach was applied for the analysis of 100 target illicit ingredients in 123 genuine cosmetic samples, and exhibited great potential for high-throughput, sensitive, and reliable screening of multi-class illicit compounds in cosmetics. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.

PubMed

Zhao, Jonathan Z L; Mucaki, Eliseos J; Rogan, Peter K

2018-01-01

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2 ,  PRKDC , TPP2 , PTPRE , and GADD45A ) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2 ,  CD8A ,  TALDO1 ,  PCNA ,  EIF4G2 ,  LCN2 ,  CDKN1A ,  PRKCH ,  ENO1 ,  and PPM1D ) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Gender imbalances in history: causes, consequences and social adjustment.

PubMed

Courtwright, David T

2008-03-01

This article surveys the causes, consequences and social adjustments of gender-imbalanced populations. Though recent studies emphasize the role of medical technology in creating gender imbalances, historical and biological evidence shows that they have deeper and more tangled roots. Female-selective abortion, sperm sorting and other innovations are essentially new wrinkles in a very old social problem. How past societies struggled with and rectified gender imbalances offers insight into how contemporary and future societies might do so--and into the price they will pay if they ignore the problem.

Possible Origin of Stagnation and Variability of Earth's Biodiversity

NASA Astrophysics Data System (ADS)

Stollmeier, Frank; Geisel, Theo; Nagler, Jan

2014-06-01

The magnitude and variability of Earth's biodiversity have puzzled scientists ever since paleontologic fossil databases became available. We identify and study a model of interdependent species where both endogenous and exogenous impacts determine the nonstationary extinction dynamics. The framework provides an explanation for the qualitative difference of marine and continental biodiversity growth. In particular, the stagnation of marine biodiversity may result from a global transition from an imbalanced to a balanced state of the species dependency network. The predictions of our framework are in agreement with paleontologic databases.

Dark solitons with Majorana fermions in spin-orbit-coupled Fermi gases.

PubMed

Xu, Yong; Mao, Li; Wu, Biao; Zhang, Chuanwei

2014-09-26

We show that a single dark soliton can exist in a spin-orbit-coupled Fermi gas with a high spin imbalance, where spin-orbit coupling favors uniform superfluids over nonuniform Fulde-Ferrell-Larkin-Ovchinnikov states, leading to dark soliton excitations in highly imbalanced gases. Above a critical spin imbalance, two topological Majorana fermions without interactions can coexist inside a dark soliton, paving a way for manipulating Majorana fermions through controlling solitons. At the topological transition point, the atom density contrast across the soliton suddenly vanishes, suggesting a signature for identifying topological solitons.

Chiral magnetic effect in lattice QCD with a chiral chemical potential.

PubMed

Yamamoto, Arata

2011-07-15

We perform a first lattice QCD simulation including a two-flavor dynamical fermion with a chiral chemical potential. Because the chiral chemical potential gives rise to no sign problem, we can exactly analyze a chirally imbalanced QCD matter by Monte Carlo simulation. By applying an external magnetic field to this system, we obtain a finite induced current along the magnetic field, which corresponds to the chiral magnetic effect. The obtained induced current is proportional to the magnetic field and to the chiral chemical potential, which is consistent with an analytical prediction.

[Formation and persistence of L-variants of Salmonella typhi in experimental typhoid and in carriers].

PubMed

Levina, G A; Prozorovskiĭ, S V; Iagud, S L; Grumman, M I; Gorelov, A L

1981-07-01

The possibility of the induction and persistence of S. typhi L-forms in the process of experimental typhoid infection and carriership has been studied in rabbits. This study has revealed that the process of L-transformation leading to the appearance of the imbalanced growth forms and unstable L-forms of S. typhi in the organism of the animals infected with S. typhi culture may occur under the conditions of carriership. Such changed forms can be detected in the organism of the animals 18 months after the primary infection.

Crimp-Imbalanced Protective (CRIMP) Fabrics: An Analytical Investigation into the Relationship Between Crimp Contents, Energy Absorption, and Fabric Ballistic Performance

DTIC Science & Technology

2010-09-15

migration and yarn stretching. These mechanisms relate to the force required to pull a yarn out from the fabric. If the fabric is made of low...the following assumptions were made : The fabric architecture is plain-woven. The yarns have a circular cross section with diameter D equal to 1.0... Bulletproof Aramid Fabric," Journal of Materials Science, vol. 32, pp. 4167-4173, 1997. 16. D. A. Shockey, D. C. Erlich, and J. W. Simons, "Improved

Count on kappa.

PubMed

Czodrowski, Paul

2014-11-01

In the 1960s, the kappa statistic was introduced for the estimation of chance agreement in inter- and intra-rater reliability studies. The kappa statistic was strongly pushed by the medical field where it could be successfully applied via analyzing diagnoses of identical patient groups. Kappa is well suited for classification tasks where ranking is not considered. The main advantage of kappa is its simplicity and the general applicability to multi-class problems which is the major difference to receiver operating characteristic area under the curve. In this manuscript, I will outline the usage of kappa for classification tasks, and I will evaluate the role and uses of kappa in specifically machine learning and cheminformatics.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Object recognition based on Google's reverse image search and image similarity

NASA Astrophysics Data System (ADS)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

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

Steganalysis using logistic regression

NASA Astrophysics Data System (ADS)

Lubenko, Ivans; Ker, Andrew D.

2011-02-01

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

Epoch Lifetimes in the Dynamics of a Competing Population

NASA Astrophysics Data System (ADS)

Yeung, C. H.; Ma, Y. P.; Wong, K. Y. Michael

We propose a dynamical model of a competing population whose agents have a tendency to balance their decisions in time. The model is applicable to financial markets in which the agents trade with finite capital, or other multiagent systems such as routers in communication networks attempting to transmit multiclass traffic in a fair way. We find an oscillatory behavior due to the segregation of agents into two groups. Each group remains winning over epochs. The aggregation of smart agents is able to explain the lifetime distribution of epochs to 8 decades of probability. The existence of the super agents further refines the lifetime distribution of short epochs.

CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA.

PubMed

Kang, Shuli; Li, Qingjiao; Chen, Quan; Zhou, Yonggang; Park, Stacy; Lee, Gina; Grimes, Brandon; Krysan, Kostyantyn; Yu, Min; Wang, Wei; Alber, Frank; Sun, Fengzhu; Dubinett, Steven M; Li, Wenyuan; Zhou, Xianghong Jasmine

2017-03-24

We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage.

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data

PubMed Central

Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

2016-01-01

The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew’s Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions. PMID:26861308

Evolving nutritional strategies in the presence of competition: a geometric agent-based model.

PubMed

Senior, Alistair M; Charleston, Michael A; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J

2015-03-01

Access to nutrients is a key factor governing development, reproduction and ultimately fitness. Within social groups, contest-competition can fundamentally affect nutrient access, potentially leading to reproductive asymmetry among individuals. Previously, agent-based models have been combined with the Geometric Framework of nutrition to provide insight into how nutrition and social interactions affect one another. Here, we expand this modelling approach by incorporating evolutionary algorithms to explore how contest-competition over nutrient acquisition might affect the evolution of animal nutritional strategies. Specifically, we model tolerance of nutrient excesses and deficits when ingesting nutritionally imbalanced foods, which we term 'nutritional latitude'; a higher degree of nutritional latitude constitutes a higher tolerance of nutritional excess and deficit. Our results indicate that a transition between two alternative strategies occurs at moderate to high levels of competition. When competition is low, individuals display a low level of nutritional latitude and regularly switch foods in search of an optimum. When food is scarce and contest-competition is intense, high nutritional latitude appears optimal, and individuals continue to consume an imbalanced food for longer periods before attempting to switch to an alternative. However, the relative balance of nutrients within available foods also strongly influences at what levels of competition, if any, transitions between these two strategies occur. Our models imply that competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth. We discuss how the integration of agent-based, nutritional and evolutionary modelling may be applied in future studies to further understand the evolution of nutritional strategies across social and ecological contexts.

Imbalanced nutrient regimes increase Prymnesium parvum resilience to herbicide exposure.

PubMed

Flood, Stacie L; Burkholder, JoAnn M

2018-05-01

The toxigenic haptophyte Prymnesium parvum is a mixotrophic phytoplankter with an extensive historic record of forming nearly monospecific, high-biomass, ecosystem-disrupting blooms, and it has been responsible for major fish kills in brackish waters and aquaculture facilities in many regions of the world. Little is known about how this species responds to commonly occurring environmental contaminants, or how nutrient (nitrogen, phosphorus) pollution may interact with environmentally relevant pesticide exposures to affect this harmful algal species. Here, standard algal toxicity bioassays from pesticide hazard assessments were used along with modified erythrocyte lysis assays to evaluate how atrazine exposures, imbalanced nutrient supplies, and salinity interact to influence the growth and toxicity in P. parvum isolates from three different regions. In nutrient-replete media, P. parvum 96 h IC 50 s ranged from 73.0 to 88.3 μg atrazine L -1 at salinity 10 and from 118 to >200 μg atrazine μg L -1 at salinity 20, and the response depended on the strain and the test duration. Relative hemolytic activity, used as an indication of toxicity, was a function of herbicide exposure, nutrient availability, salinity, geographic origin, and interactions among these factors. Highest levels of hemolytic activity were measured from a South Carolina strain in low-nitrogen media with high atrazine concentrations. Herbicide concentration was related to relative hemolytic activity, although a consistent relationship between growth phase and toxicity was not observed. Overall, these findings suggest that increasing chemical contamination is helping to promote ecosystem-disruptive, strongly mixotrophic algal blooms. Copyright © 2018 Elsevier B.V. All rights reserved.

Dynamic Echo Information Guides Flight in the Big Brown Bat

PubMed Central

Warnecke, Michaela; Lee, Wu-Jung; Krishnan, Anand; Moss, Cynthia F.

2016-01-01

Animals rely on sensory feedback from their environment to guide locomotion. For instance, visually guided animals use patterns of optic flow to control their velocity and to estimate their distance to objects (e.g., Srinivasan et al., 1991, 1996). In this study, we investigated how acoustic information guides locomotion of animals that use hearing as a primary sensory modality to orient and navigate in the dark, where visual information is unavailable. We studied flight and echolocation behaviors of big brown bats as they flew under infrared illumination through a corridor with walls constructed from a series of individual vertical wooden poles. The spacing between poles on opposite walls of the corridor was experimentally manipulated to create dense/sparse and balanced/imbalanced spatial structure. The bats’ flight trajectories and echolocation signals were recorded with high-speed infrared motion-capture cameras and ultrasound microphones, respectively. As bats flew through the corridor, successive biosonar emissions returned cascades of echoes from the walls of the corridor. The bats flew through the center of the corridor when the pole spacing on opposite walls was balanced and closer to the side with wider pole spacing when opposite walls had an imbalanced density. Moreover, bats produced shorter duration echolocation calls when they flew through corridors with smaller spacing between poles, suggesting that clutter density influences features of the bat’s sonar signals. Flight speed and echolocation call rate did not, however, vary with dense and sparse spacing between the poles forming the corridor walls. Overall, these data demonstrate that bats adapt their flight and echolocation behavior dynamically when flying through acoustically complex environments. PMID:27199690

Microsaccade production during saccade cancelation in a stop-signal task

PubMed Central

Godlove, David C.; Schall, Jeffrey D.

2014-01-01

We obtained behavioral data to evaluate two alternative hypotheses about the neural mechanisms of gaze control. The “fixation” hypothesis states that neurons in rostral superior colliculus (SC) enforce fixation of gaze. The “microsaccade” hypothesis states that neurons in rostral SC encode microsaccades rather than fixation per se. Previously reported neuronal activity in monkey SC during the saccade stop-signal task leads to specific, dissociable behavioral predictions of these two hypotheses. When subjects are required to cancel partially-prepared saccades, imbalanced activity spreads across rostral and caudal SC with a reliable temporal profile. The microsaccade hypothesis predicts that this imbalance will lead to elevated microsaccade production biased toward the target location, while the fixation hypothesis predicts reduced microsaccade production. We tested these predictions by analyzing the microsaccades produced by 4 monkeys while they voluntarily canceled partially prepared eye movements in response to explicit stop signals. Consistent with the fixation hypothesis and contradicting the microsaccade hypothesis, we found that each subject produced significantly fewer microsaccades when normal saccades were successfully canceled. The few microsaccades escaping this inhibition tended to be directed toward the target location. We additionally investigated interactions between initiating microsaccades and inhibiting normal saccades. Reaction times were longer when microsaccades immediately preceded target presentation. However, pre-target microsaccade production did not affect stop-signal reaction time or alter the probability of canceling saccades following stop signals. These findings demonstrate that imbalanced activity within SC does not necessarily produce microsaccades and add to evidence that saccade preparation and cancelation are separate processes. PMID:25448116

Geometric analysis of nutrient balancing in the mealworm beetle, Tenebrio molitor L. (Coleoptera: Tenebrionidae).

PubMed

Rho, Myung Suk; Lee, Kwang Pum

2014-12-01

Geometric analysis of the nutritional regulatory responses was performed on an omnivorous mealworm beetle, Tenebrio molitor L. (Coleoptera: Tenebrionidae) to test whether this beetle had the capacity to balance the intake of protein and carbohydrate. We also identified the pattern of ingestive trade-off employed when the insect was forced to balance the costs of over- and under-ingesting macronutrients. When allowed to mix their diet from two nutritionally imbalanced but complementary foods (protein-biased food: p35:c7 or p28:c5.6; carbohydrate-biased food: p7:c35 or p5.6:c28), beetles of both sexes actively regulated their intake of protein and carbohydrate to a ratio of 1:1. When confined to one of seven nutritionally imbalanced foods (p0:c42, p7:c35, p14:c28, p21:c21, p28:c14, p35:c7 or p42:c0), beetles over-ingested the excessive nutrient from these foods to such an extent that all the points of protein-carbohydrate intake aligned linearly in the nutrient space, a pattern that is characteristic of generalist feeders and omnivores. Under the restricted feeding conditions, males ate more nutrients but were less efficient at retaining their body lipids than females. Body lipid content was higher on carbohydrate-rich foods and was positively correlated with starvation resistance. Our results are consistent with the prediction based on the nutritional heterogeneity hypothesis, which links the nutritional regulatory responses of insects to their diet breadth and feeding ecology. Copyright © 2014. Published by Elsevier Ltd.

Educing the emission mechanism of internal gravity waves in the differentially heat rotating annulus

NASA Astrophysics Data System (ADS)

Rolland, Joran; Hien, Steffen; Achatz, Ulrich; Borchert, Sebastian; Fruman, Mark

2016-04-01

Understanding the lifecycle of gravity waves is fundamental to a good comprehension of the dynamics of the atmosphere. In this lifecycle, the emission mechanisms may be the most elusive. Indeed, while the emission of gravity waves by orography or convection is well understood, the so-called spontaneous emission is still a quite open topic of investigation [1]. This type of emission usually occur very near jet-front systems in the troposphere. In this abstract, we announce our numerical study of the question. Model systems of the atmosphere which can be easily simulated or built in a laboratory have always been an important part of the study of atmospheric dynamics, alongside global simulations, in situ measurements and theory. In the case of the study of the spontaneous emission of gravity waves near jet-front systems, the differentially heated rotating annulus set up has been proposed and extensively used. It comprises of an annular tank containing water: the inner cylinder is kept at a cold temperature while the outer cylinder is kept at a warm temperature. The whole system is rotating. Provided the values of the control parameters (temperature, rotation rate, gap between the cylinders, height of water) are well chosen, the resulting flow mimics the troposphere at midlatitudes: it has a jet stream, and a baroclinic lifecycle develops on top of it. A very reasonable ratio of Brunt-Väisälä frequency over rotation rate of the system can be obtained, so as to be as close to the atmosphere as possible. Recent experiments as well as earlier numerical simulations in our research group have shown that gravity waves are indeed emitted in this set up, in particular near the jet front system of the baroclinic wave [2]. After a first experimental stage of characterising the emitted wavepacket, we focused our work on testing hypotheses on the gravity wave emission mechanism: we have tested and validated the hypothesis of spontaneous imbalance generated by the flow in geostrophic balance. For the first stage of this investigation, we separated the flow between a balance and an imbalanced part at first order in Rossby number: the balanced pressure field was computed through an inversion of the potential vorticity equation [3]. The balanced horizontal velocity field and buoyancy were then computed using the geostrophic and hydrostatic balance conditions. We first checked that this decomposition gave on the one hand a large scaled balanced flow, comprising mostly of the baroclinic wave, and on the other hand a small scale flow comprising mostly of the gravity wave signal. We then proceeded with the central stage of the validation: we simulated the tangent linear dynamics of the imbalanced part of the flow [4]. The equations are linearised about the balanced part, and any imbalances forces the modeled imbalanced part. The output of this simulation compares very well with the actual imbalanced part, thus confirming that the observed gravity waves are indeed generated through spontaneous imbalance. To our knowledge, this is the first demonstration of emission by this mechanism in a flow which is not idealised: a flow which can be obtained as a result of a numerical simulation of primitive equations or actually observed in a laboratory experiment. References [1] R. Plougonven, F. Zhang, Internal gravity waves from atmospheric jets and fronts, Rev. Geophys. 52, 33-76 (2014). [2] S. Borchert, U. Achatz, M.D. Fruman, Spontaneous Gravity wave emission in the differentially heated annulus, J. Fluid Mech. 758, 287-311 (2014). [3] F. Zhang, S.E . Koch, C. A. Davis, M. L. Kaplan, A Survey of unbalanced flow diagnostics and their application, Adv. Atmo. Sci. 17, 165-183 (2000). [4] S. Wang, F. Zhang, Source of gravity waves within a vortex dipole jet revealed by a linear model, J. Atmo. Sci. 67, 1438-1455 (2010).

International nursing exchange and collaboration with China: a perspective from the South and the East.

PubMed

Xu, Yu

2012-01-01

There are an imbalanced world power relationships and international knowledge system, as well as cultural differences across nations. Based on the author's international experiences, this article describes the needs and motivations of international exchange and collaboration in nursing from the perspective of both China and Western countries, examines the ethical and cultural issues involved, and suggests winning strategies. Western educators and scholars must keep these issues and strategies in mind in order to build a productive, mutually beneficial, and sustainable international exchanges and collaboration. © 2012 Wiley Periodicals, Inc.

Mitochondrial deoxyribonucleoside triphosphate pools in thymidine kinase 2 deficiency.

PubMed

Saada, Ann; Ben-Shalom, Efrat; Zyslin, Rivka; Miller, Chaya; Mandel, Hanna; Elpeleg, Orly

2003-10-24

Deficiency of mitochondrial thymidine kinase (TK2) is associated with mitochondrial DNA (mtDNA) depletion and manifests by severe skeletal myopathy in infancy. In order to elucidate the pathophysiology of this condition, mitochondrial deoxyribonucleoside triphosphate (dNTP) pools were determined in patients' fibroblasts. Despite normal mtDNA content and cytochrome c oxidase (COX) activity, mitochondrial dNTP pools were imbalanced. Specifically, deoxythymidine triphosphate (dTTP) content was markedly decreased, resulting in reduced dTTP:deoxycytidine triphosphate ratio. These findings underline the importance of balanced mitochondrial dNTP pools for mtDNA synthesis and may serve as the basis for future therapeutic interventions.

Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.

PubMed

Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong

2018-01-01

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.