Input Decimated Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2001-01-01

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Neural networks for vertical microcode compaction

NASA Astrophysics Data System (ADS)

Chu, Pong P.

1992-09-01

Neural networks provide an alternative way to solve complex optimization problems. Instead of performing a program of instructions sequentially as in a traditional computer, neural network model explores many competing hypotheses simultaneously using its massively parallel net. The paper shows how to use the neural network approach to perform vertical micro-code compaction for a micro-programmed control unit. The compaction procedure includes two basic steps. The first step determines the compatibility classes and the second step selects a minimal subset to cover the control signals. Since the selection process is an NP- complete problem, to find an optimal solution is impractical. In this study, we employ a customized neural network to obtain the minimal subset. We first formalize this problem, and then define an `energy function' and map it to a two-layer fully connected neural network. The modified network has two types of neurons and can always obtain a valid solution.

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.

The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity.

PubMed

Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko

2008-06-01

One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.

PubMed

Koutsoukas, Alexios; Paricharak, Shardul; Galloway, Warren R J D; Spring, David R; Ijzerman, Adriaan P; Glen, Robert C; Marcus, David; Bender, Andreas

2014-01-27

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.

Medical students perceive better group learning processes when large classes are made to seem small.

PubMed

Hommes, Juliette; Arah, Onyebuchi A; de Grave, Willem; Schuwirth, Lambert W T; Scherpbier, Albert J J A; Bos, Gerard M J

2014-01-01

Medical schools struggle with large classes, which might interfere with the effectiveness of learning within small groups due to students being unfamiliar to fellow students. The aim of this study was to assess the effects of making a large class seem small on the students' collaborative learning processes. A randomised controlled intervention study was undertaken to make a large class seem small, without the need to reduce the number of students enrolling in the medical programme. The class was divided into subsets: two small subsets (n=50) as the intervention groups; a control group (n=102) was mixed with the remaining students (the non-randomised group n∼100) to create one large subset. The undergraduate curriculum of the Maastricht Medical School, applying the Problem-Based Learning principles. In this learning context, students learn mainly in tutorial groups, composed randomly from a large class every 6-10 weeks. The formal group learning activities were organised within the subsets. Students from the intervention groups met frequently within the formal groups, in contrast to the students from the large subset who hardly enrolled with the same students in formal activities. Three outcome measures assessed students' group learning processes over time: learning within formally organised small groups, learning with other students in the informal context and perceptions of the intervention. Formal group learning processes were perceived more positive in the intervention groups from the second study year on, with a mean increase of β=0.48. Informal group learning activities occurred almost exclusively within the subsets as defined by the intervention from the first week involved in the medical curriculum (E-I indexes>-0.69). Interviews tapped mainly positive effects and negligible negative side effects of the intervention. Better group learning processes can be achieved in large medical schools by making large classes seem small.

Medical Students Perceive Better Group Learning Processes when Large Classes Are Made to Seem Small

PubMed Central

Hommes, Juliette; Arah, Onyebuchi A.; de Grave, Willem; Schuwirth, Lambert W. T.; Scherpbier, Albert J. J. A.; Bos, Gerard M. J.

2014-01-01

Objective Medical schools struggle with large classes, which might interfere with the effectiveness of learning within small groups due to students being unfamiliar to fellow students. The aim of this study was to assess the effects of making a large class seem small on the students' collaborative learning processes. Design A randomised controlled intervention study was undertaken to make a large class seem small, without the need to reduce the number of students enrolling in the medical programme. The class was divided into subsets: two small subsets (n = 50) as the intervention groups; a control group (n = 102) was mixed with the remaining students (the non-randomised group n∼100) to create one large subset. Setting The undergraduate curriculum of the Maastricht Medical School, applying the Problem-Based Learning principles. In this learning context, students learn mainly in tutorial groups, composed randomly from a large class every 6–10 weeks. Intervention The formal group learning activities were organised within the subsets. Students from the intervention groups met frequently within the formal groups, in contrast to the students from the large subset who hardly enrolled with the same students in formal activities. Main Outcome Measures Three outcome measures assessed students' group learning processes over time: learning within formally organised small groups, learning with other students in the informal context and perceptions of the intervention. Results Formal group learning processes were perceived more positive in the intervention groups from the second study year on, with a mean increase of β = 0.48. Informal group learning activities occurred almost exclusively within the subsets as defined by the intervention from the first week involved in the medical curriculum (E-I indexes>−0.69). Interviews tapped mainly positive effects and negligible negative side effects of the intervention. Conclusion Better group learning processes can be achieved in large medical schools by making large classes seem small. PMID:24736272

Circulating B cells in type 1 diabetics exhibit fewer maturation-associated phenotypes.

PubMed

Hanley, Patrick; Sutter, Jennifer A; Goodman, Noah G; Du, Yangzhu; Sekiguchi, Debora R; Meng, Wenzhao; Rickels, Michael R; Naji, Ali; Luning Prak, Eline T

2017-10-01

Although autoantibodies have been used for decades as diagnostic and prognostic markers in type 1 diabetes (T1D), further analysis of developmental abnormalities in B cells could reveal tolerance checkpoint defects that could improve individualized therapy. To evaluate B cell developmental progression in T1D, immunophenotyping was used to classify circulating B cells into transitional, mature naïve, mature activated, and resting memory subsets. Then each subset was analyzed for the expression of additional maturation-associated markers. While the frequencies of B cell subsets did not differ significantly between patients and controls, some T1D subjects exhibited reduced proportions of B cells that expressed transmembrane activator and CAML interactor (TACI) and Fas receptor (FasR). Furthermore, some T1D subjects had B cell subsets with lower frequencies of class switching. These results suggest circulating B cells exhibit variable maturation phenotypes in T1D. These phenotypic variations may correlate with differences in B cell selection in individual T1D patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Chemical library subset selection algorithms: a unified derivation using spatial statistics.

PubMed

Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F

2002-01-01

If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

Sparse Zero-Sum Games as Stable Functional Feature Selection

PubMed Central

Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel

2015-01-01

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268

Depletion of CD8+ cells in human thymic medulla results in selective immune deficiency

PubMed Central

1989-01-01

CD8 molecules expressed on the surface of a subset of T cells participate in the selection of class I MHC antigen-restricted T cells in the thymus, and in MHC-restricted immune responses of mature class I MHC antigen-restricted T cells. Here we describe an immune-deficient patient with lack of CD8+ peripheral blood cells. The patient presented with Pneumocystis carinii pneumonia and was unable to reject an allogeneic skin graft, but had normal primary and secondary antibody responses. Examination of the patient's thymus revealed that the loss of CD8+ cells occurred during intrathymic differentiation: the patient's immature cortical thymocytes included both CD4+ and CD8+ cells while the mature medullary cells expressed the CD4 but not the CD8 protein on their surface. Northern blot and polymerase chain reaction analyses revealed the presence of CD8 alpha and beta mRNA in the patient's thymus but not in the peripheral blood. Both class I MHC antigen expression and the expressed TCR V beta repertoire are normal in this patient. These data are consistent with an impaired selection of CD8+ cells in the patient's thymus and support the role of the CD8 surface protein in thymic selection previously characterized in genetically manipulated and inbred mice. PMID:2511270

Universality Classes of Interaction Structures for NK Fitness Landscapes

NASA Astrophysics Data System (ADS)

Hwang, Sungmin; Schmiegelt, Benjamin; Ferretti, Luca; Krug, Joachim

2018-07-01

Kauffman's NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k ≤ L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.

Universality Classes of Interaction Structures for NK Fitness Landscapes

NASA Astrophysics Data System (ADS)

Hwang, Sungmin; Schmiegelt, Benjamin; Ferretti, Luca; Krug, Joachim

2018-02-01

Kauffman's NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k ≤ L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.

Screening for oral potentially malignant disorders among areca (betel) nut chewers in Guam and Saipan.

PubMed

Paulino, Yvette C; Hurwitz, Eric L; Warnakulasuriya, Saman; Gatewood, Robert R; Pierson, Kenneth D; Tenorio, Lynnette F; Novotny, Rachel; Palafox, Neal A; Wilkens, Lynne R; Badowski, Grazyna

2014-12-11

The Mariana Islands, including Guam and Saipan, are home to many ethnic subpopulations of Micronesia. Oral cancer incidence rates vary among subpopulations, and areca (betel) nut chewing, a habit with carcinogenic risks, is common. Our objectives were to conduct a screening program to detect oral potentially malignant disorders (OPMD) in betel nut chewers, measure their betel nut chewing practices, and assess the prevalence of the oral human papillomavirus (HPV) infection in a subset of betel nut chewers in these islands. A cross-section of 300 betel nut chewers ≥18 years old [in Guam (n = 137) and in Saipan (n = 163)] were recruited between January 2011-June 2012. We collected demographic, socioeconomic, and oral behavioural characteristics. Latent class analysis was used to identify chewing patterns from selected chewing behaviours. Following calibration of OPMD against an expert, a registered oral hygienist conducted oral examinations by house to house visits and referred positive cases to the study dentist for a second oral examination. Buccal smears were collected from a subset (n = 123) for HPV testing. Two classes of betel nut chewers were identified on 7 betel nut behaviours, smoking, and alcohol use; a key difference between the two Classes was the addition of ingredients to the betel quid among those in Class 2. When compared on other characteristics, Class 1 chewers were older, had been chewing for more years, and chewed fewer nuts per day although chewing episodes lasted longer than Class 2 chewers. More Class 1 chewers visited the dentist regularly than Class 2 chewers. Of the 300 participants, 46 (15.3%; 3.8% for Class 1 and 19.4% for Class 2) had OPMD and one (0.3%) was confirmed to have squamous cell carcinoma. The prevalence of oral HPV was 5.7% (7/123), although none were high-risk types. We found two patterns of betel nut chewing behaviour; Class 2 had a higher frequency of OPMD. Additional epidemiologic research is needed to examine the relationship between pattern of chewing behaviours and oral cancer incidence. Based on risk stratification, oral screening in Guam and Saipan can be targeted to Class 2 chewers.

Biochemical Sensors Using Carbon Nanotube Arrays

NASA Technical Reports Server (NTRS)

Meyyappan, Meyya (Inventor); Cassell, Alan M. (Inventor); Li, Jun (Inventor)

2011-01-01

Method and system for detecting presence of biomolecules in a selected subset, or in each of several selected subsets, in a fluid. Each of an array of two or more carbon nanotubes ("CNTs") is connected at a first CNT end to one or more electronics devices, each of which senses a selected electrochemical signal that is generated when a target biomolecule in the selected subset becomes attached to a functionalized second end of the CNT, which is covalently bonded with a probe molecule. This approach indicates when target biomolecules in the selected subset are present and indicates presence or absence of target biomolecules in two or more selected subsets. Alternatively, presence of absence of an analyte can be detected.

Design of a general-purpose European compound screening library for EU-OPENSCREEN.

PubMed

Horvath, Dragos; Lisurek, Michael; Rupp, Bernd; Kühne, Ronald; Specker, Edgar; von Kries, Jens; Rognan, Didier; Andersson, C David; Almqvist, Fredrik; Elofsson, Mikael; Enqvist, Per-Anders; Gustavsson, Anna-Lena; Remez, Nikita; Mestres, Jordi; Marcou, Gilles; Varnek, Alexander; Hibert, Marcel; Quintana, Jordi; Frank, Ronald

2014-10-01

This work describes a collaborative effort to define and apply a protocol for the rational selection of a general-purpose screening library, to be used by the screening platforms affiliated with the EU-OPENSCREEN initiative. It is designed as a standard source of compounds for primary screening against novel biological targets, at the request of research partners. Given the general nature of the potential applications of this compound collection, the focus of the selection strategy lies on ensuring chemical stability, absence of reactive compounds, screening-compliant physicochemical properties, loose compliance to drug-likeness criteria (as drug design is a major, but not exclusive application), and maximal diversity/coverage of chemical space, aimed at providing hits for a wide spectrum of drugable targets. Finally, practical availability/cost issues cannot be avoided. The main goal of this publication is to inform potential future users of this library about its conception, sources, and characteristics. The outline of the selection procedure, notably of the filtering rules designed by a large committee of European medicinal chemists and chemoinformaticians, may be of general methodological interest for the screening/medicinal chemistry community. The selection task of 200K molecules out of a pre-filtered set of 1.4M candidates was shared by five independent European research groups, each picking a subset of 40K compounds according to their own in-house methodology and expertise. An in-depth analysis of chemical space coverage of the library serves not only to characterize the collection, but also to compare the various chemoinformatics-driven selection procedures of maximal diversity sets. Compound selections contributed by various participating groups were mapped onto general-purpose self-organizing maps (SOMs) built on the basis of marketed drugs and bioactive reference molecules. In this way, the occupancy of chemical space by the EU-OPENSCREEN library could be directly compared with distributions of known bioactives of various classes. This mapping highlights the relevance of the selection and shows how the consensus reached by merging the five different 40K selections contributes to achieve this relevance. The approach also allows one to readily identify subsets of target- or target-class-oriented compounds from the EU-OPENSCREEN library to suit the needs of the diverse range of potential users. The final EU-OPENSCREEN library, assembled by merging five independent selections of 40K compounds from various expert groups, represents an excellent example of a Europe-wide collaborative effort toward the common objective of building best-in-class European open screening platforms. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Data-driven confounder selection via Markov and Bayesian networks.

PubMed

Häggström, Jenny

2018-06-01

To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder selection criteria can be used to select subsets of the observed pretreatment covariates, X, sufficient for unconfoundedness, if such subsets exist. Here, estimation of these target subsets is considered when the underlying causal structure is unknown. The proposed method is to model the causal structure by a probabilistic graphical model, for example, a Markov or Bayesian network, estimate this graph from observed data and select the target subsets given the estimated graph. The approach is evaluated by simulation both in a high-dimensional setting where unconfoundedness holds given X and in a setting where unconfoundedness only holds given subsets of X. Several common target subsets are investigated and the selected subsets are compared with respect to accuracy in estimating the average causal effect. The proposed method is implemented with existing software that can easily handle high-dimensional data, in terms of large samples and large number of covariates. The results from the simulation study show that, if unconfoundedness holds given X, this approach is very successful in selecting the target subsets, outperforming alternative approaches based on random forests and LASSO, and that the subset estimating the target subset containing all causes of outcome yields smallest MSE in the average causal effect estimation. © 2017, The International Biometric Society.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Diversification of C. elegans Motor Neuron Identity via Selective Effector Gene Repression.

PubMed

Kerk, Sze Yen; Kratsios, Paschalis; Hart, Michael; Mourao, Romulo; Hobert, Oliver

2017-01-04

A common organizational feature of nervous systems is the existence of groups of neurons that share common traits but can be divided into individual subtypes based on anatomical or molecular features. We elucidate the mechanistic basis of neuronal diversification processes in the context of C.elegans ventral cord motor neurons that share common traits that are directly activated by the terminal selector UNC-3. Diversification of motor neurons into different classes, each characterized by unique patterns of effector gene expression, is controlled by distinct combinations of phylogenetically conserved, class-specific transcriptional repressors. These repressors are continuously required in postmitotic neurons to prevent UNC-3, which is active in all neuron classes, from activating class-specific effector genes in specific motor neuron subsets via discrete cis-regulatory elements. The strategy of antagonizing the activity of broadly acting terminal selectors of neuron identity in a subtype-specific fashion may constitute a general principle of neuron subtype diversification. Copyright © 2017 Elsevier Inc. All rights reserved.

Texture analysis based on the Hermite transform for image classification and segmentation

NASA Astrophysics Data System (ADS)

Estudillo-Romero, Alfonso; Escalante-Ramirez, Boris; Savage-Carmona, Jesus

2012-06-01

Texture analysis has become an important task in image processing because it is used as a preprocessing stage in different research areas including medical image analysis, industrial inspection, segmentation of remote sensed imaginary, multimedia indexing and retrieval. In order to extract visual texture features a texture image analysis technique is presented based on the Hermite transform. Psychovisual evidence suggests that the Gaussian derivatives fit the receptive field profiles of mammalian visual systems. The Hermite transform describes locally basic texture features in terms of Gaussian derivatives. Multiresolution combined with several analysis orders provides detection of patterns that characterizes every texture class. The analysis of the local maximum energy direction and steering of the transformation coefficients increase the method robustness against the texture orientation. This method presents an advantage over classical filter bank design because in the latter a fixed number of orientations for the analysis has to be selected. During the training stage, a subset of the Hermite analysis filters is chosen in order to improve the inter-class separability, reduce dimensionality of the feature vectors and computational cost during the classification stage. We exhaustively evaluated the correct classification rate of real randomly selected training and testing texture subsets using several kinds of common used texture features. A comparison between different distance measurements is also presented. Results of the unsupervised real texture segmentation using this approach and comparison with previous approaches showed the benefits of our proposal.

Dimensionality Reduction Through Classifier Ensembles

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

1999-01-01

In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

The Ia.2 Epitope Defines a Subset of Lipid Raft Resident MHC Class II Molecules Crucial to Effective Antigen Presentation1

PubMed Central

Busman-Sahay, Kathleen; Sargent, Elizabeth; Harton, Jonathan A.; Drake, James R.

2016-01-01

Previous work has established that binding of the 11-5.2 anti-I-Ak mAb, which recognizes the Ia.2 epitope on I-Ak class II molecules, elicits MHC class II signaling, whereas binding of two other anti-I-Ak mAb that recognize the Ia.17 epitope fail to elicit signaling. Using a biochemical approach, we establish that the Ia.2 epitope recognized by the widely used 11-5.2 mAb defines a subset of cell surface I-Ak molecules predominantly found within membrane lipid rafts. Functional studies demonstrate that the Ia.2 bearing subset of I-Ak class II molecules is critically necessary for effective B cell–T cell interactions especially at low antigen doses, a finding consistent with published studies on the role of raft-resident class II molecules in CD4 T cell activation. Interestingly, B cells expressing recombinant I-Ak class II molecules possessing a β chain-tethered HEL peptide lack the Ia.2 epitope and fail to partition into lipid rafts. Moreover, cells expressing Ia.2 negative tethered peptide-class II molecules are severely impaired in their ability to present both tethered peptide or peptide derived from exogenous antigen to CD4 T cells. These results establish the Ia.2 epitope as defining a lipid raft-resident MHC class II confomer vital to the initiation of MHC class II restricted B cell–T cell interactions. PMID:21543648

A genetic algorithm-based framework for wavelength selection on sample categorization.

PubMed

Anzanello, Michel J; Yamashita, Gabrielli; Marcelo, Marcelo; Fogliatto, Flávio S; Ortiz, Rafael S; Mariotti, Kristiane; Ferrão, Marco F

2017-08-01

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

PubMed

Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

2014-03-01

The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

TWO CRITERIA FOR WEAK GENERALIZED LOCALIZATION FOR MULTIPLE TRIGONOMETRIC FOURIER SERIES OF FUNCTIONS IN L_p, p \\ge 1

NASA Astrophysics Data System (ADS)

Bloshanskiĭ, I. L.

1986-04-01

The concept of weak generalized localization almost everywhere is introduced. For the multiple Fourier series of a function f, weak generalized localization almost everywhere holds on the set E (E is an arbitrary set of positive measure E \\subset T^N = \\lbrack- \\pi, \\pi\\rbrack^N) if the condition f(x) \\in L_p(T^N), p \\ge 1, f = 0 on E implies that the indicated series converges almost everywhere on some subset E_1 \\subset E of positive measure. For a large class of sets \\{ E \\}, E \\subset T^N, a number of propositions are proved showing that weak localization of rectangular sums holds on the set E in the classes L_p, p \\ge 1, if and only if the set E has certain specific properties. In the course of the proof the precise geometry and structure of the subset E_1 of E on which the multiple Fourier series converges almost everywhere to zero are determined. Bibliography: 13 titles.

LS Bound based gene selection for DNA microarray data.

PubMed

Zhou, Xin; Mao, K Z

2005-04-15

One problem with discriminant analysis of DNA microarray data is that each sample is represented by quite a large number of genes, and many of them are irrelevant, insignificant or redundant to the discriminant problem at hand. Methods for selecting important genes are, therefore, of much significance in microarray data analysis. In the present study, a new criterion, called LS Bound measure, is proposed to address the gene selection problem. The LS Bound measure is derived from leave-one-out procedure of LS-SVMs (least squares support vector machines), and as the upper bound for leave-one-out classification results it reflects to some extent the generalization performance of gene subsets. We applied this LS Bound measure for gene selection on two benchmark microarray datasets: colon cancer and leukemia. We also compared the LS Bound measure with other evaluation criteria, including the well-known Fisher's ratio and Mahalanobis class separability measure, and other published gene selection algorithms, including Weighting factor and SVM Recursive Feature Elimination. The strength of the LS Bound measure is that it provides gene subsets leading to more accurate classification results than the filter method while its computational complexity is at the level of the filter method. A companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/. The website contains: (1) the source code of the gene selection algorithm; (2) the complete set of tables and figures regarding the experimental study; (3) proof of the inequality (9). ekzmao@ntu.edu.sg.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

PubMed

Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

2014-10-01

Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.

Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

2016-11-01

Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

Multiobjective optimization of combinatorial libraries.

PubMed

Agrafiotis, D K

2002-01-01

Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.

Eye movement analysis for activity recognition using electrooculography.

PubMed

Bulling, Andreas; Ward, Jamie A; Gellersen, Hans; Tröster, Gerhard

2011-04-01

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

Physiological IgM Class Catalytic Antibodies Selective for Transthyretin Amyloid*

PubMed Central

Planque, Stephanie A.; Nishiyama, Yasuhiro; Hara, Mariko; Sonoda, Sari; Murphy, Sarah K.; Watanabe, Kenji; Mitsuda, Yukie; Brown, Eric L.; Massey, Richard J.; Primmer, Stanley R.; O'Nuallain, Brian; Paul, Sudhir

2014-01-01

Peptide bond-hydrolyzing catalytic antibodies (catabodies) could degrade toxic proteins, but acquired immunity principles have not provided evidence for beneficial catabodies. Transthyretin (TTR) forms misfolded β-sheet aggregates responsible for age-associated amyloidosis. We describe nucleophilic catabodies from healthy humans without amyloidosis that degraded misfolded TTR (misTTR) without reactivity to the physiological tetrameric TTR (phyTTR). IgM class B cell receptors specifically recognized the electrophilic analog of misTTR but not phyTTR. IgM but not IgG class antibodies hydrolyzed the particulate and soluble misTTR species. No misTTR-IgM binding was detected. The IgMs accounted for essentially all of the misTTR hydrolytic activity of unfractionated human serum. The IgMs did not degrade non-amyloidogenic, non-superantigenic proteins. Individual monoclonal IgMs (mIgMs) expressed variable misTTR hydrolytic rates and differing oligoreactivity directed to amyloid β peptide and microbial superantigen proteins. A subset of the mIgMs was monoreactive for misTTR. Excess misTTR was dissolved by a hydrolytic mIgM. The studies reveal a novel antibody property, the innate ability of IgMs to selectively degrade and dissolve toxic misTTR species as a first line immune function. PMID:24648510

Langerin+ dermal dendritic cells are critical for CD8+ T cell activation and IgH γ-1 class switching in response to gene gun vaccines.

PubMed

Stoecklinger, Angelika; Eticha, Tekalign D; Mesdaghi, Mehrnaz; Kissenpfennig, Adrien; Malissen, Bernard; Thalhamer, Josef; Hammerl, Peter

2011-02-01

The C-type lectin langerin/CD207 was originally discovered as a specific marker for epidermal Langerhans cells (LC). Recently, additional and distinct subsets of langerin(+) dendritic cells (DC) have been identified in lymph nodes and peripheral tissues of mice. Although the role of LC for immune activation or modulation is now being discussed controversially, other langerin(+) DC appear crucial for protective immunity in a growing set of infection and vaccination models. In knock-in mice that express the human diphtheria toxin receptor under control of the langerin promoter, injection of diphtheria toxin ablates LC for several weeks whereas other langerin(+) DC subsets are replenished within just a few days. Thus, by careful timing of diphtheria toxin injections selective states of deficiency in either LC only or all langerin(+) cells can be established. Taking advantage of this system, we found that, unlike selective LC deficiency, ablation of all langerin(+) DC abrogated the activation of IFN-γ-producing and cytolytic CD8(+) T cells after gene gun vaccination. Moreover, we identified migratory langerin(+) dermal DC as the subset that directly activated CD8(+) T cells in lymph nodes. Langerin(+) DC were also critical for IgG1 but not IgG2a Ab induction, suggesting differential polarization of CD4(+) T helper cells by langerin(+) or langerin-negative DC, respectively. In contrast, protein vaccines administered with various adjuvants induced IgG1 independently of langerin(+) DC. Taken together, these findings reflect a highly specialized division of labor between different DC subsets both with respect to Ag encounter as well as downstream processes of immune activation.

Cerebellins are differentially expressed in selective subsets of neurons throughout the brain.

PubMed

Seigneur, Erica; Südhof, Thomas C

2017-10-15

Cerebellins are secreted hexameric proteins that form tripartite complexes with the presynaptic cell-adhesion molecules neurexins or 'deleted-in-colorectal-cancer', and the postsynaptic glutamate-receptor-related proteins GluD1 and GluD2. These tripartite complexes are thought to regulate synapses. However, cerebellins are expressed in multiple isoforms whose relative distributions and overall functions are not understood. Three of the four cerebellins, Cbln1, Cbln2, and Cbln4, autonomously assemble into homohexamers, whereas the Cbln3 requires Cbln1 for assembly and secretion. Here, we show that Cbln1, Cbln2, and Cbln4 are abundantly expressed in nearly all brain regions, but exhibit strikingly different expression patterns and developmental dynamics. Using newly generated knockin reporter mice for Cbln2 and Cbln4, we find that Cbln2 and Cbln4 are not universally expressed in all neurons, but only in specific subsets of neurons. For example, Cbln2 and Cbln4 are broadly expressed in largely non-overlapping subpopulations of excitatory cortical neurons, but only sparse expression was observed in excitatory hippocampal neurons of the CA1- or CA3-region. Similarly, Cbln2 and Cbln4 are selectively expressed, respectively, in inhibitory interneurons and excitatory mitral projection neurons of the main olfactory bulb; here, these two classes of neurons form dendrodendritic reciprocal synapses with each other. A few brain regions, such as the nucleus of the lateral olfactory tract, exhibit astoundingly high Cbln2 expression levels. Viewed together, our data show that cerebellins are abundantly expressed in relatively small subsets of neurons, suggesting specific roles restricted to subsets of synapses. © 2017 Wiley Periodicals, Inc.

How to Select a Good Training-data Subset for Transcription: Submodular Active Selection for Sequences

DTIC Science & Technology

2009-01-01

selection and uncertainty sampling signif- icantly. Index Terms: Transcription, labeling, submodularity, submod- ular selection, active learning , sequence...name of batch active learning , where a subset of data that is most informative and represen- tative of the whole is selected for labeling. Often...representative subset. Note that our Fisher ker- nel is over an unsupervised generative model, which enables us to bootstrap our active learning approach

Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE.

PubMed

Chen, Qi; Meng, Zhaopeng; Liu, Xinyi; Jin, Qianguo; Su, Ran

2018-06-15

Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

PubMed

Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

2015-04-01

Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Adenovirus-specific T-cell Subsets in Human Peripheral Blood and After IFN-γ Immunomagnetic Selection.

PubMed

Qian, Chongsheng; Wang, Yingying; Cai, Huili; Laroye, Caroline; De Carvalho Bittencourt, Marcelo; Clement, Laurence; Stoltz, Jean-François; Decot, Véronique; Reppel, Loïc; Bensoussan, Danièle

2016-01-01

Adoptive antiviral cellular immunotherapy by infusion of virus-specific T cells (VSTs) is becoming an alternative treatment for viral infection after hematopoietic stem cell transplantation. The T memory stem cell (TSCM) subset was recently described as exhibiting self-renewal and multipotency properties which are required for sustained efficacy in vivo. We wondered if such a crucial subset for immunotherapy was present in VSTs. We identified, by flow cytometry, TSCM in adenovirus (ADV)-specific interferon (IFN)-γ+ T cells before and after IFN-γ-based immunomagnetic selection, and analyzed the distribution of the main T-cell subsets in VSTs: naive T cells (TN), TSCM, T central memory cells (TCM), T effector memory cell (TEM), and effector T cells (TEFF). In this study all of the different T-cell subsets were observed in the blood sample from healthy donor ADV-VSTs, both before and after IFN-γ-based immunomagnetic selection. As the IFN-γ-based immunomagnetic selection system sorts mainly the most differentiated T-cell subsets, we observed that TEM was always the major T-cell subset of ADV-specific T cells after immunomagnetic isolation and especially after expansion in vitro. Comparing T-cell subpopulation profiles before and after in vitro expansion, we observed that in vitro cell culture with interleukin-2 resulted in a significant expansion of TN-like, TCM, TEM, and TEFF subsets in CD4IFN-γ T cells and of TCM and TEM subsets only in CD8IFN-γ T cells. We demonstrated the presence of all T-cell subsets in IFN-γ VSTs including the TSCM subpopulation, although this was weakly selected by the IFN-γ-based immunomagnetic selection system.

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

2017-04-01

Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of rain events. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices

NASA Astrophysics Data System (ADS)

Seo, Seung Beom; Kim, Young-Oh; Kim, Youngil; Eum, Hyung-Il

2018-04-01

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis-Kuo-Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

Different hunting strategies select for different weights in red deer.

PubMed

Martínez, María; Rodríguez-Vigal, Carlos; Jones, Owen R; Coulson, Tim; San Miguel, Alfonso

2005-09-22

Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data.

Heat shock protein 90-mediated peptide-selective presentation of cytosolic tumor antigen for direct recognition of tumors by CD4(+) T cells.

PubMed

Tsuji, Takemasa; Matsuzaki, Junko; Caballero, Otavia L; Jungbluth, Achim A; Ritter, Gerd; Odunsi, Kunle; Old, Lloyd J; Gnjatic, Sacha

2012-04-15

Tumor Ag-specific CD4(+) T cells play important functions in tumor immunosurveillance, and in certain cases they can directly recognize HLA class II-expressing tumor cells. However, the underlying mechanism of intracellular Ag presentation to CD4(+) T cells by tumor cells has not yet been well characterized. We analyzed two naturally occurring human CD4(+) T cell lines specific for different peptides from cytosolic tumor Ag NY-ESO-1. Whereas both lines had the same HLA restriction and a similar ability to recognize exogenous NY-ESO-1 protein, only one CD4(+) T cell line recognized NY-ESO-1(+) HLA class II-expressing melanoma cells. Modulation of Ag processing in melanoma cells using specific molecular inhibitors and small interfering RNA revealed a previously undescribed peptide-selective Ag-presentation pathway by HLA class II(+) melanoma cells. The presentation required both proteasome and endosomal protease-dependent processing mechanisms, as well as cytosolic heat shock protein 90-mediated chaperoning. Such tumor-specific pathway of endogenous HLA class II Ag presentation is expected to play an important role in immunosurveillance or immunosuppression mediated by various subsets of CD4(+) T cells at the tumor local site. Furthermore, targeted activation of tumor-recognizing CD4(+) T cells by vaccination or adoptive transfer could be a suitable strategy for enhancing the efficacy of tumor immunotherapy.

Statistical separability and classification of land use classes using image-100. [Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Kumar, R.; Niero, M.

1977-01-01

The author has identified the following significant results. The statistical separability of land use classes in the subsets of one to four spectral channels was investigated. Using ground observations and aerial photography, the MSS data of LANDSAT were analyzed with the Image-100. In the subsets of one to three spectral channels, channel 4, channel 4 & 7, and channels 4, 5, & 7 were found to be the best choices (ch.4 - 0.5 to 0.6 microns, ch. 5 - 0.6 to 0.7 microns, ch. 6 - 0.7 to 0.8 microns, and ch. 7 - 0.8 to 1.1 microns). For the single cell option of the Image-100, the errors of omission varied from 5% for the industrial class to 46% for the institutional class. The errors of commission varied from 11% for the commercial class to 39% for the industrial class. On the whole, the sample classifier gave considerably more accurate results compared to the single cell or multicell option.

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

1995-01-01

Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files.

PubMed

Bell, Andrew S; Bradley, Joseph; Everett, Jeremy R; Loesel, Jens; McLoughlin, David; Mills, James; Peakman, Marie-Claire; Sharp, Robert E; Williams, Christine; Zhu, Hongyao

2016-11-01

High-throughput screening (HTS) is an effective method for lead and probe discovery that is widely used in industry and academia to identify novel chemical matter and to initiate the drug discovery process. However, HTS can be time consuming and costly and the use of subsets as an efficient alternative to screening entire compound collections has been investigated. Subsets may be selected on the basis of chemical diversity, molecular properties, biological activity diversity or biological target focus. Previously, we described a novel form of subset screening: plate-based diversity subset (PBDS) screening, in which the screening subset is constructed by plate selection (rather than individual compound cherry-picking), using algorithms that select for compound quality and chemical diversity on a plate basis. In this paper, we describe a second-generation approach to the construction of an updated subset: PBDS2, using both plate and individual compound selection, that has an improved coverage of the chemical space of the screening file, whilst only selecting the same number of plates for screening. We describe the validation of PBDS2 and its successful use in hit and lead discovery. PBDS2 screening became the default mode of singleton (one compound per well) HTS for lead discovery in Pfizer.

When can preheating affect the CMB?

NASA Astrophysics Data System (ADS)

Tsujikawa, Shinji; Bassett, Bruce A.

2002-05-01

We discuss the principles governing the selection of inflationary models for which preheating can affect the CMB. This is a (fairly small) subset of those models which have nonnegligible entropy/isocurvature perturbations on large scales during inflation. We study new models which belong to this class-two-field inflation with negative nonminimal coupling and hybrid/double/supernatural inflation models where the tachyonic growth of entropy perturbations can lead to the variation of the curvature perturbation, /R, on super-Hubble scales. Finally, we present evidence against recent claims for the variation of /R in the absence of substantial super-Hubble entropy perturbations.

Landslide susceptibility mapping using a bivariate statistical model in a tropical hilly area of southeastern Brazil

NASA Astrophysics Data System (ADS)

Araújo, J. P. C.; DA Silva, L. M.; Dourado, F. A. D.; Fernandes, N.

2015-12-01

Landslides are the most damaging natural hazard in the mountainous region of Rio de Janeiro State in Brazil, responsible for thousands of deaths and important financial and environmental losses. However, this region has currently few landslide susceptibility maps implemented on an adequate scale. Identification of landslide susceptibility areas is fundamental in successful land use planning and management practices to reduce risk. This paper applied the Bayes' theorem based on weight of evidence (WoE) using 8 landslide-related factors in a geographic information system (GIS) for landslide susceptibility mapping. 378 landslide locations were identified and mapped on a selected basin in the city of Nova Friburgo, triggered by the January 2011 rainfall event. The landslide scars were divided into two subsets: training and validation subsets. The 8 landslide-related factors weighted by WoE were performed using chi-square test to indicate which variables are conditionally independent of each other to be used in the final map. Finally, the maps of weighted factors were summed up to construct the landslide susceptibility map and validated by the validation landslide subset. According to the results, slope, aspect and contribution area showed the higher positive spatial correlation with landslides. In the landslide susceptibility map, 21% of the area presented very low and low susceptibilities with 3% of the validation scars, 41% presented medium susceptibility with 22% of the validation scars and 38% presented high and very high susceptibilities with 75% of the validation scars. The very high susceptibility class stands for 16% of the basin area and has 54% of the all scars. The approach used in this study can be considered very useful since 75% of the area affected by landslides was included in the high and very high susceptibility classes.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

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

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Different hunting strategies select for different weights in red deer

PubMed Central

Martínez, María; Rodríguez-Vigal, Carlos; Jones, Owen R; Coulson, Tim; Miguel, Alfonso San

2005-01-01

Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data. PMID:17148205

Overlapping and Divergent Actions of Structurally Distinct Histone Deacetylase Inhibitors in Cardiac Fibroblasts

PubMed Central

Schuetze, Katherine B.; Stratton, Matthew S.; Blakeslee, Weston W.; Wempe, Michael F.; Wagner, Florence F.; Holson, Edward B.; Kuo, Yin-Ming; Andrews, Andrew J.; Gilbert, Tonya M.; Hooker, Jacob M.

2017-01-01

Inhibitors of zinc-dependent histone deacetylases (HDACs) profoundly affect cellular function by altering gene expression via changes in nucleosomal histone tail acetylation. Historically, investigators have employed pan-HDAC inhibitors, such as the hydroxamate trichostatin A (TSA), which simultaneously targets members of each of the three zinc-dependent HDAC classes (classes I, II, and IV). More recently, class- and isoform-selective HDAC inhibitors have been developed, providing invaluable chemical biology probes for dissecting the roles of distinct HDACs in the control of various physiologic and pathophysiological processes. For example, the benzamide class I HDAC-selective inhibitor, MGCD0103 [N-(2-aminophenyl)-4-[[(4-pyridin-3-ylpyrimidin-2-yl)amino]methyl] benzamide], was shown to block cardiac fibrosis, a process involving excess extracellular matrix deposition, which often results in heart dysfunction. Here, we compare the mechanisms of action of structurally distinct HDAC inhibitors in isolated primary cardiac fibroblasts, which are the major extracellular matrix–producing cells of the heart. TSA, MGCD0103, and the cyclic peptide class I HDAC inhibitor, apicidin, exhibited a common ability to enhance histone acetylation, and all potently blocked cardiac fibroblast cell cycle progression. In contrast, MGCD0103, but not TSA or apicidin, paradoxically increased expression of a subset of fibrosis-associated genes. Using the cellular thermal shift assay, we provide evidence that the divergent effects of HDAC inhibitors on cardiac fibroblast gene expression relate to differential engagement of HDAC1- and HDAC2-containing complexes. These findings illustrate the importance of employing multiple compounds when pharmacologically assessing HDAC function in a cellular context and during HDAC inhibitor drug development. PMID:28174211

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

PubMed

Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

2017-10-01

The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploiting Quantum Resonance to Solve Combinatorial Problems

NASA Technical Reports Server (NTRS)

Zak, Michail; Fijany, Amir

2006-01-01

Quantum resonance would be exploited in a proposed quantum-computing approach to the solution of combinatorial optimization problems. In quantum computing in general, one takes advantage of the fact that an algorithm cannot be decoupled from the physical effects available to implement it. Prior approaches to quantum computing have involved exploitation of only a subset of known quantum physical effects, notably including parallelism and entanglement, but not including resonance. In the proposed approach, one would utilize the combinatorial properties of tensor-product decomposability of unitary evolution of many-particle quantum systems for physically simulating solutions to NP-complete problems (a class of problems that are intractable with respect to classical methods of computation). In this approach, reinforcement and selection of a desired solution would be executed by means of quantum resonance. Classes of NP-complete problems that are important in practice and could be solved by the proposed approach include planning, scheduling, search, and optimal design.

Hydroxyurea-Mediated Cytotoxicity Without Inhibition of Ribonucleotide Reductase.

PubMed

Liew, Li Phing; Lim, Zun Yi; Cohen, Matan; Kong, Ziqing; Marjavaara, Lisette; Chabes, Andrei; Bell, Stephen D

2016-11-01

In many organisms, hydroxyurea (HU) inhibits class I ribonucleotide reductase, leading to lowered cellular pools of deoxyribonucleoside triphosphates. The reduced levels for DNA precursors is believed to cause replication fork stalling. Upon treatment of the hyperthermophilic archaeon Sulfolobus solfataricus with HU, we observe dose-dependent cell cycle arrest, accumulation of DNA double-strand breaks, stalled replication forks, and elevated levels of recombination structures. However, Sulfolobus has a HU-insensitive class II ribonucleotide reductase, and we reveal that HU treatment does not significantly impact cellular DNA precursor pools. Profiling of protein and transcript levels reveals modulation of a specific subset of replication initiation and cell division genes. Notably, the selective loss of the regulatory subunit of the primase correlates with cessation of replication initiation and stalling of replication forks. Furthermore, we find evidence for a detoxification response induced by HU treatment. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

Gene Selection and Cancer Classification: A Rough Sets Based Approach

NASA Astrophysics Data System (ADS)

Sun, Lijun; Miao, Duoqian; Zhang, Hongyun

Indentification of informative gene subsets responsible for discerning between available samples of gene expression data is an important task in bioinformatics. Reducts, from rough sets theory, corresponding to a minimal set of essential genes for discerning samples, is an efficient tool for gene selection. Due to the compuational complexty of the existing reduct algoritms, feature ranking is usually used to narrow down gene space as the first step and top ranked genes are selected . In this paper,we define a novel certierion based on the expression level difference btween classes and contribution to classification of the gene for scoring genes and present a algorithm for generating all possible reduct from informative genes.The algorithm takes the whole attribute sets into account and find short reduct with a significant reduction in computational complexity. An exploration of this approach on benchmark gene expression data sets demonstrates that this approach is successful for selecting high discriminative genes and the classification accuracy is impressive.

An evaluation of exact methods for the multiple subset maximum cardinality selection problem.

PubMed

Brusco, Michael J; Köhn, Hans-Friedrich; Steinley, Douglas

2016-05-01

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach. © 2016 The British Psychological Society.

Evaluation and application of multiple scoring functions for a virtual screening experiment

NASA Astrophysics Data System (ADS)

Xing, Li; Hodgkin, Edward; Liu, Qian; Sedlock, David

2004-05-01

In order to identify novel chemical classes of factor Xa inhibitors, five scoring functions (FlexX, DOCK, GOLD, ChemScore and PMF) were engaged to evaluate the multiple docking poses generated by FlexX. The compound collection was composed of confirmed potent factor Xa inhibitors and a subset of the LeadQuest® screening compound library. Except for PMF the other four scoring functions succeeded in reproducing the crystal complex (PDB code: 1FAX). During virtual screening the highest hit rate (80%) was demonstrated by FlexX at an energy cutoff of -40 kJ/mol, which is about 40-fold over random screening (2.06%). Limited results suggest that presenting more poses of a single molecule to the scoring functions could deteriorate their enrichment factors. A series of promising scaffolds with favorable binding scores was retrieved from LeadQuest. Consensus scoring by pair-wise intersection failed to enrich the hit rate yielded by single scorings (i.e. FlexX). We note that reported successes of consensus scoring in hit rate enrichment could be artificial because their comparisons were based on a selected subset of single scoring and a markedly reduced subset of double or triple scoring. The findings presented in this report are based upon a single biological system and support further studies.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

KIR Polymorphisms Modulate Peptide-Dependent Binding to an MHC Class I Ligand with a Bw6 Motif

PubMed Central

Colantonio, Arnaud D.; Bimber, Benjamin N.; Neidermyer, William J.; Reeves, R. Keith; Alter, Galit; Altfeld, Marcus; Johnson, R. Paul; Carrington, Mary; O'Connor, David H.; Evans, David T.

2011-01-01

Molecular interactions between killer immunoglobulin-like receptors (KIRs) and their MHC class I ligands play a central role in the regulation of natural killer (NK) cell responses to viral pathogens and tumors. Here we identify Mamu-A1*00201 (Mamu-A*02), a common MHC class I molecule in the rhesus macaque with a canonical Bw6 motif, as a ligand for Mamu-KIR3DL05. Mamu-A1*00201 tetramers folded with certain SIV peptides, but not others, directly stained primary NK cells and Jurkat cells expressing multiple allotypes of Mamu-KIR3DL05. Differences in binding avidity were associated with polymorphisms in the D0 and D1 domains of Mamu-KIR3DL05, whereas differences in peptide-selectivity mapped to the D1 domain. The reciprocal exchange of the third predicted MHC class I-contact loop of the D1 domain switched the specificity of two Mamu-KIR3DL05 allotypes for different Mamu-A1*00201-peptide complexes. Consistent with the function of an inhibitory KIR, incubation of lymphocytes from Mamu-KIR3DL05+ macaques with target cells expressing Mamu-A1*00201 suppressed the degranulation of tetramer-positive NK cells. These observations reveal a previously unappreciated role for D1 polymorphisms in determining the selectivity of KIRs for MHC class I-bound peptides, and identify the first functional KIR-MHC class I interaction in the rhesus macaque. The modulation of KIR-MHC class I interactions by viral peptides has important implications to pathogenesis, since it suggests that the immunodeficiency viruses, and potentially other types of viruses and tumors, may acquire changes in epitopes that increase the affinity of certain MHC class I ligands for inhibitory KIRs to prevent the activation of specific NK cell subsets. PMID:21423672

Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

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

Lee, Youngrok

2013-05-15

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates ofmore » nite mixture models by soft-decomposition of heterogeneous samples without labels for a subset or the entire set of data. In medical surveillance databases we can find partially labeled data, that is, while not completely unlabeled there is only imprecise information about class values. In this study we propose new EM algorithms that take advantages of using such partial labels, and thus incorporate more information than traditional EM algorithms. We particularly propose four variants of the EM algorithm named EM-OCML, EM-PCML, EM-HCML and EM-CPCML, each of which assumes a specific mechanism of missing class values. We conducted a simulation study on exponential survival trees with five classes and showed that the advantages of incorporating substantial amount of partially labeled data can be highly signi cant. We also showed model selection based on AIC values fairly works to select the best proposed algorithm on each specific data set. A case study on a real-world data set of gastric cancer provided by Surveillance, Epidemiology and End Results (SEER) program showed a superiority of EM-CPCML to not only the other proposed EM algorithms but also conventional supervised, unsupervised and semi-supervised learning algorithms.« less

Hepatic dendritic cell subsets in the mouse.

PubMed

Jomantaite, Ieva; Dikopoulos, Nektarios; Kröger, Andrea; Leithäuser, Frank; Hauser, Hansjörg; Schirmbeck, Reinhold; Reimann, Jörg

2004-02-01

The CD11c(+) cell population in the non-parenchymal cell population of the mouse liver contains dendritic cells (DC), NK cells, B cells and T cells. In the hepatic CD11c(+) DC population from immunocompetent or immunodeficient [recombinase-activating gene-1 (RAG1)(-/-)] C57BL/6 mice (rigorously depleted of T cells, B cells and NK cells), we identified a B220(+) CD11c(int) subset of 'plasmacytoid' DC, and a B220(-) CD11c(+) DC subset. The latter DC population could be subdivided into a major, immature (CD40(lo) CD80(lo) CD86(lo) MHC class II(lo)) CD11c(int) subset, and a minor, mature (CD40(hi) CD80(hi) CD86(hi) MHC class II(hi)) CD11c(hi) subset. Stimulated B220(+) but not B220(-) DC produced type I interferon. NKT cell activation in vivo increased the number of liver B220(-) DC three- to fourfold within 18 h post-injection, and up-regulated their surface expression of activation marker, while it contracted the B220(+) DC population. Early in virus infection, the hepatic B220(+) DC subset expanded, and both, the B220(+) as well as B220(-) DC populations in the liver matured. In vitro, B220(-) but not B220(+) DC primed CD4(+) or CD8(+)T cells. Expression of distinct marker profiles and functions, and distinct early reaction to activation signals hence identify two distinct B220(+) and B220(-) subsets in CD11c(+) DC populations freshly isolated from the mouse liver.

Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach.

PubMed

Armañanzas, Rubén; Bielza, Concha; Chaudhuri, Kallol Ray; Martinez-Martin, Pablo; Larrañaga, Pedro

2013-07-01

Is it possible to predict the severity staging of a Parkinson's disease (PD) patient using scores of non-motor symptoms? This is the kickoff question for a machine learning approach to classify two widely known PD severity indexes using individual tests from a broad set of non-motor PD clinical scales only. The Hoehn & Yahr index and clinical impression of severity index are global measures of PD severity. They constitute the labels to be assigned in two supervised classification problems using only non-motor symptom tests as predictor variables. Such predictors come from a wide range of PD symptoms, such as cognitive impairment, psychiatric complications, autonomic dysfunction or sleep disturbance. The classification was coupled with a feature subset selection task using an advanced evolutionary algorithm, namely an estimation of distribution algorithm. Results show how five different classification paradigms using a wrapper feature selection scheme are capable of predicting each of the class variables with estimated accuracy in the range of 72-92%. In addition, classification into the main three severity categories (mild, moderate and severe) was split into dichotomic problems where binary classifiers perform better and select different subsets of non-motor symptoms. The number of jointly selected symptoms throughout the whole process was low, suggesting a link between the selected non-motor symptoms and the general severity of the disease. Quantitative results are discussed from a medical point of view, reflecting a clear translation to the clinical manifestations of PD. Moreover, results include a brief panel of non-motor symptoms that could help clinical practitioners to identify patients who are at different stages of the disease from a limited set of symptoms, such as hallucinations, fainting, inability to control body sphincters or believing in unlikely facts. Copyright © 2013 Elsevier B.V. All rights reserved.

Exploring the utility of organo-polyoxometalate hybrids to inhibit SOX transcription factors

PubMed Central

2014-01-01

Background SOX transcription factors constitute an attractive target class for intervention with small molecules as they play a prominent role in the field of regenerative biomedicine and cancer biology. However, rationally engineering specific inhibitors that interfere with transcription factor DNA interfaces continues to be a monumental challenge in the field of transcription factor chemical biology. Polyoxometalates (POMs) are inorganic compounds that were previously shown to target the high-mobility group (HMG) of SOX proteins at nanomolar concentrations. In continuation of this work, we carried out an assessment of the selectivity of a panel of newly synthesized organo-polyoxometalate hybrids in targeting different transcription factor families to enable the usage of polyoxometalates as specific SOX transcription factor drugs. Results The residual DNA-binding activities of 15 different transcription factors were measured after treatment with a panel of diverse polyoxometalates. Polyoxometalates belonging to the Dawson structural class were found to be more potent inhibitors than the Keggin class. Further, organically modified Dawson polyoxometalates were found to be the most potent in inhibiting transcription factor DNA binding activity. The size of the polyoxometalates and its derivitization were found to be the key determinants of their potency. Conclusion Polyoxometalates are highly potent, nanomolar range inhibitors of the DNA binding activity of the Sox-HMG family. However, binding assays involving a limited subset of structurally diverse polyoxometalates revealed a low selectivity profile against different transcription factor families. Further progress in achieving selectivity and deciphering structure-activity relationship of POMs require the identification of POM binding sites on transcription factors using elaborate approaches like X-ray crystallography and multidimensional NMR. In summary, our report reaffirms that transcription factors are challenging molecular architectures and that future polyoxometalate chemistry must consider further modification strategies, to address the substantial challenges involved in achieving target selectivity. PMID:25678957

OntoFox: web-based support for ontology reuse

PubMed Central

2010-01-01

Background Ontology development is a rapidly growing area of research, especially in the life sciences domain. To promote collaboration and interoperability between different projects, the OBO Foundry principles require that these ontologies be open and non-redundant, avoiding duplication of terms through the re-use of existing resources. As current options to do so present various difficulties, a new approach, MIREOT, allows specifying import of single terms. Initial implementations allow for controlled import of selected annotations and certain classes of related terms. Findings OntoFox http://ontofox.hegroup.org/ is a web-based system that allows users to input terms, fetch selected properties, annotations, and certain classes of related terms from the source ontologies and save the results using the RDF/XML serialization of the Web Ontology Language (OWL). Compared to an initial implementation of MIREOT, OntoFox allows additional and more easily configurable options for selecting and rewriting annotation properties, and for inclusion of all or a computed subset of terms between low and top level terms. Additional methods for including related classes include a SPARQL-based ontology term retrieval algorithm that extracts terms related to a given set of signature terms and an option to extract the hierarchy rooted at a specified ontology term. OntoFox's output can be directly imported into a developer's ontology. OntoFox currently supports term retrieval from a selection of 15 ontologies accessible via SPARQL endpoints and allows users to extend this by specifying additional endpoints. An OntoFox application in the development of the Vaccine Ontology (VO) is demonstrated. Conclusions OntoFox provides a timely publicly available service, providing different options for users to collect terms from external ontologies, making them available for reuse by import into client OWL ontologies. PMID:20569493

Local Feature Selection for Data Classification.

PubMed

Armanfard, Narges; Reilly, James P; Komeili, Majid

2016-06-01

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.

Fecundity selection on ornamental plumage colour differs between ages and sexes and varies over small spatial scales.

PubMed

Parker, T H; Wilkin, T A; Barr, I R; Sheldon, B C; Rowe, L; Griffith, S C

2011-07-01

Avian plumage colours are some of the most conspicuous sexual ornaments, and yet standardized selection gradients for plumage colour have rarely been quantified. We examined patterns of fecundity selection on plumage colour in blue tits (Cyanistes caeruleus L.). When not accounting for environmental heterogeneity, we detected relatively few cases of selection. We found significant disruptive selection on adult male crown colour and yearling female chest colour and marginally nonsignificant positive linear selection on adult female crown colour. We discovered no new significant selection gradients with canonical rotation of the matrix of nonlinear selection. Next, using a long-term data set, we identified territory-level environmental variables that predicted fecundity to determine whether these variables influenced patterns of plumage selection. The first of these variables, the density of oaks within 50 m of the nest, influenced selection gradients only for yearling males. The second variable, an inverse function of nesting density, interacted with a subset of plumage selection gradients for yearling males and adult females, although the strength and direction of selection did not vary predictably with population density across these analyses. Overall, fecundity selection on plumage colour in blue tits appeared rare and inconsistent among sexes and age classes. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Overlapping and Divergent Actions of Structurally Distinct Histone Deacetylase Inhibitors in Cardiac Fibroblasts.

PubMed

Schuetze, Katherine B; Stratton, Matthew S; Blakeslee, Weston W; Wempe, Michael F; Wagner, Florence F; Holson, Edward B; Kuo, Yin-Ming; Andrews, Andrew J; Gilbert, Tonya M; Hooker, Jacob M; McKinsey, Timothy A

2017-04-01

Inhibitors of zinc-dependent histone deacetylases (HDACs) profoundly affect cellular function by altering gene expression via changes in nucleosomal histone tail acetylation. Historically, investigators have employed pan-HDAC inhibitors, such as the hydroxamate trichostatin A (TSA), which simultaneously targets members of each of the three zinc-dependent HDAC classes (classes I, II, and IV). More recently, class- and isoform-selective HDAC inhibitors have been developed, providing invaluable chemical biology probes for dissecting the roles of distinct HDACs in the control of various physiologic and pathophysiological processes. For example, the benzamide class I HDAC-selective inhibitor, MGCD0103 [ N -(2-aminophenyl)-4-[[(4-pyridin-3-ylpyrimidin-2-yl)amino]methyl] benzamide], was shown to block cardiac fibrosis, a process involving excess extracellular matrix deposition, which often results in heart dysfunction. Here, we compare the mechanisms of action of structurally distinct HDAC inhibitors in isolated primary cardiac fibroblasts, which are the major extracellular matrix-producing cells of the heart. TSA, MGCD0103, and the cyclic peptide class I HDAC inhibitor, apicidin, exhibited a common ability to enhance histone acetylation, and all potently blocked cardiac fibroblast cell cycle progression. In contrast, MGCD0103, but not TSA or apicidin, paradoxically increased expression of a subset of fibrosis-associated genes. Using the cellular thermal shift assay, we provide evidence that the divergent effects of HDAC inhibitors on cardiac fibroblast gene expression relate to differential engagement of HDAC1- and HDAC2-containing complexes. These findings illustrate the importance of employing multiple compounds when pharmacologically assessing HDAC function in a cellular context and during HDAC inhibitor drug development. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA).

PubMed

Tang, Rongnian; Chen, Xupeng; Li, Chuang

2018-05-01

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.

Selective nuclear export of specific classes of mRNA from mammalian nuclei is promoted by GANP

PubMed Central

Wickramasinghe, Vihandha O.; Andrews, Robert; Ellis, Peter; Langford, Cordelia; Gurdon, John B.; Stewart, Murray; Venkitaraman, Ashok R.; Laskey, Ronald A.

2014-01-01

The nuclear phase of the gene expression pathway culminates in the export of mature messenger RNAs (mRNAs) to the cytoplasm through nuclear pore complexes. GANP (germinal- centre associated nuclear protein) promotes the transfer of mRNAs bound to the transport factor NXF1 to nuclear pore complexes. Here, we demonstrate that GANP, subunit of the TRanscription-EXport-2 (TREX-2) mRNA export complex, promotes selective nuclear export of a specific subset of mRNAs whose transport depends on NXF1. Genome-wide gene expression profiling showed that half of the transcripts whose nuclear export was impaired following NXF1 depletion also showed reduced export when GANP was depleted. GANP-dependent transcripts were highly expressed, yet short-lived, and were highly enriched in those encoding central components of the gene expression machinery such as RNA synthesis and processing factors. After injection into Xenopus oocyte nuclei, representative GANP-dependent transcripts showed faster nuclear export kinetics than representative transcripts that were not influenced by GANP depletion. We propose that GANP promotes the nuclear export of specific classes of mRNAs that may facilitate rapid changes in gene expression. PMID:24510098

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

PubMed

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.

Fourier spatial frequency analysis for image classification: training the training set

NASA Astrophysics Data System (ADS)

Johnson, Timothy H.; Lhamo, Yigah; Shi, Lingyan; Alfano, Robert R.; Russell, Stewart

2016-04-01

The Directional Fourier Spatial Frequencies (DFSF) of a 2D image can identify similarity in spatial patterns within groups of related images. A Support Vector Machine (SVM) can then be used to classify images if the inter-image variance of the FSF in the training set is bounded. However, if variation in FSF increases with training set size, accuracy may decrease as the size of the training set increases. This calls for a method to identify a set of training images from among the originals that can form a vector basis for the entire class. Applying the Cauchy product method we extract the DFSF spectrum from radiographs of osteoporotic bone, and use it as a matched filter set to eliminate noise and image specific frequencies, and demonstrate that selection of a subset of superclassifiers from within a set of training images improves SVM accuracy. Central to this challenge is that the size of the search space can become computationally prohibitive for all but the smallest training sets. We are investigating methods to reduce the search space to identify an optimal subset of basis training images.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.

PubMed

Mizianty, Marcin J; Kurgan, Lukasz

2009-12-13

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

PubMed Central

2009-01-01

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

Effect of freezing of sputum samples on flow cytometric analysis of lymphocyte subsets.

PubMed

Jaksztat, E; Holz, O; Paasch, K; Kelly, M M; Hargreave, F E; Cox, G; Magnussen, H; Jörres, R A

2004-08-01

Sputum samples should be processed shortly after induction to prevent cell degradation. For intermediate storage, freezing of homogenised samples or immediate fixation have been shown to be suitable for cytospins. The aim of this study was to investigate whether freezing or immediate fixation of sputum affect the analysis of lymphocyte subsets by flow cytometry. Selected plugs from 24 sputum samples were homogenised. One aliquot was processed immediately and analysed by flow cytometry. A second aliquot was homogenised, frozen at -20 C after addition of dimethylsulfoxide and stored for a median time of 6 days. In six samples a third aliquot was fixed in formalin after induction and stored for up to 72 h before further processing. Compared to immediate processing, percentages of total lymphocytes and T-suppressor cells were elevated after being frozen, with a minor decrease in the T4/T8 ratio. Proportions of total lymphocytes, T-helper and T-suppressor cells correlated between native and frozen samples, intra-class correlation coefficients being 0.74, 0.85 and 0.70, respectively. The formalin-fixed aliquots could not be analysed with the antibodies used. In conclusion, freezing seems to be a suitable technique to store sputum samples for flow cytometry of CD3, CD4 and CD8 lymphocyte subsets. Its effects were minor compared to the variation between subjects.

Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.

Two-stage atlas subset selection in multi-atlas based image segmentation.

PubMed

Zhao, Tingting; Ruan, Dan

2015-06-01

Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. The authors have developed a novel two-stage atlas subset selection scheme for multi-atlas based segmentation. It achieves good segmentation accuracy with significantly reduced computation cost, making it a suitable configuration in the presence of extensive heterogeneous atlases.

Evaluation of criteria for selecting the spectral attributes of digital LANDSAT MSS imagery for discriminating lithological units in the lower Curaca River Valley, Bahia. [Brazil

NASA Technical Reports Server (NTRS)

Paradella, W. R. (Principal Investigator)

1984-01-01

The use of spectral attributes criteria was investigated, based on measures of statistical distance of separability between thematic classes in MSS digital LANDSAT imagery, in order to select the best subsets of channels in composite colors for the detection and discrimination of lithological units in the lower valley of Curaca River, State of Bahia, Brazil. Three situations were investigated: (1) selection of the three best channels, considering all of the original bands (channels 4, 5, 6, and 7); (2) selection of the three best bands, considering the six MSS band-ratios (channels 4/5, 4/6. 4/7, 5/6, 5/7, and 6/7); and (3) selection of the three best bands in a hybrid approach (the four original bands and the six ratios). A visual analysis was done on color composite images using the selected sets. Results show that the hybrid product (bands 4, 5/7, and 7 with green, blue, and red respectively) and the Normal Color Composite (bands 4, 5, and 7 with blue, green, and red colors respectively) had the best performance.

Structural basis for ligand recognition at the benzodiazepine binding site of GABAA alpha 3 receptor, and pharmacophore-based virtual screening approach.

PubMed

Vijayan, R S K; Ghoshal, Nanda

2008-10-01

Given the heterogeneity of GABA(A) receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized. Thus, drugs selective for GABA(A) alpha(3) receptors are expected to display fewer side effects than the drugs presently in clinical use. Hence we carried out 3D QSAR (three-dimensional quantitative structure-activity relationship) studies on a series of novel GABA(A) alpha(3) subtype selective modulators to gain more insight into subtype affinity. To identify the 3D functional attributes required for subtype selectivity, a chemical feature-based pharmacophore, primarily based on selective ligands representing diverse structural classes was generated. The obtained pseudo receptor model of the benzodiazepine binding site revealed a binding mode akin to "Message-Address" concept. Scaffold hopping was carried out across multi-conformational May Bridge database for the identification of novel chemotypes. Further a focused data reduction approach was employed to choose a subset of enriched compounds based on "Drug likeness" and "Similarity-based" methods. These results taken together could provide impetus for rational design and optimization of more selective and high affinity leads with a potential to have decreased adverse effects.

Adaptive feature selection using v-shaped binary particle swarm optimization.

PubMed

Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

2017-01-01

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

Adaptive feature selection using v-shaped binary particle swarm optimization

PubMed Central

Dong, Hongbin; Zhou, Xiurong

2017-01-01

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850

Fizzy: feature subset selection for metagenomics.

PubMed

Ditzler, Gregory; Morrison, J Calvin; Lan, Yemin; Rosen, Gail L

2015-11-04

Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome. We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.

Fizzy. Feature subset selection for metagenomics

DOE PAGES

Ditzler, Gregory; Morrison, J. Calvin; Lan, Yemin; ...

2015-11-04

Background: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α– & β–diversity. Feature subset selection – a sub-field of machine learning – can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate betweenmore » age groups in the human gut microbiome. Results: We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. Conclusions: We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.« less

Anatomical constraints on attention: Hemifield independence is a signature of multifocal spatial selection

PubMed Central

Alvarez, George A; Gill, Jonathan; Cavanagh, Patrick

2012-01-01

Previous studies have shown independent attentional selection of targets in the left and right visual hemifields during attentional tracking (Alvarez & Cavanagh, 2005) but not during a visual search (Luck, Hillyard, Mangun, & Gazzaniga, 1989). Here we tested whether multifocal spatial attention is the critical process that operates independently in the two hemifields. It is explicitly required in tracking (attend to a subset of object locations, suppress the others) but not in the standard visual search task (where all items are potential targets). We used a modified visual search task in which observers searched for a target within a subset of display items, where the subset was selected based on location (Experiments 1 and 3A) or based on a salient feature difference (Experiments 2 and 3B). The results show hemifield independence in this subset visual search task with location-based selection but not with feature-based selection; this effect cannot be explained by general difficulty (Experiment 4). Combined, these findings suggest that hemifield independence is a signature of multifocal spatial attention and highlight the need for cognitive and neural theories of attention to account for anatomical constraints on selection mechanisms. PMID:22637710

Ziconotide for treatment of severe chronic pain.

PubMed

Schmidtko, Achim; Lötsch, Jörn; Freynhagen, Rainer; Geisslinger, Gerd

2010-05-01

Pharmacological management of severe chronic pain is difficult to achieve with currently available analgesic drugs, and remains a large unmet therapeutic need. The synthetic peptide ziconotide has been approved by the US Food and Drug Administration and the European Medicines Agency for intrathecal treatment of patients with severe chronic pain that is refractory to other treatment modalities. Ziconotide is the first member in the new drug class of selective N-type voltage-sensitive calcium-channel blockers. The ziconotide-induced blockade of N-type calcium channels in the spinal cord inhibits release of pain-relevant neurotransmitters from central terminals of primary afferent neurons. By this mechanism, ziconotide can effectively reduce pain. However, ziconotide has a narrow therapeutic window because of substantial CNS side-effects, and thus treatment with ziconotide is appropriate for only a small subset of patients with severe chronic pain. We provide an overview of the benefits and limitations of intrathecal ziconotide treatment and review potential future developments in this new drug class. Copyright 2010 Elsevier Ltd. All rights reserved.

Reference ranges of hematology and lymphocyte subsets in healthy Korean native cattle (Hanwoo) and Holstein dairy cattle.

PubMed

Kim, Yun-Mi; Lee, Jin-A; Jung, Bock-Gie; Kim, Tae-Hoon; Lee, Bong-Joo; Suh, Guk-Hyun

2016-06-01

There are no accurate reference ranges for hematology parameters and lymphocyte subsets in Korean native beef cattle (Hanwoo). This study was performed to establish reliable reference ranges of hematology and lymphocyte subsets using a large number of Hanwoo cattle (n = 350) and to compare differences between Hanwoo and Holstein dairy cattle (n = 334). Additionally, age-related changes in lymphocyte subsets were studied. Bovine leukocyte subpopulation analysis was performed using mono or dual color flow cytometry. The leukocyte subpopulations investigated in healthy cattle included: CD2(+) cells, sIgM(+) cells, MHC class II(+) cells, CD3(+) CD4(+) cells, CD3(+) CD8(+) cells, and WC1(+) cells. Although Hanwoo and Holstein cattle are the same species, results showed several differences in hematology and lymphocyte subsets between Hanwoo and Holstein cattle. This study is the first report to establish reference ranges of hematology and lymphocyte subsets in adult Hanwoo cattle. © 2015 Japanese Society of Animal Science.

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

PI3K pathway dependencies in endometrioid endometrial cancer cell lines

PubMed Central

Weigelt, Britta; Warne, Patricia H; Lambros, Maryou B; Reis-Filho, Jorge S; Downward, Julian

2013-01-01

Purpose Endometrioid endometrial cancers (EECs) frequently harbor coexisting mutations in PI3K pathway genes, including PTEN, PIK3CA, PIK3R1, and KRAS. We sought to define the genetic determinants of PI3K pathway inhibitor response in EEC cells, and whether PTEN-mutant EEC cell lines rely on p110β signaling for survival. Experimental Design Twenty-four human EEC cell lines were characterized for their mutation profile and activation state of PI3K and MAPK signaling pathway proteins. Cells were treated with pan-class I PI3K, p110α and p110β isoform-specific, allosteric mTOR, mTOR kinase, dual PI3K/mTOR, MEK and RAF inhibitors. RNA interference (RNAi) was employed to assess effects of KRAS silencing in EEC cells. Results EEC cell lines harboring PIK3CA and PTEN mutations were selectively sensitive to the pan-class I PI3K inhibitor GDC-0941 and allosteric mTOR inhibitor Temsirolimus, respectively. Subsets of EEC cells with concurrent PIK3CA and/or PTEN and KRAS mutations were sensitive to PI3K pathway inhibition, and only 2/6 KRAS-mutant cell lines showed response to MEK inhibition. KRAS RNAi silencing did not induce apoptosis in KRAS-mutant EEC cells. PTEN-mutant EEC cell lines were resistant to the p110β inhibitors GSK2636771 and AZD6482, and only in combination with the p110α selective inhibitor A66, a decrease in cell viability was observed. Conclusions Targeted pan-PI3K and mTOR inhibition in EEC cells may be most effective in PIK3CA-mutant and PTEN-mutant tumors, respectively, even in a subset of EECs concurrently harboring KRAS mutations. Inhibition of p110β alone may not be sufficient to sensitize PTEN-mutant EEC cells and combination with other targeted agents may be required. PMID:23674493

PI3K pathway dependencies in endometrioid endometrial cancer cell lines.

PubMed

Weigelt, Britta; Warne, Patricia H; Lambros, Maryou B; Reis-Filho, Jorge S; Downward, Julian

2013-07-01

Endometrioid endometrial cancers (EEC) frequently harbor coexisting mutations in phosphoinositide 3-kinase (PI3K) pathway genes, including PTEN, PIK3CA, PIK3R1, and KRAS. We sought to define the genetic determinants of PI3K pathway inhibitor response in EEC cells, and whether PTEN-mutant EEC cell lines rely on p110β signaling for survival. Twenty-four human EEC cell lines were characterized for their mutation profile and activation state of PI3K and mitogen-activated protein kinase (MAPK) signaling pathway proteins. Cells were treated with pan-class I PI3K, p110α, and p110β isoform-specific, allosteric mTOR, mTOR kinase, dual PI3K/mTOR, mitogen-activated protein/extracellular signal-regulated kinase (MEK), and RAF inhibitors. RNA interference (RNAi) was used to assess effects of KRAS silencing in EEC cells. EEC cell lines harboring PIK3CA and PTEN mutations were selectively sensitive to the pan-class I PI3K inhibitor GDC-0941 and allosteric mTOR inhibitor temsirolimus, respectively. Subsets of EEC cells with concurrent PIK3CA and/or PTEN and KRAS mutations were sensitive to PI3K pathway inhibition, and only 2 of 6 KRAS-mutant cell lines showed response to MEK inhibition. KRAS RNAi silencing did not induce apoptosis in KRAS-mutant EEC cells. PTEN-mutant EEC cell lines were resistant to the p110β inhibitors GSK2636771 and AZD6482, and only in combination with the p110α selective inhibitor A66 was a decrease in cell viability observed. Targeted pan-PI3K and mTOR inhibition in EEC cells may be most effective in PIK3CA- and PTEN-mutant tumors, respectively, even in a subset of EECs concurrently harboring KRAS mutations. Inhibition of p110β alone may not be sufficient to sensitize PTEN-mutant EEC cells and combination with other targeted agents may be required. ©2013 AACR.

Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces

NASA Astrophysics Data System (ADS)

Xu, Kai; Wang, Yiwen; Wang, Yueming; Wang, Fang; Hao, Yaoyao; Zhang, Shaomin; Zhang, Qiaosheng; Chen, Weidong; Zheng, Xiaoxiang

2013-04-01

Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different decoding models. It shows better robustness of identifying the important neurons with noisy signals presented. The low demand of computational resources which, reflected by the fast convergence, indicates the feasibility of the method applied in portable BMI systems. The ascertainment of the important neurons helps to inspect neural patterns visually associated with the movement task. The elimination of irrelevant neurons greatly reduces the computational burden of mBMI systems and maintains the performance with better robustness.

Tangled up in views: Beliefs in the nature of science and responses to socioscientific dilemmas

NASA Astrophysics Data System (ADS)

Zeidler, Dana L.; Walker, Kimberly A.; Ackett, Wayne A.; Simmons, Michael L.

2002-05-01

The purpose of this study was to investigate the relationships between students' conceptions of the nature of science and their reactions to evidence that challenged their beliefs about socioscientific issues. This study involved 41 pairs of students representing critical cases of contrasting ethical viewpoints. These 82 students were identified from a larger sample of 248 students from 9th and 10th grade general science classes, 11th and 12th grade honors biology, honors science, and physics classes, and upper-level college preservice science education classes. Students responded to questions aimed at revealing their epistemological views of the nature of science and their belief convictions on a selected socioscientific issue. The smaller subset of students was selected based on varying degrees of belief convictions about the socioscientific issues and the selected students were then paired to discuss their reasoning related to the issue while being exposed to anomalous data and information from each other and in response to epistemological probes of an interviewer. Taxonomic categories of students' conceptions of the nature of science were derived from the researchers' analysis of student responses to interviews and questionnaires. In some instances, students' conceptions of the nature of science were reflected in their reasoning on a moral and ethical issue. This study stimulated students to reflect on their own beliefs and defend their opinions. The findings suggest that the reactions of students to anomalous socioscientific data are varied and complex, with notable differences in the reasoning processes of high school students compared to college students. A deeper understanding of how students reason about the moral and ethical context of controversial socioscientific issues will allow science educators to incorporate teaching strategies aimed at developing students' reasoning skills in these crucial areas.

HLA-DRB1 alleles in juvenile-onset systemic lupus erythematosus: renal histologic class correlations.

PubMed

Liphaus, B L; Kiss, M H B; Goldberg, A C

2007-04-01

Human leukocyte antigens (HLA) DRB1*03 and DRB1*02 have been associated with systemic lupus erythematosus (SLE) in Caucasians and black populations. It has been observed that certain HLA alleles show stronger associations with SLE autoantibodies and clinical subsets, although they have rarely been associated with lupus renal histologic class. In the present study, HLA-DRB1 allele correlations with clinical features, autoantibodies and renal histologic class were analyzed in a cohort of racially mixed Brazilian patients with juvenile-onset SLE. HLA-DRB1 typing was carried out by polymerase chain reaction amplification with sequence-specific primers using genomic DNA from 55 children and adolescents fulfilling at least four of the American College of Rheumatology criteria for SLE. Significance was determined by the chi-square test applied to 2 x 2 tables. The HLA-DRB1*15 allele was most frequent in patients with renal, musculoskeletal, cutaneous, hematologic, cardiac, and neuropsychiatric involvement, as well as in patients positive for anti-dsDNA, anti-Sm, anti-U1-RNP, and anti-SSA/Ro antibodies, although an association between HLA alleles and SLE clinical features and autoantibodies could not be observed. The HLA-DRB1*17, HLA-DRB1*10, HLA-DRB1*15, and HLA-DRB1*07 alleles were significantly higher in patients with renal histologic class I, class IIA, class IIB, and class V, respectively. The present results suggest that the contribution of HLA- DRB1 alleles to juvenile-onset SLE could not be related to clinical or serological subsets of the disease, but it may be related to renal histologic classes, especially class I, class II A, class II B, and class V. The latter correlations have not been observed in literature.

A Survey of Object Oriented Languages in Programming Environments.

DTIC Science & Technology

1987-06-01

subset of natural languages might be more effective , and make the human-computer interface more friendly. 19 .. .. . . -.. -, " ,. o...and complexty of Ada. He meant that the language contained too many features that made it complicated to use effectively . Much of the complexity comes...by sending messages to a class instance. A small subset of the methods in Smalltalk-80 are not expressed in the !-’ Smalhalk-80 programming language

Variability of Root Traits in Spring Wheat Germplasm

PubMed Central

Narayanan, Sruthi; Mohan, Amita; Gill, Kulvinder S.; Prasad, P. V. Vara

2014-01-01

Root traits influence the amount of water and nutrient absorption, and are important for maintaining crop yield under drought conditions. The objectives of this research were to characterize variability of root traits among spring wheat genotypes and determine whether root traits are related to shoot traits (plant height, tiller number per plant, shoot dry weight, and coleoptile length), regions of origin, and market classes. Plants were grown in 150-cm columns for 61 days in a greenhouse under optimal growth conditions. Rooting depth, root dry weight, root: shoot ratio, and shoot traits were determined for 297 genotypes of the germplasm, Cultivated Wheat Collection (CWC). The remaining root traits such as total root length and surface area were measured for a subset of 30 genotypes selected based on rooting depth. Significant genetic variability was observed for root traits among spring wheat genotypes in CWC germplasm or its subset. Genotypes Sonora and Currawa were ranked high, and genotype Vandal was ranked low for most root traits. A positive relationship (R2≥0.35) was found between root and shoot dry weights within the CWC germplasm and between total root surface area and tiller number; total root surface area and shoot dry weight; and total root length and coleoptile length within the subset. No correlations were found between plant height and most root traits within the CWC germplasm or its subset. Region of origin had significant impact on rooting depth in the CWC germplasm. Wheat genotypes collected from Australia, Mediterranean, and west Asia had greater rooting depth than those from south Asia, Latin America, Mexico, and Canada. Soft wheat had greater rooting depth than hard wheat in the CWC germplasm. The genetic variability identified in this research for root traits can be exploited to improve drought tolerance and/or resource capture in wheat. PMID:24945438

Effects of Sample Selection on Estimates of Economic Impacts of Outdoor Recreation

Treesearch

Donald B.K. English

1997-01-01

Estimates of the economic impacts of recreation often come from spending data provided by a self-selected subset of a random sample of site visitors. The subset is frequently less than half the onsite sample. Biased vectors of per trip spending and impact estimates can result if self-selection is related to spending pattctns, and proper corrective procedures arc not...

Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

PubMed

Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

2015-01-01

The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with a low cognitive effort in contrast with the alternative of using an ad-hoc manual analysis of the selected descriptors. Graphical abstractVIDEAN allows the visual analysis of candidate subsets of descriptors for QSAR/QSPR. In the two panels on the top, users can interactively explore numerical correlations as well as co-occurrences in the candidate subsets through two interactive graphs.

Ovarian phagocyte subsets and their distinct tissue distribution patterns.

PubMed

Carlock, Colin; Wu, Jean; Zhou, Cindy; Ross, April; Adams, Henry; Lou, Yahuan

2013-01-01

Ovarian macrophages, which play critical roles in various ovarian events, are probably derived from multiple lineages. Thus, a systemic classification of their subsets is a necessary first step for determination of their functions. Utilizing antibodies to five phagocyte markers, i.e. IA/IE (major histocompatibility complex class II), F4/80, CD11b (Mac-1), CD11c, and CD68, this study investigated subsets of ovarian phagocytes in mice. Three-color immunofluorescence and flow cytometry, together with morphological observation on isolated ovarian cells, demonstrated complicated phenotypes of ovarian phagocytes. Four macrophage and one dendritic cell subset, in addition to many minor phagocyte subsets, were identified. A dendritic cell-like population with a unique phenotype of CD11c(high)IA/IE⁻F4/80⁻ was also frequently observed. A preliminary age-dependent study showed dramatic increases in IA/IE⁺ macrophages and IA/IE⁺ dendritic cells after puberty. Furthermore, immunofluorescences on ovarian sections showed that each subset displayed a distinct tissue distribution pattern. The pattern for each subset may hint to their role in an ovarian function. In addition, partial isolation of ovarian macrophage subset using CD11b antibodies was attempted. Establishment of this isolation method may have provided us a tool for more precise investigation of each subset's functions at the cellular and molecular levels.

The role of atmospheric stability/turbulence on wakes at the Egmond aan Zee offshore wind farm

NASA Astrophysics Data System (ADS)

Barthelmie, R. J.; Churchfield, M. J.; Moriarty, P. J.; Lundquist, J. K.; Oxley, G. S.; Hahn, S.; Pryor, S. C.

2015-06-01

The aim of the paper is to present results from the NREL SOWFA project that compares simulations from models of different fidelity to meteorological and turbine data from the Egmond aan Zee wind farm. Initial results illustrate that wake behavior and impacts are strongly impacted by turbulence intensity [1]. This includes both power losses from wakes and loading illustrated by the out of plane bending moment. Here we focus on understanding the relationship between turbulence and atmospheric stability and whether power losses due to wakes can effectively be characterized by measures of turbulence alone or whether atmospheric stability as a whole plays a fundamental role in wake behavior. The study defines atmospheric stability using the Monin-Obukhov length estimated based on the temperature difference between 116 and 70 m. The data subset selected using this method for the calculation of the Monin-Obukhov length indicate little diurnal or directional dependence of the stability classes but a dominance of stable classes in the spring/unstable classes in fall and of near-neutral classes at high wind speeds (Figure 2). The analysis is complicated by the need to define turbulence intensity. We can select the ratio of the standard deviation of wind speed to mean wind speed in each observation period using data from the meteorological mast, in which case a substantial amount of data must be excluded due to the presence of the wind farm. An alternative is to use data from the wind turbines which could provide a larger data set for analysis. These approaches are examined and compared to illustrate their robustness. Finally, power losses from wakes are categorized according to stability and/or turbulence in order to understand their relative importance in determining the behavior of wind turbine wakes.

The role of atmospheric stability/turbulence on wakes at the Egmond aan Zee offshore wind farm

DOE PAGES

Barthelmie, R. J.; Churchfield, Matthew J.; Moriarty, Patrick J.; ...

2015-06-18

Here, the aim of the paper is to present results from the NREL SOWFA project that compares simulations from models of different fidelity to meteorological and turbine data from the Egmond aan Zee wind farm. Initial results illustrate that wake behavior and impacts are strongly impacted by turbulence intensity. This includes both power losses from wakes and loading illustrated by the out of plane bending moment. Here we focus on understanding the relationship between turbulence and atmospheric stability and whether power losses due to wakes can effectively be characterized by measures of turbulence alone or whether atmospheric stability as amore » whole plays a fundamental role in wake behavior. The study defines atmospheric stability using the Monin-Obukhov length estimated based on the temperature difference between 116 and 70 m. The data subset selected using this method for the calculation of the Monin-Obukhov length indicate little diurnal or directional dependence of the stability classes but a dominance of stable classes in the spring/unstable classes in fall and of near-neutral classes at high wind speeds. The analysis is complicated by the need to define turbulence intensity. We can select the ratio of the standard deviation of wind speed to mean wind speed in each observation period using data from the meteorological mast, in which case a substantial amount of data must be excluded due to the presence of the wind farm. An alternative is to use data from the wind turbines which could provide a larger data set for analysis. These approaches are examined and compared to illustrate their robustness. Finally, power losses from wakes are categorized according to stability and/or turbulence in order to understand their relative importance in determining the behavior of wind turbine wakes.« less

Engaging patients and caregivers in prioritizing symptoms impacting quality of life for Duchenne and Becker muscular dystrophy.

PubMed

Hollin, Ilene L; Peay, Holly; Fischer, Ryan; Janssen, Ellen M; Bridges, John F P

2018-05-26

Patient preference information (PPI) have an increasing role in regulatory decision-making, especially in benefit-risk assessment. PPI can also facilitate prioritization of symptoms to treat and inform meaningful selection of clinical trial endpoints. We engaged patients and caregivers to prioritize symptoms of Duchenne and Becker muscular dystrophy (DBMD) and explored preference heterogeneity. Best-worst scaling (object case) was used to assess priorities across 11 symptoms of DBMD that impact quality of life and for which there is unmet need. Respondents selected the most and least important symptoms to treat among a subset of five. Relative importance scores were estimated for each symptom, and preference heterogeneity was identified using mixed logit and latent class analysis. Respondents included patients (n = 59) and caregivers (n = 96) affected by DBMD. Results indicated that respondents prioritized "weaker heart pumping" [score = 5.13; 95% CI (4.67, 5.59)] and pulmonary symptoms: "lung infections" [3.15; (2.80, 3.50)] and "weaker ability to cough" [2.65; (2.33, 2.97)] as the most important symptoms to treat and "poor attention span" as the least important symptom to treat [- 5.23; (- 5.93, - 4.54)]. Statistically significant preference heterogeneity existed (p value < 0.001). At least two classes existed with different priorities. Priorities of the majority latent class (80%) reflected the aggregate results, whereas the minority latent class (20%) did not distinguish among pulmonary and other symptoms. Estimates of the relative importance for symptoms of Duchenne muscular dystrophy indicated that symptoms with direct links to morbidity and mortality were prioritized above other non-skeletal muscle symptoms. Findings suggested the existence of preference heterogeneity for symptoms, which may be related to symptom experience.

Retinal ganglion cells with distinct directional preferences differ in molecular identity, structure, and central projections.

PubMed

Kay, Jeremy N; De la Huerta, Irina; Kim, In-Jung; Zhang, Yifeng; Yamagata, Masahito; Chu, Monica W; Meister, Markus; Sanes, Joshua R

2011-05-25

The retina contains ganglion cells (RGCs) that respond selectively to objects moving in particular directions. Individual members of a group of ON-OFF direction-selective RGCs (ooDSGCs) detect stimuli moving in one of four directions: ventral, dorsal, nasal, or temporal. Despite this physiological diversity, little is known about subtype-specific differences in structure, molecular identity, and projections. To seek such differences, we characterized mouse transgenic lines that selectively mark ooDSGCs preferring ventral or nasal motion as well as a line that marks both ventral- and dorsal-preferring subsets. We then used the lines to identify cell surface molecules, including Cadherin 6, CollagenXXVα1, and Matrix metalloprotease 17, that are selectively expressed by distinct subsets of ooDSGCs. We also identify a neuropeptide, CART (cocaine- and amphetamine-regulated transcript), that distinguishes all ooDSGCs from other RGCs. Together, this panel of endogenous and transgenic markers distinguishes the four ooDSGC subsets. Patterns of molecular diversification occur before eye opening and are therefore experience independent. They may help to explain how the four subsets obtain distinct inputs. We also demonstrate differences among subsets in their dendritic patterns within the retina and their axonal projections to the brain. Differences in projections indicate that information about motion in different directions is sent to different destinations.

HLA-DRB1 alleles and juvenile idiopathic arthritis: Diagnostic clues emerging from a meta-analysis.

PubMed

De Silvestri, Annalisa; Capittini, Cristina; Poddighe, Dimitri; Marseglia, Gian Luigi; Mascaretti, Luca; Bevilacqua, Elena; Scotti, Valeria; Rebuffi, Chiara; Pasi, Annamaria; Martinetti, Miryam; Tinelli, Carmine

2017-12-01

Juvenile Idiopathic Arthritis (JIA) is characterized with a variable pattern of articular involvement and systemic symptoms and, thus, it has been classified in several subtypes. Genetic predisposition to JIA is mainly due to HLA class II molecules (HLA-DRB1, HLA-DPB1), although HLA class I molecules and non-HLA genes have been implicated, too. Here, we carried out a meta-analysis including selected studies designed to assess HLA genetic background of JIA patients, compared to healthy controls; particularly, we focused our attention on HLA-DRB1. In summary, our meta-analysis showed four main findings regarding HLA-DRB1 locus as a genetic factor of JIA: i) HLA-DRB1*08 is a strong factor predisposing to JIA, both for oligo-articular and poly-articular forms (oJIA>pJIA); ii) HLA-DRB1*01 and HLA-DRB1*04 may be involved in the genetic predisposition of Rheumatoid Factor (RF) positive forms of JIA; iii) HLA-DRB1*11 was confirmed to be predisposing to oligo-articular JIA; iv) HLA-DRB1*04 was confirmed to have a role in systemic JIA. Importantly, RF positivity seems to select the JIA clinical subset with the strongest immunogenetic similarities with adult rheumatoid arthritis. Copyright © 2017 Elsevier B.V. All rights reserved.

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

Two-stage atlas subset selection in multi-atlas based image segmentation

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

Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu

2015-06-15

Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stagemore » atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors have developed a novel two-stage atlas subset selection scheme for multi-atlas based segmentation. It achieves good segmentation accuracy with significantly reduced computation cost, making it a suitable configuration in the presence of extensive heterogeneous atlases.« less

Investigating Optimal Learning Moments in U.S. and Finnish Science Classes

ERIC Educational Resources Information Center

Schneider, Barbara; Krajcik, Joseph; Lavonen, Jari; Salmela-Aro, Katariina; Broda, Michael; Spicer, Justina; Bruner, Justin; Moeller, Julia; Linnansaari, Janna; Juuti, Kalle; Viljaranta, Jaana

2016-01-01

This study explores how often students are engaged in their science classes and their affective states during these times, using an innovative methodology that records these experiences "in situ". Sampling a subset of high schools in the U.S. and Finland, we collected over 7,000 momentary responses from 344 students over the course of a…

Dissipative particle dynamics: Systematic parametrization using water-octanol partition coefficients

NASA Astrophysics Data System (ADS)

Anderson, Richard L.; Bray, David J.; Ferrante, Andrea S.; Noro, Massimo G.; Stott, Ian P.; Warren, Patrick B.

2017-09-01

We present a systematic, top-down, thermodynamic parametrization scheme for dissipative particle dynamics (DPD) using water-octanol partition coefficients, supplemented by water-octanol phase equilibria and pure liquid phase density data. We demonstrate the feasibility of computing the required partition coefficients in DPD using brute-force simulation, within an adaptive semi-automatic staged optimization scheme. We test the methodology by fitting to experimental partition coefficient data for twenty one small molecules in five classes comprising alcohols and poly-alcohols, amines, ethers and simple aromatics, and alkanes (i.e., hexane). Finally, we illustrate the transferability of a subset of the determined parameters by calculating the critical micelle concentrations and mean aggregation numbers of selected alkyl ethoxylate surfactants, in good agreement with reported experimental values.

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

PubMed Central

Wang, Yu; Zhang, Yaonan; Yao, Zhaomin; Zhao, Ruixue; Zhou, Fengfeng

2016-01-01

Non-lethal macular diseases greatly impact patients’ life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples. PMID:28018716

Minimally buffered data transfers between nodes in a data communications network

DOEpatents

Miller, Douglas R.

2015-06-23

Methods, apparatus, and products for minimally buffered data transfers between nodes in a data communications network are disclosed that include: receiving, by a messaging module on an origin node, a storage identifier, a origin data type, and a target data type, the storage identifier specifying application storage containing data, the origin data type describing a data subset contained in the origin application storage, the target data type describing an arrangement of the data subset in application storage on a target node; creating, by the messaging module, origin metadata describing the origin data type; selecting, by the messaging module from the origin application storage in dependence upon the origin metadata and the storage identifier, the data subset; and transmitting, by the messaging module to the target node, the selected data subset for storing in the target application storage in dependence upon the target data type without temporarily buffering the data subset.

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.

A Cancer Gene Selection Algorithm Based on the K-S Test and CFS.

PubMed

Su, Qiang; Wang, Yina; Jiang, Xiaobing; Chen, Fuxue; Lu, Wen-Cong

2017-01-01

To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.

The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection.

PubMed

Sun, Yingqiang; Lu, Chengbo; Li, Xiaobo

2018-05-17

The gene expression profile has the characteristics of a high dimension, low sample, and continuous type, and it is a great challenge to use gene expression profile data for the classification of tumor samples. This paper proposes a cross-entropy based multi-filter ensemble (CEMFE) method for microarray data classification. Firstly, multiple filters are used to select the microarray data in order to obtain a plurality of the pre-selected feature subsets with a different classification ability. The top N genes with the highest rank of each subset are integrated so as to form a new data set. Secondly, the cross-entropy algorithm is used to remove the redundant data in the data set. Finally, the wrapper method, which is based on forward feature selection, is used to select the best feature subset. The experimental results show that the proposed method is more efficient than other gene selection methods and that it can achieve a higher classification accuracy under fewer characteristic genes.

On the reliable and flexible solution of practical subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm for solving subset regression problems is described. The algorithm performs a QR decomposition with a new column-pivoting strategy, which permits subset selection directly from the originally defined regression parameters. This, in combination with a number of extensions of the new technique, makes the method a very flexible tool for analyzing subset regression problems in which the parameters have a physical meaning.

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Gatos, I.; Tsantis, S.; Karamesini, M.; Skouroliakou, A.; Kagadis, G.

2015-09-01

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

Analysis of Information Content in High-Spectral Resolution Sounders using Subset Selection Analysis

NASA Technical Reports Server (NTRS)

Velez-Reyes, Miguel; Joiner, Joanna

1998-01-01

In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.

PubMed

Abuassba, Adnan O M; Zhang, Dezheng; Luo, Xiong; Shaheryar, Ahmad; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L 2 -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

PubMed Central

Abuassba, Adnan O. M.; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets. PMID:28546808

Ci8 short, a novel LPS-induced peptide from the ascidian Ciona intestinalis, modulates responses of the human immune system.

PubMed

Bonura, Angela; Vizzini, Aiti; Vlah, Sara; Gervasi, Francesco; Longo, Alessandra; Melis, Mario R; Schildberg, Frank A; Colombo, Paolo

2018-02-01

The selective modulation of immunity is an emerging concept driven by the vast advances in our understanding of this crucial host defense system. Invertebrates have raised researchers' interest as potential sources of new bioactive molecules owing to their antibacterial, anticancer and immunomodulatory activities. A LipoPolySaccharide (LPS) challenge in the ascidian Ciona intestinalis generates the transcript, Ci8 short, with cis-regulatory elements in the 3' UTR region that are essential for shaping innate immune responses. The derived amino acidic sequence in silico analysis showed specific binding to human Major Histocompatibility Complex (MHC) Class I and Class II alleles. The role of Ci8 short peptide was investigated in a more evolved immune system using human Peripheral Blood Mononuclear Cells (PBMCs) as in vitro model. The biological activities of this molecule include the activation of 70kDa TCR ζ chain Associated Protein kinase (ZAP-70) and T Cell Receptor (TCR) Vβ oligo clonal selection on CD4 + T lymphocytes as well as increased proliferation and IFN-γ secretion. Furthermore Ci8 short affects CD4 + /CD25 high induced regulatory T cells (iTreg) subset selection which co-expressed the functional markers TGF-β1/Latency Associated Protein (LAP) and CD39/CD73. This paper describes a new molecule that modulates important responses of the human adaptive immune system. Copyright © 2017 Elsevier GmbH. All rights reserved.

Multiple Scale Landscape Pattern Index Interpretation for the Persistent Monitoring of Land-Cover and Land-Use

NASA Astrophysics Data System (ADS)

Spivey, Alvin J.

Mapping land-cover land-use change (LCLUC) over regional and continental scales, and long time scales (years and decades), can be accomplished using thematically identified classification maps of a landscape---a LCLU class map. Observations of a landscape's LCLU class map pattern can indicate the most relevant process, like hydrologic or ecologic function, causing landscape scale environmental change. Quantified as Landscape Pattern Metrics (LPM), emergent landscape patterns act as Landscape Indicators (LI) when physically interpreted. The common mathematical approach to quantifying observed landscape scale pattern is to have LPM measure how connected a class exists within the landscape, through nonlinear local kernel operations of edges and gradients in class maps. Commonly applied kernel-based LPM that consistently reveal causal processes are Dominance, Contagion, and Fractal Dimension. These kernel-based LPM can be difficult to interpret. The emphasis on an image pixel's edge by gradient operations and dependence on an image pixel's existence according to classification accuracy limit the interpretation of LPM. For example, the Dominance and Contagion kernel-based LPM very similarly measure how connected a landscape is. Because of this, their reported edge measurements of connected pattern correlate strongly, making their results ambiguous. Additionally, each of these kernel-based LPM are unscalable when comparing class maps from separate imaging system sensor scenarios that change the image pixel's edge position (i.e. changes in landscape extent, changes in pixel size, changes in orientation, etc), and can only interpret landscape pattern as accurately as the LCLU map classification will allow. This dissertation discusses the reliability of common LPM in light of imaging system effects such as: algorithm classification likelihoods, LCLU classification accuracy due to random image sensor noise, and image scale. A description of an approach to generating well behaved LPM through a Fourier system analysis of the entire class map, or any subset of the class map (e.g. the watershed) is the focus of this work. The Fourier approach provides four improvements for LPM. First, the approach reduces any correlation between metrics by developing them within an independent (i.e. orthogonal) Fourier vector space; a Fourier vector space that includes relevant physically representative parameters ( i.e. between class Euclidean distance). Second, accounting for LCLU classification accuracy the LPM measurement precision and measurement accuracy are reported. Third, the mathematics of this approach makes it possible to compare image data captured at separate pixel resolutions or even from separate landscape scenes. Fourth, Fourier interpreted landscape pattern measurement can be a measure of the entire landscape shape, of individual landscape cover change, or as exchanges between class map subsets by operating on the entire class map, subset of class map, or separate subsets of class map[s] respectively. These LCLUC LPM are examined along the 1991-1992 and 2000-2001 records of National Land Cover Database Landsat data products. Those LPM results are used in a predictive fecal coliform model at the South Carolina watershed level in the context of past (validation study) change. Finally, the proposed LPM ability to be used as ecologically relevant environmental indicators is tested by correlating metrics with other, well known LI that consistently reveal causal processes in the literature.

Paramecium tetraurelia chromatin assembly factor-1-like protein PtCAF-1 is involved in RNA-mediated control of DNA elimination.

PubMed

Ignarski, Michael; Singh, Aditi; Swart, Estienne C; Arambasic, Miroslav; Sandoval, Pamela Y; Nowacki, Mariusz

2014-10-29

Genome-wide DNA remodelling in the ciliate Paramecium is ensured by RNA-mediated trans-nuclear crosstalk between the germline and the somatic genomes during sexual development. The rearrangements include elimination of transposable elements, minisatellites and tens of thousands non-coding elements called internally eliminated sequences (IESs). The trans-nuclear genome comparison process employs a distinct class of germline small RNAs (scnRNAs) that are compared against the parental somatic genome to select the germline-specific subset of scnRNAs that subsequently target DNA elimination in the progeny genome. Only a handful of proteins involved in this process have been identified so far and the mechanism of DNA targeting is unknown. Here we describe chromatin assembly factor-1-like protein (PtCAF-1), which we show is required for the survival of sexual progeny and localizes first in the parental and later in the newly developing macronucleus. Gene silencing shows that PtCAF-1 is required for the elimination of transposable elements and a subset of IESs. PTCAF-1 depletion also impairs the selection of germline-specific scnRNAs during development. We identify specific histone modifications appearing during Paramecium development which are strongly reduced in PTCAF-1 depleted cells. Our results demonstrate the importance of PtCAF-1 for the epigenetic trans-nuclear cross-talk mechanism. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.

PubMed

Yu, Hualong; Hong, Shufang; Yang, Xibei; Ni, Jun; Dan, Yuanyuan; Qin, Bin

2013-01-01

DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.

Algorithm For Solution Of Subset-Regression Problems

NASA Technical Reports Server (NTRS)

Verhaegen, Michel

1991-01-01

Reliable and flexible algorithm for solution of subset-regression problem performs QR decomposition with new column-pivoting strategy, enables selection of subset directly from originally defined regression parameters. This feature, in combination with number of extensions, makes algorithm very flexible for use in analysis of subset-regression problems in which parameters have physical meanings. Also extended to enable joint processing of columns contaminated by noise with those free of noise, without using scaling techniques.

A ROC-based feature selection method for computer-aided detection and diagnosis

NASA Astrophysics Data System (ADS)

Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

2014-03-01

Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Lectin Ulex europaeus agglutinin I specifically labels a subset of primary afferent fibers which project selectively to the superficial dorsal horn of the spinal cord.

PubMed

Mori, K

1986-02-19

To examine differential carbohydrate expression among different subsets of primary afferent fibers, several fluorescein-isothiocyanate conjugated lectins were used in a histochemical study of the dorsal root ganglion (DRG) and spinal cord of the rabbit. The lectin Ulex europaeus agglutinin I specifically labeled a subset of DRG cells and primary afferent fibers which projected to the superficial laminae of the dorsal horn. These results suggest that specific carbohydrates containing L-fucosyl residue is expressed selectively in small diameter primary afferent fibers which subserve nociception or thermoception.

Cosmology with XMM galaxy clusters: the X-CLASS/GROND catalogue and photometric redshifts

NASA Astrophysics Data System (ADS)

Ridl, J.; Clerc, N.; Sadibekova, T.; Faccioli, L.; Pacaud, F.; Greiner, J.; Krühler, T.; Rau, A.; Salvato, M.; Menzel, M.-L.; Steinle, H.; Wiseman, P.; Nandra, K.; Sanders, J.

2017-06-01

The XMM Cluster Archive Super Survey (X-CLASS) is a serendipitously detected X-ray-selected sample of 845 galaxy clusters based on 2774 XMM archival observations and covering an approximately 90 deg2 spread across the high-Galactic latitude (|b| > 20°) sky. The primary goal of this survey is to produce a well-selected sample of galaxy clusters on which cosmological analyses can be performed. This paper presents the photometric redshift follow-up of a high signal-to-noise ratio subset of 265 of these clusters with declination δ < +20° with Gamma-Ray Burst Optical and Near-Infrared Detector (GROND), a 7-channel (grizJHK) simultaneous imager on the MPG 2.2-m telescope at the ESO La Silla Observatory. We use a newly developed technique based on the red sequence colour-redshift relation, enhanced with information coming from the X-ray detection to provide photometric redshifts for this sample. We determine photometric redshifts for 232 clusters, finding a median redshift of z = 0.39 with an accuracy of Δz = 0.02(1 + z) when compared to a sample of 76 spectroscopically confirmed clusters. We also compute X-ray luminosities for the entire sample and find a median bolometric luminosity of 7.2 × 1043 erg s-1 and a median temperature of 2.9 keV. We compare our results to those of the XMM-XCS and XMM-XXL surveys, finding good agreement in both samples. The X-CLASS catalogue is available online at http://xmm-lss.in2p3.fr:8080/l4sdb/.

Integrated Task and Data Parallel Programming

NASA Technical Reports Server (NTRS)

Grimshaw, A. S.

1998-01-01

This research investigates the combination of task and data parallel language constructs within a single programming language. There are an number of applications that exhibit properties which would be well served by such an integrated language. Examples include global climate models, aircraft design problems, and multidisciplinary design optimization problems. Our approach incorporates data parallel language constructs into an existing, object oriented, task parallel language. The language will support creation and manipulation of parallel classes and objects of both types (task parallel and data parallel). Ultimately, the language will allow data parallel and task parallel classes to be used either as building blocks or managers of parallel objects of either type, thus allowing the development of single and multi-paradigm parallel applications. 1995 Research Accomplishments In February I presented a paper at Frontiers 1995 describing the design of the data parallel language subset. During the spring I wrote and defended my dissertation proposal. Since that time I have developed a runtime model for the language subset. I have begun implementing the model and hand-coding simple examples which demonstrate the language subset. I have identified an astrophysical fluid flow application which will validate the data parallel language subset. 1996 Research Agenda Milestones for the coming year include implementing a significant portion of the data parallel language subset over the Legion system. Using simple hand-coded methods, I plan to demonstrate (1) concurrent task and data parallel objects and (2) task parallel objects managing both task and data parallel objects. My next steps will focus on constructing a compiler and implementing the fluid flow application with the language. Concurrently, I will conduct a search for a real-world application exhibiting both task and data parallelism within the same program. Additional 1995 Activities During the fall I collaborated with Andrew Grimshaw and Adam Ferrari to write a book chapter which will be included in Parallel Processing in C++ edited by Gregory Wilson. I also finished two courses, Compilers and Advanced Compilers, in 1995. These courses complete my class requirements at the University of Virginia. I have only my dissertation research and defense to complete.

Integrated Task And Data Parallel Programming: Language Design

NASA Technical Reports Server (NTRS)

Grimshaw, Andrew S.; West, Emily A.

1998-01-01

his research investigates the combination of task and data parallel language constructs within a single programming language. There are an number of applications that exhibit properties which would be well served by such an integrated language. Examples include global climate models, aircraft design problems, and multidisciplinary design optimization problems. Our approach incorporates data parallel language constructs into an existing, object oriented, task parallel language. The language will support creation and manipulation of parallel classes and objects of both types (task parallel and data parallel). Ultimately, the language will allow data parallel and task parallel classes to be used either as building blocks or managers of parallel objects of either type, thus allowing the development of single and multi-paradigm parallel applications. 1995 Research Accomplishments In February I presented a paper at Frontiers '95 describing the design of the data parallel language subset. During the spring I wrote and defended my dissertation proposal. Since that time I have developed a runtime model for the language subset. I have begun implementing the model and hand-coding simple examples which demonstrate the language subset. I have identified an astrophysical fluid flow application which will validate the data parallel language subset. 1996 Research Agenda Milestones for the coming year include implementing a significant portion of the data parallel language subset over the Legion system. Using simple hand-coded methods, I plan to demonstrate (1) concurrent task and data parallel objects and (2) task parallel objects managing both task and data parallel objects. My next steps will focus on constructing a compiler and implementing the fluid flow application with the language. Concurrently, I will conduct a search for a real-world application exhibiting both task and data parallelism within the same program m. Additional 1995 Activities During the fall I collaborated with Andrew Grimshaw and Adam Ferrari to write a book chapter which will be included in Parallel Processing in C++ edited by Gregory Wilson. I also finished two courses, Compilers and Advanced Compilers, in 1995. These courses complete my class requirements at the University of Virginia. I have only my dissertation research and defense to complete.

The effect of trade books on the environmental literacy of 11th and 12th graders in aquatic science

NASA Astrophysics Data System (ADS)

Lewis, Ann S.

The purpose of this study was to compare the environmental literacy of 11th and 12th graders who participated in an eighteen-week environmental education program using trade books versus 11 th- and 12th-graders who participated in an eighteen-week, traditional environmental education program without the use of trade books. This study was conducted using a quasi-experimental research technique. Four high school aquatic science classes at two suburban high schools were used in the research. One teacher at each high school taught one control class and one experimental class of aquatic science. In the experimental classes, four trade books were read to the classes during the eighteen-week semester. These four books were selected by the participating teachers before the semester began. The books used were A Home by the Sea, Sea Otter Rescue, There's a Hair in My Dirt, and The Missing Gator of Gumbo Limbo. The instrument used to measure environmental literacy was the Children's Environmental Attitude and Knowledge Scale. This test was given at the beginning of the semester and at the end of the semester. The scores at the end of the semester were analyzed by 2 x 2 mixed model ANOVA with the teacher as the random effect and the condition (trade books) as the fixed effect. The statistical analysis of this study showed that the students in the experimental classes did not score higher than the control classes on the Children's Environmental Attitude and Knowledge Scale or on a subset of "water" questions. Several limitations were placed on this research. These limitations included the following: (1) a small number of classes and a small number of teachers, (2) change from the original plan of using environmental science classes to aquatic science classes, (3) possible indifference of the students, and (4) restrictive teaching strategies of the teachers.

Striatal fast-spiking interneurons selectively modulate circuit output and are required for habitual behavior

PubMed Central

O'Hare, Justin K; Li, Haofang; Kim, Namsoo; Gaidis, Erin; Ade, Kristen; Beck, Jeff; Yin, Henry

2017-01-01

Habit formation is a behavioral adaptation that automates routine actions. Habitual behavior correlates with broad reconfigurations of dorsolateral striatal (DLS) circuit properties that increase gain and shift pathway timing. The mechanism(s) for these circuit adaptations are unknown and could be responsible for habitual behavior. Here we find that a single class of interneuron, fast-spiking interneurons (FSIs), modulates all of these habit-predictive properties. Consistent with a role in habits, FSIs are more excitable in habitual mice compared to goal-directed and acute chemogenetic inhibition of FSIs in DLS prevents the expression of habitual lever pressing. In vivo recordings further reveal a previously unappreciated selective modulation of SPNs based on their firing patterns; FSIs inhibit most SPNs but paradoxically promote the activity of a subset displaying high fractions of gamma-frequency spiking. These results establish a microcircuit mechanism for habits and provide a new example of how interneurons mediate experience-dependent behavior. PMID:28871960

Balancing selection is common in the extended MHC region but most alleles with opposite risk profile for autoimmune diseases are neutrally evolving

PubMed Central

2011-01-01

Background Several susceptibility genetic variants for autoimmune diseases have been identified. A subset of these polymorphisms displays an opposite risk profile in different autoimmune conditions. This observation open interesting questions on the evolutionary forces shaping the frequency of these alleles in human populations. We aimed at testing the hypothesis whereby balancing selection has shaped the frequency of opposite risk alleles. Results Since balancing selection signatures are expected to extend over short genomic portions, we focused our analyses on 11 regions carrying putative functional polymorphisms that may represent the disease variants (and the selection targets). No exceptional nucleotide diversity was observed for ZSCAN23, HLA-DMB, VARS2, PTPN22, BAT3, C6orf47, and IL10; summary statistics were consistent with evolutionary neutrality for these gene regions. Conversely, CDSN/PSORS1C1, TRIM10/TRIM40, BTNL2, and TAP2 showed extremely high nucleotide diversity and most tests rejected neutrality, suggesting the action of balancing selection. For TAP2 and BTNL2 these signatures are not secondary to linkage disequilibrium with HLA class II genes. Nonetheless, with the exception of variants in TRIM40 and CDSN, our data suggest that opposite risk SNPs are not selection targets but rather have accumulated as neutral variants. Conclusion Data herein indicate that balancing selection is common within the extended MHC region and involves several non-HLA loci. Yet, the evolutionary history of most SNPs with an opposite effect for autoimmune diseases is consistent with evolutionary neutrality. We suggest that variants with an opposite effect on autoimmune diseases should not be considered a distinct class of disease alleles from the evolutionary perspective and, in a few cases, the opposite effect on distinct diseases may derive from complex haplotype structures in regions with high genetic diversity. PMID:21682861

Computerized stratified random site-selection approaches for design of a ground-water-quality sampling network

USGS Publications Warehouse

Scott, J.C.

1990-01-01

Computer software was written to randomly select sites for a ground-water-quality sampling network. The software uses digital cartographic techniques and subroutines from a proprietary geographic information system. The report presents the approaches, computer software, and sample applications. It is often desirable to collect ground-water-quality samples from various areas in a study region that have different values of a spatial characteristic, such as land-use or hydrogeologic setting. A stratified network can be used for testing hypotheses about relations between spatial characteristics and water quality, or for calculating statistical descriptions of water-quality data that account for variations that correspond to the spatial characteristic. In the software described, a study region is subdivided into areal subsets that have a common spatial characteristic to stratify the population into several categories from which sampling sites are selected. Different numbers of sites may be selected from each category of areal subsets. A population of potential sampling sites may be defined by either specifying a fixed population of existing sites, or by preparing an equally spaced population of potential sites. In either case, each site is identified with a single category, depending on the value of the spatial characteristic of the areal subset in which the site is located. Sites are selected from one category at a time. One of two approaches may be used to select sites. Sites may be selected randomly, or the areal subsets in the category can be grouped into cells and sites selected randomly from each cell.

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

Adjuvants in the Driver’s Seat: How Magnitude, Type, Fine Specificity and Longevity of Immune Responses Are Driven by Distinct Classes of Immune Potentiators

PubMed Central

Bergmann-Leitner, Elke S.; Leitner, Wolfgang W.

2014-01-01

The mechanism by which vaccine adjuvants enhance immune responses has historically been considered to be the creation of an antigen depot. From here, the antigen is slowly released and provided to immune cells over an extended period of time. This “depot” was formed by associating the antigen with substances able to persist at the injection site, such as aluminum salts or emulsions. The identification of Pathogen-Associated Molecular Patterns (PAMPs) has greatly advanced our understanding of how adjuvants work beyond the simple concept of extended antigen release and has accelerated the development of novel adjuvants. This review focuses on the mode of action of different adjuvant classes in regards to the stimulation of specific immune cell subsets, the biasing of immune responses towards cellular or humoral immune response, the ability to mediate epitope spreading and the induction of persistent immunological memory. A better understanding of how particular adjuvants mediate their biological effects will eventually allow them to be selected for specific vaccines in a targeted and rational manner. PMID:26344620

Characterization of leukocyte subsets in buffalo (Bubalus bubalis) with cross-reactive monoclonal antibodies specific for bovine MHC class I and class II molecules and leukocyte differentiation molecules

USDA-ARS?s Scientific Manuscript database

Although buffalo (Bubalus bubalis) are a major component of the livestock industry worldwide, limited progress has been made in the study of the mechanisms regulating the immune response to pathogens and parasites affecting their health and productivity. This has been, in part, attributable to the l...

Claims-based studies of oral glucose-lowering medications can achieve balance in critical clinical variables only observed in electronic health records.

PubMed

Patorno, Elisabetta; Gopalakrishnan, Chandrasekar; Franklin, Jessica M; Brodovicz, Kimberly G; Masso-Gonzalez, Elvira; Bartels, Dorothee B; Liu, Jun; Schneeweiss, Sebastian

2018-04-01

To evaluate the extent to which balance in unmeasured characteristics of patients with type 2 diabetes (T2DM) was achieved in claims data, by comparing against more detailed information from linked electronic health records (EHR) data. Within a large US commercial insurance database and using a cohort design, we identified patients with T2DM initiating linagliptin or a comparator agent within class (ie, another dipeptidyl peptidase-4 inhibitor) or outside class (ie, pioglitazone or a sulphonylurea) between May 2011 and December 2012. We focused on comparators used at a similar stage of diabetes to linagliptin. For each comparison, 1:1 propensity score (PS) matching was used to balance >100 baseline claims-based characteristics, including proxies of diabetes severity and duration. Additional clinical data from EHR were available for a subset of patients. We assessed representativeness of the claims-EHR-linked subset, evaluated the balance of claims- and EHR-based covariates before and after PS-matching via standardized differences (SDs), and quantified the potential bias associated with observed imbalances. From a claims-based study population of 166 613 patients with T2DM, 7219 (4.3%) patients were linked to their EHR data. Claims-based characteristics in the EHR-linked and EHR-unlinked patients were similar (SD < 0.1), confirming the representativeness of the EHR-linked subset. The balance of claims-based and EHR-based patient characteristics appeared to be reasonable before PS-matching and generally improved in the PS-matched population, to be SD < 0.1 for most patient characteristics and SD < 0.2 for select laboratory results and body mass index categories, which was not large enough to cause meaningful confounding. In the context of pharmacoepidemiological research on diabetes therapy, choosing appropriate comparison groups paired with a new-user design and 1:1 PS matching on many proxies of diabetes severity and duration improves balance in covariates typically unmeasured in administrative claims datasets, to the extent that residual confounding is unlikely. © 2017 John Wiley & Sons Ltd.

Functional differences in epigenetic modulators-superiority of mercaptoacetamide-based histone deacetylase inhibitors relative to hydroxamates in cortical neuron neuroprotection studies.

PubMed

Kozikowski, Alan P; Chen, Yufeng; Gaysin, Arsen; Chen, Bin; D'Annibale, Melissa A; Suto, Carla M; Langley, Brett C

2007-06-28

We compare the ability of two structurally different classes of epigenetic modulators, namely, histone deacetylase (HDAC) inhibitors containing either a hydroxamate or a mercaptoacetamide as the zinc binding group, to protect cortical neurons in culture from oxidative stress-induced death. This study reveals that some of the mercaptoacetamide-based HDAC inhibitors are fully protective, whereas the hydroxamates show toxicity at higher concentrations. Our present results appear to be consistent with the possibility that the mercaptoacetamide-based HDAC inhibitors interact with a different subset of the HDAC isozymes [less activity at HDAC1 and 2 correlates with less inhibitor toxicity], or alternatively, are interacting selectively with only the cytoplasmic HDACs that are crucial for protection from oxidative stress.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

An endogenous small interfering RNA pathway in Drosophila

PubMed Central

Czech, Benjamin; Malone, Colin D.; Zhou, Rui; Stark, Alexander; Schlingeheyde, Catherine; Dus, Monica; Perrimon, Norbert; Kellis, Manolis; Wohlschlegel, James A.; Sachidanandam, Ravi; Hannon, Gregory J.; Brennecke, Julius

2009-01-01

Drosophila endogenous small RNAs are categorized according to their mechanisms of biogenesis and the Argonaute protein to which they bind. MicroRNAs are a class of ubiquitously expressed RNAs of ~22 nucleotides in length, which arise from structured precursors through the action of Drosha–Pasha and Dicer-1–Loquacious complexes1–7. These join Argonaute-1 to regulate gene expression8,9. A second endogenous small RNA class, the Piwi-interacting RNAs, bind Piwi proteins and suppress transposons10,11. Piwi-interacting RNAs are restricted to the gonad, and at least a subset of these arises by Piwi-catalysed cleavage of single-stranded RNAs12,13. Here we show that Drosophila generates a third small RNA class, endogenous small interfering RNAs, in both gonadal and somatic tissues. Production of these RNAs requires Dicer-2, but a subset depends preferentially on Loquacious1,4,5 rather than the canonical Dicer-2 partner, R2D2 (ref. 14). Endogenous small interfering RNAs arise both from convergent transcription units and from structured genomic loci in a tissue-specific fashion. They predominantly join Argonaute-2 and have the capacity, as a class, to target both protein-coding genes and mobile elements. These observations expand the repertoire of small RNAs in Drosophila, adding a class that blurs distinctions based on known biogenesis mechanisms and functional roles. PMID:18463631

Approximate error conjugation gradient minimization methods

DOEpatents

Kallman, Jeffrey S

2013-05-21

In one embodiment, a method includes selecting a subset of rays from a set of all rays to use in an error calculation for a constrained conjugate gradient minimization problem, calculating an approximate error using the subset of rays, and calculating a minimum in a conjugate gradient direction based on the approximate error. In another embodiment, a system includes a processor for executing logic, logic for selecting a subset of rays from a set of all rays to use in an error calculation for a constrained conjugate gradient minimization problem, logic for calculating an approximate error using the subset of rays, and logic for calculating a minimum in a conjugate gradient direction based on the approximate error. In other embodiments, computer program products, methods, and systems are described capable of using approximate error in constrained conjugate gradient minimization problems.

Optimization of OSEM parameters in myocardial perfusion imaging reconstruction as a function of body mass index: a clinical approach*

PubMed Central

de Barros, Pietro Paolo; Metello, Luis F.; Camozzato, Tatiane Sabriela Cagol; Vieira, Domingos Manuel da Silva

2015-01-01

Objective The present study is aimed at contributing to identify the most appropriate OSEM parameters to generate myocardial perfusion imaging reconstructions with the best diagnostic quality, correlating them with patients’ body mass index. Materials and Methods The present study included 28 adult patients submitted to myocardial perfusion imaging in a public hospital. The OSEM method was utilized in the images reconstruction with six different combinations of iterations and subsets numbers. The images were analyzed by nuclear cardiology specialists taking their diagnostic value into consideration and indicating the most appropriate images in terms of diagnostic quality. Results An overall scoring analysis demonstrated that the combination of four iterations and four subsets has generated the most appropriate images in terms of diagnostic quality for all the classes of body mass index; however, the role played by the combination of six iterations and four subsets is highlighted in relation to the higher body mass index classes. Conclusion The use of optimized parameters seems to play a relevant role in the generation of images with better diagnostic quality, ensuring the diagnosis and consequential appropriate and effective treatment for the patient. PMID:26543282

SU-F-T-312: Identifying Distinct Radiation Therapy Plan Classes Through Multi-Dimensional Analysis of Plan Complexity Metrics

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

Desai, V; Labby, Z; Culberson, W

Purpose: To determine whether body site-specific treatment plans form unique “plan class” clusters in a multi-dimensional analysis of plan complexity metrics such that a single beam quality correction determined for a representative plan could be universally applied within the “plan class”, thereby increasing the dosimetric accuracy of a detector’s response within a subset of similarly modulated nonstandard deliveries. Methods: We collected 95 clinical volumetric modulated arc therapy (VMAT) plans from four body sites (brain, lung, prostate, and spine). The lung data was further subdivided into SBRT and non-SBRT data for a total of five plan classes. For each control pointmore » in each plan, a variety of aperture-based complexity metrics were calculated and stored as unique characteristics of each patient plan. A multiple comparison of means analysis was performed such that every plan class was compared to every other plan class for every complexity metric in order to determine which groups could be considered different from one another. Statistical significance was assessed after correcting for multiple hypothesis testing. Results: Six out of a possible 10 pairwise plan class comparisons were uniquely distinguished based on at least nine out of 14 of the proposed metrics (Brain/Lung, Brain/SBRT lung, Lung/Prostate, Lung/SBRT Lung, Lung/Spine, Prostate/SBRT Lung). Eight out of 14 of the complexity metrics could distinguish at least six out of the possible 10 pairwise plan class comparisons. Conclusion: Aperture-based complexity metrics could prove to be useful tools to quantitatively describe a distinct class of treatment plans. Certain plan-averaged complexity metrics could be considered unique characteristics of a particular plan. A new approach to generating plan-class specific reference (pcsr) fields could be established through a targeted preservation of select complexity metrics or a clustering algorithm that identifies plans exhibiting similar modulation characteristics. Measurements and simulations will better elucidate potential plan-class specific dosimetry correction factors.« less

A data driven partial ambiguity resolution: Two step success rate criterion, and its simulation demonstration

NASA Astrophysics Data System (ADS)

Hou, Yanqing; Verhagen, Sandra; Wu, Jie

2016-12-01

Ambiguity Resolution (AR) is a key technique in GNSS precise positioning. In case of weak models (i.e., low precision of data), however, the success rate of AR may be low, which may consequently introduce large errors to the baseline solution in cases of wrong fixing. Partial Ambiguity Resolution (PAR) is therefore proposed such that the baseline precision can be improved by fixing only a subset of ambiguities with high success rate. This contribution proposes a new PAR strategy, allowing to select the subset such that the expected precision gain is maximized among a set of pre-selected subsets, while at the same time the failure rate is controlled. These pre-selected subsets are supposed to obtain the highest success rate among those with the same subset size. The strategy is called Two-step Success Rate Criterion (TSRC) as it will first try to fix a relatively large subset with the fixed failure rate ratio test (FFRT) to decide on acceptance or rejection. In case of rejection, a smaller subset will be fixed and validated by the ratio test so as to fulfill the overall failure rate criterion. It is shown how the method can be practically used, without introducing a large additional computation effort. And more importantly, how it can improve (or at least not deteriorate) the availability in terms of baseline precision comparing to classical Success Rate Criterion (SRC) PAR strategy, based on a simulation validation. In the simulation validation, significant improvements are obtained for single-GNSS on short baselines with dual-frequency observations. For dual-constellation GNSS, the improvement for single-frequency observations on short baselines is very significant, on average 68%. For the medium- to long baselines, with dual-constellation GNSS the average improvement is around 20-30%.

A Parameter Subset Selection Algorithm for Mixed-Effects Models

DOE PAGES

Schmidt, Kathleen L.; Smith, Ralph C.

2016-01-01

Mixed-effects models are commonly used to statistically model phenomena that include attributes associated with a population or general underlying mechanism as well as effects specific to individuals or components of the general mechanism. This can include individual effects associated with data from multiple experiments. However, the parameterizations used to incorporate the population and individual effects are often unidentifiable in the sense that parameters are not uniquely specified by the data. As a result, the current literature focuses on model selection, by which insensitive parameters are fixed or removed from the model. Model selection methods that employ information criteria are applicablemore » to both linear and nonlinear mixed-effects models, but such techniques are limited in that they are computationally prohibitive for large problems due to the number of possible models that must be tested. To limit the scope of possible models for model selection via information criteria, we introduce a parameter subset selection (PSS) algorithm for mixed-effects models, which orders the parameters by their significance. In conclusion, we provide examples to verify the effectiveness of the PSS algorithm and to test the performance of mixed-effects model selection that makes use of parameter subset selection.« less

Astronomy for Astronomical Numbers: a Worldwide Massive Open Online Class

NASA Astrophysics Data System (ADS)

Austin, Carmen; Impey, Chris David; Wenger, Matthew

2015-01-01

Astronomy: State of the Art is a massive, open, online class (MOOC) offered through Udemy by an instructional team at the University of Arizona. With over 18,000 enrolled, it is the largest astronomy MOOC available. The astronomical numbers enrolled do not translate into a similar level of engagement. The content consists of 14 hours of video lecture, nearly 1000 PowerPoint slides, 250 pages of background readings, and 20 podcast interviews with leading researchers. Perhaps in part because of the large amount of course content, the overall completion rate is low, about 3%. However, this number was four times higher for an early cohort of learners who were selected to have a prior interest in astronomy and who took the class in synchronous mode, with new content being added every week. Completion correlates with engagement as measured by posts to the online discussion board. For a subset of learners, social media like Facebook and Twitter provide an additional, important mode of engagement. For the asynchronous learners who have continuously enrolled for the past 15 months, those who complete the course do so quickly, with few persisting longer than two months. The availability of a completion certificate had no impact of completion rates. This experiment informs a future offering of this MOOC via Coursera, along with a co-convened 'flipped' introductory astronomy class at the University of Arizona, where the video lectures will be online and class time will be used exclusively for small group labs and hands-on activities. Despite their typically low completion rates, MOOCs have the potential to add significantly to public engagement with science.

A Framework for Empirical Discovery.

DTIC Science & Technology

1986-09-24

history of science reveal distinct classes of defined terms. Some systems have focused on one subset of these classes, while other programs have...the operators in detail, presenting examples of each from the history of science . 2.1 Defining Numeric Terms The most obvious operator for defining...laws; they can also simplify the process of discovering such laws. Let us consider some examples from the history of science in which the definition of

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Predictive biomarkers of sensitivity to the phosphatidylinositol 3' kinase inhibitor GDC-0941 in breast cancer preclinical models.

PubMed

O'Brien, Carol; Wallin, Jeffrey J; Sampath, Deepak; GuhaThakurta, Debraj; Savage, Heidi; Punnoose, Elizabeth A; Guan, Jane; Berry, Leanne; Prior, Wei Wei; Amler, Lukas C; Belvin, Marcia; Friedman, Lori S; Lackner, Mark R

2010-07-15

The class I phosphatidylinositol 3' kinase (PI3K) plays a major role in proliferation and survival in a wide variety of human cancers. A key factor in successful development of drugs targeting this pathway is likely to be the identification of responsive patient populations with predictive diagnostic biomarkers. This study sought to identify candidate biomarkers of response to the selective PI3K inhibitor GDC-0941. We used a large panel of breast cancer cell lines and in vivo xenograft models to identify candidate predictive biomarkers for a selective inhibitor of class I PI3K that is currently in clinical development. The approach involved pharmacogenomic profiling as well as analysis of gene expression data sets from cells profiled at baseline or after GDC-0941 treatment. We found that models harboring mutations in PIK3CA, amplification of human epidermal growth factor receptor 2, or dual alterations in two pathway components were exquisitely sensitive to the antitumor effects of GDC-0941. We found that several models that do not harbor these alterations also showed sensitivity, suggesting a need for additional diagnostic markers. Gene expression studies identified a collection of genes whose expression was associated with in vitro sensitivity to GDC-0941, and expression of a subset of these genes was found to be intimately linked to signaling through the pathway. Pathway focused biomarkers and the gene expression signature described in this study may have utility in the identification of patients likely to benefit from therapy with a selective PI3K inhibitor. Copyright 2010 AACR.

Forging T-Lymphocyte Identity: Intersecting Networks of Transcriptional Control

PubMed Central

Rothenberg, Ellen V.; Ungerbäck, Jonas; Champhekar, Ameya

2016-01-01

T lymphocyte development branches off from other lymphoid developmental programs through its requirement for sustained environmental signals through the Notch pathway. In the thymus, Notch signaling induces a succession of T-lineage regulatory factors that collectively create the T-cell identity through distinct steps. This process involves both the staged activation of T-cell identity genes and the staged repression of progenitor-cell-inherited regulatory genes once their roles in self-renewal and population expansion are no longer needed. With the recent characterization of Innate Lymphoid Cells (ILCs) that share transcriptional regulation programs extensively with T cell subsets, T-cell identity can increasingly be seen as defined in modular terms, as the processes selecting and actuating effector function are potentially detachable from the processes generating and selecting clonally unique T-cell receptor structures. The developmental pathways of different classes of T cells and ILCs are distinguished by the numbers of prerequisites of gene rearrangement, selection, and antigen contact before the cells gain access to nearly-common regulatory mechanisms for choosing effector function. Here, the major classes of transcription factors that interact with Notch signals during T-lineage specification are discussed in terms of their roles in these programs, the evidence for their spectra of target genes at different stages, and their cross-regulatory and cooperative actions with each other. Specific topics include Notch modulation of PU.1 and GATA-3, PU.1-Notch competition, the relationship between PU.1 and GATA-3, and the roles of E proteins, Bcl11b, and GATA-3 in guiding acquisition of T-cell identity while avoiding redirection to an ILC fate. PMID:26791859

Hemodynamic and clinical impact of ultrasound-derived venous reflux parameters.

PubMed

Neglén, Peter; Egger, John F; Olivier, Jake; Raju, Seshadri

2004-08-01

This study was undertaken to assess which ultrasound-derived parameter was superior for measuring venous reflux quantitatively and to evaluate the importance of popliteal vein valve reflux. A retrospective analysis was performed of 244 refluxive limbs in 182 patients who underwent ultrasound scanning, venous pressure measurement, air plethysmography, and clinical classification of severity according to the CEAP score. Reflux time (RT, s), peak reflux velocity (PRV, m/s), time of average rate of reflux (TAF, mL/min), absolute displaced volume retrogradely (ADV, mL) were compared to clinical class, ambulatory venous pressure (% drop), venous filling time (s), and venous filling index (mL/s) using nonparametric statistical tests. A P value of <.05 was considered significant. Limbs were divided into 3 groups: (A) axial great saphenous vein reflux only (n = 68); (B) axial deep reflux including popliteal vein incompetence with or without concomitant gastrocnemius or great or small saphenous vein reflux (all ultrasound reflux parameters of each refluxive vein added at the knee level) (n = 79); and (C) all limbs with popliteal vein reflux (the ultrasound data of the refluxive popliteal vein exclusively was used in comparison regardless of concomitant associated reflux) (n = 103). Limbs were also stratified into limbs with skin changes and ulcer (C-class 4-6) and those without (C-class 1-3) and subsequently compared. No meaningful significant correlation was found between RT and the clinical and hemodynamic results in groups A and B. The PRV and TAF correlated significantly with the hemodynamic parameters. The PRV and TAF and clinical severity trended towards correlation in group A (P =.0554 and P =.0998, respectively), but was significantly correlated in group B. The poor hemodynamic condition in the subset of C-class 4-6 limbs in groups A and B was reflected in a greater PRV, TAF, and ADV in this subset as compared with the limbs in C-class 1-3. RT was not significantly different in the subsets of limbs, further suggesting that RT is not related to hemodynamic or clinical state of the limbs. No meaningful correlations were found in group C. Although the hemodynamic data were significantly poorer in the subset of limbs with C-class 4-6 than in C-class 1-3, the ultrasound-derived parameters were not significantly different. The duration of valve reflux time (or valve closure time) cannot be used to quantify severity of reflux and is purely a qualitative measurement. The PRV and the rate of reflux appeared to better reflect the magnitude of venous incompetence. In the presence of axial reflux, it appeared logical and physiologically correct to sum up these reflux parameters for each venous segment crossing the knee. The popliteal valve reflux (the "gatekeeper" function) was not in itself an important determinant of venous hemodynamics and clinical severity. Additional reflux in other venous segments must be taken into account.

VARIABLE SELECTION FOR QUALITATIVE INTERACTIONS IN PERSONALIZED MEDICINE WHILE CONTROLLING THE FAMILY-WISE ERROR RATE

PubMed Central

Gunter, Lacey; Zhu, Ji; Murphy, Susan

2012-01-01

For many years, subset analysis has been a popular topic for the biostatistics and clinical trials literature. In more recent years, the discussion has focused on finding subsets of genomes which play a role in the effect of treatment, often referred to as stratified or personalized medicine. Though highly sought after, methods for detecting subsets with altering treatment effects are limited and lacking in power. In this article we discuss variable selection for qualitative interactions with the aim to discover these critical patient subsets. We propose a new technique designed specifically to find these interaction variables among a large set of variables while still controlling for the number of false discoveries. We compare this new method against standard qualitative interaction tests using simulations and give an example of its use on data from a randomized controlled trial for the treatment of depression. PMID:22023676

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012

PubMed Central

Love, William J.; Zawack, Kelson A.; Booth, James G.; Grӧhn, Yrjo T.

2016-01-01

Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five β-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71). Unweighted modularity did not appear to change over time (p = 0.18), but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005) indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic characteristics of the isolates with the current results derived from phenotypic data. PMID:27851767

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

PubMed

Love, William J; Zawack, Kelson A; Booth, James G; Grӧhn, Yrjo T; Lanzas, Cristina

2016-11-01

Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five β-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71). Unweighted modularity did not appear to change over time (p = 0.18), but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005) indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic characteristics of the isolates with the current results derived from phenotypic data.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

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.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.

PubMed

Carugo, Oliviero; Djinović-Carugo, Kristina

2016-01-01

It is often necessary to build subsets of the Protein Data Bank to extract structural trends and average values. For this purpose it is mandatory that the subsets are non-redundant and of high quality. The first problem can be solved relatively easily at the sequence level or at the structural level. The second, on the contrary, needs special attention. It is not sufficient, in fact, to consider the crystallographic resolution and other feature must be taken into account: the absence of strings of residues from the electron density maps and from the files deposited in the Protein Data Bank; the B-factor values; the appropriate validation of the structural models; the quality of the electron density maps, which is not uniform; and the temperature of the diffraction experiments. More stringent criteria produce smaller subsets, which can be enlarged with more tolerant selection criteria. The incessant growth of the Protein Data Bank and especially of the number of high-resolution structures is allowing the use of more stringent selection criteria, with a consequent improvement of the quality of the subsets of the Protein Data Bank.

Cladribine treatment of multiple sclerosis is associated with depletion of memory B cells.

PubMed

Ceronie, Bryan; Jacobs, Benjamin M; Baker, David; Dubuisson, Nicolas; Mao, Zhifeng; Ammoscato, Francesca; Lock, Helen; Longhurst, Hilary J; Giovannoni, Gavin; Schmierer, Klaus

2018-05-01

The mechanism of action of oral cladribine, recently licensed for relapsing multiple sclerosis, is unknown. To determine whether cladribine depletes memory B cells consistent with our recent hypothesis that effective, disease-modifying treatments act by physical/functional depletion of memory B cells. A cross-sectional study examined 40 people with multiple sclerosis at the end of the first cycle of alemtuzumab or injectable cladribine. The relative proportions and absolute numbers of peripheral blood B lymphocyte subsets were measured using flow cytometry. Cell-subtype expression of genes involved in cladribine metabolism was examined from data in public repositories. Cladribine markedly depleted class-switched and unswitched memory B cells to levels comparable with alemtuzumab, but without the associated initial lymphopenia. CD3 + T cell depletion was modest. The mRNA expression of metabolism genes varied between lymphocyte subsets. A high ratio of deoxycytidine kinase to group I cytosolic 5' nucleotidase expression was present in B cells and was particularly high in mature, memory and notably germinal centre B cells, but not plasma cells. Selective B cell cytotoxicity coupled with slow repopulation kinetics results in long-term, memory B cell depletion by cladribine. These may offer a new target, possibly with potential biomarker activity, for future drug development.

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

2010-08-01

Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

PubMed Central

Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Lagarde, Nathalie; Montes, Matthieu

2018-01-01

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets. PMID:29416509

An improved wrapper-based feature selection method for machinery fault diagnosis

PubMed Central

2017-01-01

A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks. PMID:29261689

Class I Histone Deacetylase Inhibition by Tianeptinaline Modulates Neuroplasticity and Enhances Memory.

PubMed

Zhao, Wen-Ning; Ghosh, Balaram; Tyler, Marshall; Lalonde, Jasmin; Joseph, Nadine F; Kosaric, Nina; Fass, Daniel M; Tsai, Li-Huei; Mazitschek, Ralph; Haggarty, Stephen J

2018-06-22

Through epigenetic and other regulatory functions, the histone deacetylase (HDAC) family of enzymes has emerged as a promising therapeutic target for central nervous system and other disorders. Here we report on the synthesis and functional characterization of new HDAC inhibitors based structurally on tianeptine, a drug used primarily to treat major depressive disorder (MDD) that has a poorly understood mechanism of action. Since the chemical structure of tianeptine resembles certain HDAC inhibitors, we profiled the in vitro HDAC inhibitory activity of tianeptine and demonstrated its ability to inhibit the lysine deacetylase activity of a subset of class I HDACs. Consistent with a model of active site Zn 2+ chelation by the carboxylic acid present in tianeptine, newly synthesized analogues containing either a hydroxamic acid or ortho-aminoanilide exhibited increased potency and selectivity among the HDAC family. This in vitro potency translated to improved efficacy in a panel of high-content imaging assays designed to assess HDAC target engagement and functional effects on critical pathways involved in neuroplasticity in both primary mouse neurons and, for the first time, human neurons differentiated from pluripotent stem cells. Most notably, tianeptinaline, a class I HDAC-selective analogue of tianeptine, but not tianeptine itself, increased histone acetylation, and enhanced CREB-mediated transcription and the expression of Arc (activity-regulated cytoskeleton-associated protein). Systemic in vivo administration of tianeptinaline to mice confirmed its brain penetration and was found to enhance contextual fear conditioning, a behavioral test of hippocampal-dependent memory. Tianeptinaline and its derivatives provide new pharmacological tools to dissect chromatin-mediated neuroplasticity underlying memory and other epigenetically related processes implicated in health and disease.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

Which products are available for subsetting?

Atmospheric Science Data Center

2014-12-08

... users to create smaller files (subsets) of the original data by selecting desired parameters, parameter criterion, or latitude and ... fluxes, where the net flux is constrained to the global heat storage in netCDF format. Single Scanner Footprint TOA/Surface Fluxes ...

New TES Search and Subset Application

Atmospheric Science Data Center

2017-08-23

... Wednesday, September 19, 2012 The Atmospheric Science Data Center (ASDC) at NASA Langley Research Center in collaboration ... pleased to announce the release of the TES Search and Subset Web Application for select TES Level 2 products. Features of the Search and ...

An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

NASA Astrophysics Data System (ADS)

Bhardwaj, Kaushal; Patra, Swarnajyoti

2018-04-01

Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

A Simple Joint Estimation Method of Residual Frequency Offset and Sampling Frequency Offset for DVB Systems

NASA Astrophysics Data System (ADS)

Kwon, Ki-Won; Cho, Yongsoo

This letter presents a simple joint estimation method for residual frequency offset (RFO) and sampling frequency offset (STO) in OFDM-based digital video broadcasting (DVB) systems. The proposed method selects a continual pilot (CP) subset from an unsymmetrically and non-uniformly distributed CP set to obtain an unbiased estimator. Simulation results show that the proposed method using a properly selected CP subset is unbiased and performs robustly.

SU-E-J-128: Two-Stage Atlas Selection in Multi-Atlas-Based Image Segmentation

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

Zhao, T; Ruan, D

2015-06-15

Purpose: In the new era of big data, multi-atlas-based image segmentation is challenged by heterogeneous atlas quality and high computation burden from extensive atlas collection, demanding efficient identification of the most relevant atlases. This study aims to develop a two-stage atlas selection scheme to achieve computational economy with performance guarantee. Methods: We develop a low-cost fusion set selection scheme by introducing a preliminary selection to trim full atlas collection into an augmented subset, alleviating the need for extensive full-fledged registrations. More specifically, fusion set selection is performed in two successive steps: preliminary selection and refinement. An augmented subset is firstmore » roughly selected from the whole atlas collection with a simple registration scheme and the corresponding preliminary relevance metric; the augmented subset is further refined into the desired fusion set size, using full-fledged registration and the associated relevance metric. The main novelty of this work is the introduction of an inference model to relate the preliminary and refined relevance metrics, based on which the augmented subset size is rigorously derived to ensure the desired atlases survive the preliminary selection with high probability. Results: The performance and complexity of the proposed two-stage atlas selection method were assessed using a collection of 30 prostate MR images. It achieved comparable segmentation accuracy as the conventional one-stage method with full-fledged registration, but significantly reduced computation time to 1/3 (from 30.82 to 11.04 min per segmentation). Compared with alternative one-stage cost-saving approach, the proposed scheme yielded superior performance with mean and medium DSC of (0.83, 0.85) compared to (0.74, 0.78). Conclusion: This work has developed a model-guided two-stage atlas selection scheme to achieve significant cost reduction while guaranteeing high segmentation accuracy. The benefit in both complexity and performance is expected to be most pronounced with large-scale heterogeneous data.« less

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Long-Term Prognostic Risk After Neoadjuvant Chemotherapy Associated With Residual Cancer Burden and Breast Cancer Subtype

PubMed Central

Wei, Caimiao; Gould, Rebekah; Yu, Xian; Zhang, Ya; Liu, Mei; Walls, Andrew; Bousamra, Alex; Ramineni, Maheshwari; Sinn, Bruno; Hunt, Kelly; Buchholz, Thomas A.; Valero, Vicente; Buzdar, Aman U.; Yang, Wei; Brewster, Abenaa M.; Moulder, Stacy; Pusztai, Lajos; Hatzis, Christos; Hortobagyi, Gabriel N.

2017-01-01

Purpose To determine the long-term prognosis in each phenotypic subset of breast cancer related to residual cancer burden (RCB) after neoadjuvant chemotherapy alone, or with concurrent human epidermal growth factor receptor 2 (HER2)–targeted treatment. Methods We conducted a pathologic review to measure the continuous RCB index (wherein pathologic complete response has RCB = 0; residual disease is categorized into three predefined classes of RCB index [RCB-I, RCB-II, and RCB-III]), and yp-stage of residual disease. Patients were prospectively observed for survival. Three patient cohorts received paclitaxel (T) followed by fluorouracil, doxorubicin, and cyclophosphamide (T/FAC): original development cohort (T/FAC-1), validation cohort (T/FAC-2), and independent validation cohort (T/FAC-3). Another validation cohort received FAC chemotherapy only, and a fifth cohort received concurrent trastuzumab (H) with sequential paclitaxel and fluorouracil, epirubicin, and cyclophosphamide (FEC; H+T/FEC). Phenotypic subsets were defined by hormone receptor (HR) and HER2 status at diagnosis, classified as HR-positive/HER2-negative, HER2-positive (HR-negative/HER2-positive or HR-positive/HER2-positive), or triple receptor–negative. Relapse-free survival estimates were determined from Kaplan-Meier analysis and compared using the log-rank test. Results Five cohorts (T/FAC-1 [n = 219], T/FAC-2 [n = 262], T/FAC-3 [n = 342], FAC [n = 132], and H+T/FEC [n = 203]) had median event-free follow-up of 13.5, 9.1, 6.8, 16.4, and 7.1 years, respectively. Continuous RCB index was prognostic within each phenotypic subset, independent of other clinical-pathologic variables. RCB classes stratified prognostic risk overall, within each phenotypic subset, and within yp-stage categories. Estimates of 10-year relapse-free survival rates in the four RCB classes (pathologic complete response, RCB-I, RCB-II, and RCB-III) were 86%, 81%, 55%, and 23% for triple receptor–negative; 83%, 97%, 74%, and 52% for HR-positive/HER2-negative in the combined T/FAC cohorts; and 95%, 77%, 47%, and 21% in the H+T/FEC cohort. Conclusion RCB was prognostic for long-term survival after neoadjuvant chemotherapy in all three phenotypic subsets of breast cancer. Our institutional findings should be externally validated. PMID:28135148

GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets.

PubMed

Jeong, Seongmun; Kim, Jae-Yoon; Jeong, Soon-Chun; Kang, Sung-Taeg; Moon, Jung-Kyung; Kim, Namshin

2017-01-01

Selecting core subsets from plant genotype datasets is important for enhancing cost-effectiveness and to shorten the time required for analyses of genome-wide association studies (GWAS), and genomics-assisted breeding of crop species, etc. Recently, a large number of genetic markers (>100,000 single nucleotide polymorphisms) have been identified from high-density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) data. However, there is no software available for picking out the efficient and consistent core subset from such a huge dataset. It is necessary to develop software that can extract genetically important samples in a population with coherence. We here present a new program, GenoCore, which can find quickly and efficiently the core subset representing the entire population. We introduce simple measures of coverage and diversity scores, which reflect genotype errors and genetic variations, and can help to select a sample rapidly and accurately for crop genotype dataset. Comparison of our method to other core collection software using example datasets are performed to validate the performance according to genetic distance, diversity, coverage, required system resources, and the number of selected samples. GenoCore selects the smallest, most consistent, and most representative core collection from all samples, using less memory with more efficient scores, and shows greater genetic coverage compared to the other software tested. GenoCore was written in R language, and can be accessed online with an example dataset and test results at https://github.com/lovemun/Genocore.

The continuum spectral characteristics of gamma-ray bursts observed by BATSE

NASA Technical Reports Server (NTRS)

Pendleton, Geoffrey N.; Paciesas, William S.; Briggs, Michael S.; Mallozzi, Robert S.; Koshut, Tom M.; Fishman, Gerald J.; Meegan, Charles A.; Wilson, Robert B.; Harmon, Alan B.; Kouveliotou, Chryssa

1994-01-01

Distributions of the continuum spectral characteristics of 260 bursts in the first Burst And Transient Source Experiement (BATSE) catalog are presented. The data are derived from flux calculated from BATSE Large Area Detector (LAD) four-channel discriminator data. The data are converted from counts to protons using a direct spectral inversion technique to remove the effects of atmospheric scattering and the energy dependence of the detector angular response. Although there are intriguing clusters of bursts in the spectral hardness ratio distributions, no evidence for the presence of distinct burst classes based in spectral hardness ratios alone is found. All subsets of bursts selected for their spectral characteristics in this analysis exhibit spatial distributions consistent with isotropy. The spectral diversity of the burst population appears to be caused largely by the highly variable nature of the burst production mechanisms themselves.

Performance Analysis of Relay Subset Selection for Amplify-and-Forward Cognitive Relay Networks

PubMed Central

Qureshi, Ijaz Mansoor; Malik, Aqdas Naveed; Zubair, Muhammad

2014-01-01

Cooperative communication is regarded as a key technology in wireless networks, including cognitive radio networks (CRNs), which increases the diversity order of the signal to combat the unfavorable effects of the fading channels, by allowing distributed terminals to collaborate through sophisticated signal processing. Underlay CRNs have strict interference constraints towards the secondary users (SUs) active in the frequency band of the primary users (PUs), which limits their transmit power and their coverage area. Relay selection offers a potential solution to the challenges faced by underlay networks, by selecting either single best relay or a subset of potential relay set under different design requirements and assumptions. The best relay selection schemes proposed in the literature for amplify-and-forward (AF) based underlay cognitive relay networks have been very well studied in terms of outage probability (OP) and bit error rate (BER), which is deficient in multiple relay selection schemes. The novelty of this work is to study the outage behavior of multiple relay selection in the underlay CRN and derive the closed-form expressions for the OP and BER through cumulative distribution function (CDF) of the SNR received at the destination. The effectiveness of relay subset selection is shown through simulation results. PMID:24737980

Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

2018-04-01

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be used to identify optimal datasets for vegetation community mapping.

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

PubMed Central

Zhang, Xiong; Zhao, Yacong; Zhang, Yu; Zhong, Xuefei; Fan, Zhaowen

2018-01-01

The novel human-computer interface (HCI) using bioelectrical signals as input is a valuable tool to improve the lives of people with disabilities. In this paper, surface electromyography (sEMG) signals induced by four classes of wrist movements were acquired from four sites on the lower arm with our designed system. Forty-two features were extracted from the time, frequency and time-frequency domains. Optimal channels were determined from single-channel classification performance rank. The optimal-feature selection was according to a modified entropy criteria (EC) and Fisher discrimination (FD) criteria. The feature selection results were evaluated by four different classifiers, and compared with other conventional feature subsets. In online tests, the wearable system acquired real-time sEMG signals. The selected features and trained classifier model were used to control a telecar through four different paradigms in a designed environment with simple obstacles. Performance was evaluated based on travel time (TT) and recognition rate (RR). The results of hardware evaluation verified the feasibility of our acquisition systems, and ensured signal quality. Single-channel analysis results indicated that the channel located on the extensor carpi ulnaris (ECU) performed best with mean classification accuracy of 97.45% for all movement’s pairs. Channels placed on ECU and the extensor carpi radialis (ECR) were selected according to the accuracy rank. Experimental results showed that the proposed FD method was better than other feature selection methods and single-type features. The combination of FD and random forest (RF) performed best in offline analysis, with 96.77% multi-class RR. Online results illustrated that the state-machine paradigm with a 125 ms window had the highest maneuverability and was closest to real-life control. Subjects could accomplish online sessions by three sEMG-based paradigms, with average times of 46.02, 49.06 and 48.08 s, respectively. These experiments validate the feasibility of proposed real-time wearable HCI system and algorithms, providing a potential assistive device interface for persons with disabilities. PMID:29543737

Genomic selection accuracies within and between environments and small breeding groups in white spruce.

PubMed

Beaulieu, Jean; Doerksen, Trevor K; MacKay, John; Rainville, André; Bousquet, Jean

2014-12-02

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested. Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies. Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

Choosing non-redundant representative subsets of protein sequence data sets using submodular optimization.

PubMed

Libbrecht, Maxwell W; Bilmes, Jeffrey A; Noble, William Stafford

2018-04-01

Selecting a non-redundant representative subset of sequences is a common step in many bioinformatics workflows, such as the creation of non-redundant training sets for sequence and structural models or selection of "operational taxonomic units" from metagenomics data. Previous methods for this task, such as CD-HIT, PISCES, and UCLUST, apply a heuristic threshold-based algorithm that has no theoretical guarantees. We propose a new approach based on submodular optimization. Submodular optimization, a discrete analogue to continuous convex optimization, has been used with great success for other representative set selection problems. We demonstrate that the submodular optimization approach results in representative protein sequence subsets with greater structural diversity than sets chosen by existing methods, using as a gold standard the SCOPe library of protein domain structures. In this setting, submodular optimization consistently yields protein sequence subsets that include more SCOPe domain families than sets of the same size selected by competing approaches. We also show how the optimization framework allows us to design a mixture objective function that performs well for both large and small representative sets. The framework we describe is the best possible in polynomial time (under some assumptions), and it is flexible and intuitive because it applies a suite of generic methods to optimize one of a variety of objective functions. © 2018 Wiley Periodicals, Inc.

Variable selection with stepwise and best subset approaches

PubMed Central

2016-01-01

While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion. PMID:27162786

Evolution of extortion in Iterated Prisoner's Dilemma games.

PubMed

Hilbe, Christian; Nowak, Martin A; Sigmund, Karl

2013-04-23

Iterated games are a fundamental component of economic and evolutionary game theory. They describe situations where two players interact repeatedly and have the ability to use conditional strategies that depend on the outcome of previous interactions, thus allowing for reciprocation. Recently, a new class of strategies has been proposed, so-called "zero-determinant" strategies. These strategies enforce a fixed linear relationship between one's own payoff and that of the other player. A subset of those strategies allows "extortioners" to ensure that any increase in one player's own payoff exceeds that of the other player by a fixed percentage. Here, we analyze the evolutionary performance of this new class of strategies. We show that in reasonably large populations, they can act as catalysts for the evolution of cooperation, similar to tit-for-tat, but that they are not the stable outcome of natural selection. In very small populations, however, extortioners hold their ground. Extortion strategies do particularly well in coevolutionary arms races between two distinct populations. Significantly, they benefit the population that evolves at the slower rate, an example of the so-called "Red King" effect. This may affect the evolution of interactions between host species and their endosymbionts.

An objective and parsimonious approach for classifying natural flow regimes at a continental scale

NASA Astrophysics Data System (ADS)

Archfield, S. A.; Kennen, J.; Carlisle, D.; Wolock, D.

2013-12-01

Hydroecological stream classification--the process of grouping streams by similar hydrologic responses and, thereby, similar aquatic habitat--has been widely accepted and is often one of the first steps towards developing ecological flow targets. Despite its importance, the last national classification of streamgauges was completed about 20 years ago. A new classification of 1,534 streamgauges in the contiguous United States is presented using a novel and parsimonious approach to understand similarity in ecological streamflow response. This new classification approach uses seven fundamental daily streamflow statistics (FDSS) rather than winnowing down an uncorrelated subset from 200 or more ecologically relevant streamflow statistics (ERSS) commonly used in hydroecological classification studies. The results of this investigation demonstrate that the distributions of 33 tested ERSS are consistently different among the classes derived from the seven FDSS. It is further shown that classification based solely on the 33 ERSS generally does a poorer job in grouping similar streamgauges than the classification based on the seven FDSS. This new classification approach has the additional advantages of overcoming some of the subjectivity associated with the selection of the classification variables and provides a set of robust continental-scale classes of US streamgauges.

Evolution of extortion in Iterated Prisoner’s Dilemma games

PubMed Central

Hilbe, Christian; Nowak, Martin A.; Sigmund, Karl

2013-01-01

Iterated games are a fundamental component of economic and evolutionary game theory. They describe situations where two players interact repeatedly and have the ability to use conditional strategies that depend on the outcome of previous interactions, thus allowing for reciprocation. Recently, a new class of strategies has been proposed, so-called “zero-determinant” strategies. These strategies enforce a fixed linear relationship between one’s own payoff and that of the other player. A subset of those strategies allows “extortioners” to ensure that any increase in one player’s own payoff exceeds that of the other player by a fixed percentage. Here, we analyze the evolutionary performance of this new class of strategies. We show that in reasonably large populations, they can act as catalysts for the evolution of cooperation, similar to tit-for-tat, but that they are not the stable outcome of natural selection. In very small populations, however, extortioners hold their ground. Extortion strategies do particularly well in coevolutionary arms races between two distinct populations. Significantly, they benefit the population that evolves at the slower rate, an example of the so-called “Red King” effect. This may affect the evolution of interactions between host species and their endosymbionts. PMID:23572576

Automatic detection of atrial fibrillation in cardiac vibration signals.

PubMed

Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S

2013-01-01

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies.

PubMed

Bidwell, Jonathan; Khuwatsamrit, Thanin; Askew, Brittain; Ehrenberg, Joshua Andrew; Helmers, Sandra

2015-11-01

This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use. Copyright © 2015. Published by Elsevier Ltd.

Aggregating job exit statuses of a plurality of compute nodes executing a parallel application

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

Aho, Michael E.; Attinella, John E.; Gooding, Thomas M.

Aggregating job exit statuses of a plurality of compute nodes executing a parallel application, including: identifying a subset of compute nodes in the parallel computer to execute the parallel application; selecting one compute node in the subset of compute nodes in the parallel computer as a job leader compute node; initiating execution of the parallel application on the subset of compute nodes; receiving an exit status from each compute node in the subset of compute nodes, where the exit status for each compute node includes information describing execution of some portion of the parallel application by the compute node; aggregatingmore » each exit status from each compute node in the subset of compute nodes; and sending an aggregated exit status for the subset of compute nodes in the parallel computer.« less

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

Ditzler, Gregory; Morrison, J. Calvin; Lan, Yemin

Background: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α– & β–diversity. Feature subset selection – a sub-field of machine learning – can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate betweenmore » age groups in the human gut microbiome. Results: We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. Conclusions: We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.« less

Subset selective search on the basis of color and preview.

PubMed

Donk, Mieke

2017-01-01

In the preview paradigm observers are presented with one set of elements (the irrelevant set) followed by the addition of a second set among which the target is presented (the relevant set). Search efficiency in such a preview condition has been demonstrated to be higher than that in a full-baseline condition in which both sets are simultaneously presented, suggesting that a preview of the irrelevant set reduces its influence on the search process. However, numbers of irrelevant and relevant elements are typically not independently manipulated. Moreover, subset selective search also occurs when both sets are presented simultaneously but differ in color. The aim of the present study was to investigate how numbers of irrelevant and relevant elements contribute to preview search in the absence and presence of a color difference between subsets. In two experiments it was demonstrated that a preview reduced the influence of the number of irrelevant elements in the absence but not in the presence of a color difference between subsets. In the presence of a color difference, a preview lowered the effect of the number of relevant elements but only when the target was defined by a unique feature within the relevant set (Experiment 1); when the target was defined by a conjunction of features (Experiment 2), search efficiency as a function of the number of relevant elements was not modulated by a preview. Together the results are in line with the idea that subset selective search is based on different simultaneously operating mechanisms.

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.

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

PubMed Central

2012-01-01

Background Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. PMID:22830977

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

Differential modulation of FXR activity by chlorophacinone and ivermectin analogs

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

Hsu, Chia-Wen

Chemicals that alter normal function of farnesoid X receptor (FXR) have been shown to affect the homeostasis of bile acids, glucose, and lipids. Several structural classes of environmental chemicals and drugs that modulated FXR transactivation were previously identified by quantitative high-throughput screening (qHTS) of the Tox21 10 K chemical collection. In the present study, we validated the FXR antagonist activity of selected structural classes, including avermectin anthelmintics, dihydropyridine calcium channel blockers, 1,3-indandione rodenticides, and pyrethroid pesticides, using in vitro assay and quantitative structural-activity relationship (QSAR) analysis approaches. (Z)-Guggulsterone, chlorophacinone, ivermectin, and their analogs were profiled for their ability to altermore » CDCA-mediated FXR binding using a panel of 154 coregulator motifs and to induce or inhibit transactivation and coactivator recruitment activities of constitutive androstane receptor (CAR), liver X receptor alpha (LXRα), or pregnane X receptor (PXR). Our results showed that chlorophacinone and ivermectin had distinct modes of action (MOA) in modulating FXR-coregulator interactions and compound selectivity against the four aforementioned functionally-relevant nuclear receptors. These findings collectively provide mechanistic insights regarding compound activities against FXR and possible explanations for in vivo toxicological observations of chlorophacinone, ivermectin, and their analogs. - Highlights: • A subset of Tox21 chemicals was investigated for FXR antagonism. • In vitro and computational approaches were used to evaluate FXR antagonists. • Chlorophacinone and ivermectin had distinct patterns in modulating FXR activity.« less

Next-generation transcriptome sequencing, SNP discovery and validation in four market classes of peanut, Arachis hypogaea L.

PubMed

Chopra, Ratan; Burow, Gloria; Farmer, Andrew; Mudge, Joann; Simpson, Charles E; Wilkins, Thea A; Baring, Michael R; Puppala, Naveen; Chamberlin, Kelly D; Burow, Mark D

2015-06-01

Single-nucleotide polymorphisms, which can be identified in the thousands or millions from comparisons of transcriptome or genome sequences, are ideally suited for making high-resolution genetic maps, investigating population evolutionary history, and discovering marker-trait linkages. Despite significant results from their use in human genetics, progress in identification and use in plants, and particularly polyploid plants, has lagged. As part of a long-term project to identify and use SNPs suitable for these purposes in cultivated peanut, which is tetraploid, we generated transcriptome sequences of four peanut cultivars, namely OLin, New Mexico Valencia C, Tamrun OL07 and Jupiter, which represent the four major market classes of peanut grown in the world, and which are important economically to the US southwest peanut growing region. CopyDNA libraries of each genotype were used to generate 2 × 54 paired-end reads using an Illumina GAIIx sequencer. Raw reads were mapped to a custom reference consisting of Tifrunner 454 sequences plus peanut ESTs in GenBank, compromising 43,108 contigs; 263,840 SNP and indel variants were identified among four genotypes compared to the reference. A subset of 6 variants was assayed across 24 genotypes representing four market types using KASP chemistry to assess the criteria for SNP selection. Results demonstrated that transcriptome sequencing can identify SNPs usable as selectable DNA-based markers in complex polyploid species such as peanut. Criteria for effective use of SNPs as markers are discussed in this context.

Identification of features in indexed data and equipment therefore

DOEpatents

Jarman, Kristin H [Richland, WA; Daly, Don Simone [Richland, WA; Anderson, Kevin K [Richland, WA; Wahl, Karen L [Richland, WA

2002-04-02

Embodiments of the present invention provide methods of identifying a feature in an indexed dataset. Such embodiments encompass selecting an initial subset of indices, the initial subset of indices being encompassed by an initial window-of-interest and comprising at least one beginning index and at least one ending index; computing an intensity weighted measure of dispersion for the subset of indices using a subset of responses corresponding to the subset of indices; and comparing the intensity weighted measure of dispersion to a dispersion critical value determined from an expected value of the intensity weighted measure of dispersion under a null hypothesis of no transient feature present. Embodiments of the present invention also encompass equipment configured to perform the methods of the present invention.

Efficient feature subset selection with probabilistic distance criteria. [pattern recognition

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Recursive expressions are derived for efficiently computing the commonly used probabilistic distance measures as a change in the criteria both when a feature is added to and when a feature is deleted from the current feature subset. A combinatorial algorithm for generating all possible r feature combinations from a given set of s features in (s/r) steps with a change of a single feature at each step is presented. These expressions can also be used for both forward and backward sequential feature selection.

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

Forging T-Lymphocyte Identity: Intersecting Networks of Transcriptional Control.

PubMed

Rothenberg, Ellen V; Ungerbäck, Jonas; Champhekar, Ameya

2016-01-01

T-lymphocyte development branches off from other lymphoid developmental programs through its requirement for sustained environmental signals through the Notch pathway. In the thymus, Notch signaling induces a succession of T-lineage regulatory factors that collectively create the T-cell identity through distinct steps. This process involves both the staged activation of T-cell identity genes and the staged repression of progenitor-cell-inherited regulatory genes once their roles in self-renewal and population expansion are no longer needed. With the recent characterization of innate lymphoid cells (ILCs) that share transcriptional regulation programs extensively with T-cell subsets, T-cell identity can increasingly be seen as defined in modular terms, as the processes selecting and actuating effector function are potentially detachable from the processes generating and selecting clonally unique T-cell receptor structures. The developmental pathways of different classes of T cells and ILCs are distinguished by the numbers of prerequisites of gene rearrangement, selection, and antigen contact before the cells gain access to nearly common regulatory mechanisms for choosing effector function. Here, the major classes of transcription factors that interact with Notch signals during T-lineage specification are discussed in terms of their roles in these programs, the evidence for their spectra of target genes at different stages, and their cross-regulatory and cooperative actions with each other. Specific topics include Notch modulation of PU.1 and GATA-3, PU.1-Notch competition, the relationship between PU.1 and GATA-3, and the roles of E proteins, Bcl11b, and GATA-3 in guiding acquisition of T-cell identity while avoiding redirection to an ILC fate. © 2016 Elsevier Inc. All rights reserved.

Understanding MHC Class I Presentation of Viral Antigens by Human Dendritic Cells as a Basis for Rational Design of Therapeutic Vaccines

PubMed Central

van Montfoort, Nadine; van der Aa, Evelyn; Woltman, Andrea M.

2014-01-01

Effective viral clearance requires the induction of virus-specific CD8+ cytotoxic T lymphocytes (CTL). Since dendritic cells (DC) have a central role in initiating and shaping virus-specific CTL responses, it is important to understand how DC initiate virus-specific CTL responses. Some viruses can directly infect DC, which theoretically allow direct presentation of viral antigens to CTL, but many viruses target other cells than DC and thus the host depends on the cross-presentation of viral antigens by DC to activate virus-specific CTL. Research in mouse models has highly enhanced our understanding of the mechanisms underlying cross-presentation and the dendritic cells (DC) subsets involved, however, these results cannot be readily translated toward the role of human DC in MHC class I-antigen presentation of human viruses. Here, we summarize the insights gained in the past 20 years on MHC class I presentation of viral antigen by human DC and add to the current debate on the capacities of different human DC subsets herein. Furthermore, possible sources of viral antigens and essential DC characteristics for effective induction of virus-specific CTL are evaluated. We conclude that cross-presentation is not only an efficient mechanism exploited by DC to initiate immunity to viruses that do not infect DC but also to viruses that do infect DC, because cross-presentation has many conceptual advantages and bypasses direct immune modulatory effects of the virus on its infected target cells. Since knowledge on the mechanism of viral antigen presentation and the preferred DC subsets is crucial for rational vaccine design, the obtained insights are very instrumental for the development of effective anti-viral immunotherapy. PMID:24795724

Identification of selection signatures in cattle breeds selected for dairy production.

PubMed

Stella, Alessandra; Ajmone-Marsan, Paolo; Lazzari, Barbara; Boettcher, Paul

2010-08-01

The genomics revolution has spurred the undertaking of HapMap studies of numerous species, allowing for population genomics to increase the understanding of how selection has created genetic differences between subspecies populations. The objectives of this study were to (1) develop an approach to detect signatures of selection in subsets of phenotypically similar breeds of livestock by comparing single nucleotide polymorphism (SNP) diversity between the subset and a larger population, (2) verify this method in breeds selected for simply inherited traits, and (3) apply this method to the dairy breeds in the International Bovine HapMap (IBHM) study. The data consisted of genotypes for 32,689 SNPs of 497 animals from 19 breeds. For a given subset of breeds, the test statistic was the parametric composite log likelihood (CLL) of the differences in allelic frequencies between the subset and the IBHM for a sliding window of SNPs. The null distribution was obtained by calculating CLL for 50,000 random subsets (per chromosome) of individuals. The validity of this approach was confirmed by obtaining extremely large CLLs at the sites of causative variation for polled (BTA1) and black-coat-color (BTA18) phenotypes. Across the 30 bovine chromosomes, 699 putative selection signatures were detected. The largest CLL was on BTA6 and corresponded to KIT, which is responsible for the piebald phenotype present in four of the five dairy breeds. Potassium channel-related genes were at the site of the largest CLL on three chromosomes (BTA14, -16, and -25) whereas integrins (BTA18 and -19) and serine/arginine rich splicing factors (BTA20 and -23) each had the largest CLL on two chromosomes. On the basis of the results of this study, the application of population genomics to farm animals seems quite promising. Comparisons between breed groups have the potential to identify genomic regions influencing complex traits with no need for complex equipment and the collection of extensive phenotypic records and can contribute to the identification of candidate genes and to the understanding of the biological mechanisms controlling complex traits.

Identification of petroleum hydrocarbons using a reduced number of PAHs selected by Procrustes rotation.

PubMed

Fernández-Varela, R; Andrade, J M; Muniategui, S; Prada, D; Ramírez-Villalobos, F

2010-04-01

Identifying petroleum-related products released into the environment is a complex and difficult task. To achieve this, polycyclic aromatic hydrocarbons (PAHs) are of outstanding importance nowadays. Despite traditional quantitative fingerprinting uses straightforward univariate statistical analyses to differentiate among oils and to assess their sources, a multivariate strategy based on Procrustes rotation (PR) was applied in this paper. The aim of PR is to select a reduced subset of PAHs still capable of performing a satisfactory identification of petroleum-related hydrocarbons. PR selected two subsets of three (C(2)-naphthalene, C(2)-dibenzothiophene and C(2)-phenanthrene) and five (C(1)-decahidronaphthalene, naphthalene, C(2)-phenanthrene, C(3)-phenanthrene and C(2)-fluoranthene) PAHs for each of the two datasets studied here. The classification abilities of each subset of PAHs were tested using principal components analysis, hierarchical cluster analysis and Kohonen neural networks and it was demonstrated that they unraveled the same patterns as the overall set of PAHs. (c) 2009 Elsevier Ltd. All rights reserved.

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

PubMed Central

2010-01-01

Background At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI). Methods Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length. Results RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls. Conclusions Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP. PMID:20950478

Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers.

PubMed

Weigel, K A; de los Campos, G; González-Recio, O; Naya, H; Wu, X L; Long, N; Rosa, G J M; Gianola, D

2009-10-01

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R(2)) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R(2) values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.

Coverability graphs for a class of synchronously executed unbounded Petri net

NASA Technical Reports Server (NTRS)

Stotts, P. David; Pratt, Terrence W.

1990-01-01

After detailing a variant of the concurrent-execution rule for firing of maximal subsets, in which the simultaneous firing of conflicting transitions is prohibited, an algorithm is constructed for generating the coverability graph of a net executed under this synchronous firing rule. The omega insertion criteria in the algorithm are shown to be valid for any net on which the algorithm terminates. It is accordingly shown that the set of nets on which the algorithm terminates includes the 'conflict-free' class.

Probabilistic streamflow forecasting for hydroelectricity production: A comparison of two non-parametric system identification algorithms

NASA Astrophysics Data System (ADS)

Pande, Saket; Sharma, Ashish

2014-05-01

This study is motivated by the need to robustly specify, identify, and forecast runoff generation processes for hydroelectricity production. It atleast requires the identification of significant predictors of runoff generation and the influence of each such significant predictor on runoff response. To this end, we compare two non-parametric algorithms of predictor subset selection. One is based on information theory that assesses predictor significance (and hence selection) based on Partial Information (PI) rationale of Sharma and Mehrotra (2014). The other algorithm is based on a frequentist approach that uses bounds on probability of error concept of Pande (2005), assesses all possible predictor subsets on-the-go and converges to a predictor subset in an computationally efficient manner. Both the algorithms approximate the underlying system by locally constant functions and select predictor subsets corresponding to these functions. The performance of the two algorithms is compared on a set of synthetic case studies as well as a real world case study of inflow forecasting. References: Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 49, doi:10.1002/2013WR013845. Pande, S. (2005), Generalized local learning in water resource management, PhD dissertation, Utah State University, UT-USA, 148p.

Compositional differences between meteorites and near-Earth asteroids.

PubMed

Vernazza, P; Binzel, R P; Thomas, C A; DeMeo, F E; Bus, S J; Rivkin, A S; Tokunaga, A T

2008-08-14

Understanding the nature and origin of the asteroid population in Earth's vicinity (near-Earth asteroids, and its subset of potentially hazardous asteroids) is a matter of both scientific interest and practical importance. It is generally expected that the compositions of the asteroids that are most likely to hit Earth should reflect those of the most common meteorites. Here we report that most near-Earth asteroids (including the potentially hazardous subset) have spectral properties quantitatively similar to the class of meteorites known as LL chondrites. The prominent Flora family in the inner part of the asteroid belt shares the same spectral properties, suggesting that it is a dominant source of near-Earth asteroids. The observed similarity of near-Earth asteroids to LL chondrites is, however, surprising, as this meteorite class is relatively rare ( approximately 8 per cent of all meteorite falls). One possible explanation is the role of a size-dependent process, such as the Yarkovsky effect, in transporting material from the main belt.

Relevant, irredundant feature selection and noisy example elimination.

PubMed

Lashkia, George V; Anthony, Laurence

2004-04-01

In many real-world situations, the method for computing the desired output from a set of inputs is unknown. One strategy for solving these types of problems is to learn the input-output functionality from examples in a training set. However, in many situations it is difficult to know what information is relevant to the task at hand. Subsequently, researchers have investigated ways to deal with the so-called problem of consistency of attributes, i.e., attributes that can distinguish examples from different classes. In this paper, we first prove that the notion of relevance of attributes is directly related to the consistency of attributes, and show how relevant, irredundant attributes can be selected. We then compare different relevant attribute selection algorithms, and show the superiority of algorithms that select irredundant attributes over those that select relevant attributes. We also show that searching for an "optimal" subset of attributes, which is considered to be the main purpose of attribute selection, is not the best way to improve the accuracy of classifiers. Employing sets of relevant, irredundant attributes improves classification accuracy in many more cases. Finally, we propose a new method for selecting relevant examples, which is based on filtering the so-called pattern frequency domain. By identifying examples that are nontypical in the determination of relevant, irredundant attributes, irrelevant examples can be eliminated prior to the learning process. Empirical results using artificial and real databases show the effectiveness of the proposed method in selecting relevant examples leading to improved performance even on greatly reduced training sets.

An abstract class loader for the SSP and its implementation in TL.

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

Wickstrom, Gregory Lloyd; Winter, Victor Lono; Fraij, Fares

2004-08-01

The SSP is a hardware implementation of a subset of the JVM for use in high consequence embedded applications. In this context, a majority of the activities belonging to class loading, as it is defined in the specification of the JVM, can be performed statically. Static class loading has the net result of dramatically simplifying the design of the SSP as well as increasing its performance. Due to the high consequence nature of its applications, strong evidence must be provided that all aspects of the SSP have been implemented correctly. This includes the class loader. This article explores the possibilitymore » of formally verifying a class loader for the SSP implemented in the strategic programming language TL. Specifically, an implementation of the core activities of an abstract class loader is presented and its verification in ACL2 is considered.« less

Ordering Elements and Subsets: Examples for Student Understanding

ERIC Educational Resources Information Center

Mellinger, Keith E.

2004-01-01

Teaching the art of counting can be quite difficult. Many undergraduate students have difficulty separating the ideas of permutation, combination, repetition, etc. This article develops some examples to help explain some of the underlying theory while looking carefully at the selection of various subsets of objects from a larger collection. The…

Energy and the Environment: A Thematic Approach to Teaching Physics

NASA Astrophysics Data System (ADS)

Cushman, Priscilla

2000-04-01

Most physics teachers have a set of core concepts which they believe to be fundamental to understanding physics. However, an attempt to present the complete set to a liberal arts audience in a semester introductory course usually results in a disconnected series of topics. Students compensate by relying on formulae and memorization. Selecting a smaller subset of unrelated topics from a general purpose textbook is not the answer either. Instead, the appropriate choice of unifying theme can force the students to organize their thinking and thereby understand the material. Energy is a useful theme, since it is embedded in all aspects of physics. Maintaining the quality of our environment is an easily accepted ``good" and provides the motivation for worked problems and discussions which make the physics relevant to everyday life. Experience with introducing such a curriculum at the University of Minnesota is presented, including tips for keeping the class on track and involved.

The continuum spectral characteristics of gamma ray bursts observed by BATSE

NASA Technical Reports Server (NTRS)

Pendleton, Geoffrey N.; Paciesas, William S.; Briggs, Michael S.; Mallozzi, Robert S.; Koshut, Tom M.; Fishman, Gerald J.; Meegan, Charles A.; Wilson, Robert B.; Harmon, Alan B.; Kouveliotou, Chryssa

1994-01-01

Distributions of the continuum spectral characteristics of 260 bursts in the first Burst and Transient Source Experiment (BATSE) catalog are presented. The data are derived from flux ratios calculated from the BATSE Large Area Detector (LAD) four channel discriminator data. The data are converted from counts to photons using a direct spectral inversion technique to remove the effects of atmospheric scattering and the energy dependence of the detector angular response. Although there are intriguing clusterings of bursts in the spectral hardness ratio distributions, no evidence for the presence of distinct burst classes based on spectral hardness ratios alone is found. All subsets of bursts selected for their spectral characteristics in this analysis exhibit spatial distributions consistent with isotropy. The spectral diversity of the burst population appears to be caused largely by the highly variable nature of the burst production mechanisms themselves.

ASL-LEX: A lexical database of American Sign Language.

PubMed

Caselli, Naomi K; Sehyr, Zed Sevcikova; Cohen-Goldberg, Ariel M; Emmorey, Karen

2017-04-01

ASL-LEX is a lexical database that catalogues information about nearly 1,000 signs in American Sign Language (ASL). It includes the following information: subjective frequency ratings from 25-31 deaf signers, iconicity ratings from 21-37 hearing non-signers, videoclip duration, sign length (onset and offset), grammatical class, and whether the sign is initialized, a fingerspelled loan sign, or a compound. Information about English translations is available for a subset of signs (e.g., alternate translations, translation consistency). In addition, phonological properties (sign type, selected fingers, flexion, major and minor location, and movement) were coded and used to generate sub-lexical frequency and neighborhood density estimates. ASL-LEX is intended for use by researchers, educators, and students who are interested in the properties of the ASL lexicon. An interactive website where the database can be browsed and downloaded is available at http://asl-lex.org .

ASL-LEX: A lexical database of American Sign Language

PubMed Central

Caselli, Naomi K.; Sehyr, Zed Sevcikova; Cohen-Goldberg, Ariel M.; Emmorey, Karen

2016-01-01

ASL-LEX is a lexical database that catalogues information about nearly 1,000 signs in American Sign Language (ASL). It includes the following information: subjective frequency ratings from 25–31 deaf signers, iconicity ratings from 21–37 hearing non-signers, videoclip duration, sign length (onset and offset), grammatical class, and whether the sign is initialized, a fingerspelled loan sign or a compound. Information about English translations is available for a subset of signs (e.g., alternate translations, translation consistency). In addition, phonological properties (sign type, selected fingers, flexion, major and minor location, and movement) were coded and used to generate sub-lexical frequency and neighborhood density estimates. ASL-LEX is intended for use by researchers, educators, and students who are interested in the properties of the ASL lexicon. An interactive website where the database can be browsed and downloaded is available at http://asl-lex.org. PMID:27193158

A general system for automatic biomedical image segmentation using intensity neighborhoods.

PubMed

Chen, Cheng; Ozolek, John A; Wang, Wei; Rohde, Gustavo K

2011-01-01

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

Monocyte Subset Dynamics in Human Atherosclerosis Can Be Profiled with Magnetic Nano-Sensors

PubMed Central

Wildgruber, Moritz; Lee, Hakho; Chudnovskiy, Aleksey; Yoon, Tae-Jong; Etzrodt, Martin; Pittet, Mikael J.; Nahrendorf, Matthias; Croce, Kevin; Libby, Peter; Weissleder, Ralph; Swirski, Filip K.

2009-01-01

Monocytes are circulating macrophage and dendritic cell precursors that populate healthy and diseased tissue. In humans, monocytes consist of at least two subsets whose proportions in the blood fluctuate in response to coronary artery disease, sepsis, and viral infection. Animal studies have shown that specific shifts in the monocyte subset repertoire either exacerbate or attenuate disease, suggesting a role for monocyte subsets as biomarkers and therapeutic targets. Assays are therefore needed that can selectively and rapidly enumerate monocytes and their subsets. This study shows that two major human monocyte subsets express similar levels of the receptor for macrophage colony stimulating factor (MCSFR) but differ in their phagocytic capacity. We exploit these properties and custom-engineer magnetic nanoparticles for ex vivo sensing of monocytes and their subsets. We present a two-dimensional enumerative mathematical model that simultaneously reports number and proportion of monocyte subsets in a small volume of human blood. Using a recently described diagnostic magnetic resonance (DMR) chip with 1 µl sample size and high throughput capabilities, we then show that application of the model accurately quantifies subset fluctuations that occur in patients with atherosclerosis. PMID:19461894

Innate lymphoid cells: a paradigm for low SSI in cleft lip repair.

PubMed

Simmerman, Erika; Qin, Xu; Marshall, Brendan; Perry, Libby; Cai, Lei; Wang, Tailing; Yu, Jack; Akbari, Omid; Baban, Babak

2016-10-01

Cleft lip and palate reconstructions demonstrate significantly lower surgical site infection rates compared with clean-contaminated cases, prompting investigation into the pathophysiology causing this discrepancy. Recent studies have identified a new group of innate lymphocytes called innate lymphoid cells (ILCs), located in barrier surfaces of the skin, airways, and intestine. Our objectives were to explore for the first time the presence of ILCs in the vermillion of neonates and young children undergoing cleft lip reconstruction and characterize their composition by measuring the three classes of ILCs. Lip tissue samples were collected from 13 subjects undergoing vermillion resection during cleft lip reconstructive surgery. Preparative, transmission electron microscopy, and analytical flow cytometry were performed. The functionality of ILCs was tested in terms of their capacity to produce type 1 (IFN-γ/TNF-α), type 2 (IL-5/IL-13), and type 3 (IL-17/IL-22) cytokines. Data were analyzed using Student t test or the analysis of variance to establish significance (P < 0.05) among groups for all other data. All three classes of ILCs were detected and visualized in the tissue samples. In all samples, the level of ILC2 subset was significantly higher than the other two ILC subsets (P < 0.01), followed by the ILC1 subset, which was present in significantly higher levels than the ILC3 subset (P < 0.05). Our data place ILCs for the first time in the interface of oral mucosal immunity, tissue microenvironment, and homeostasis during and after tissue development, possibly explaining lower infection rates in cleft lip or palate reconstructions. Copyright © 2016 Elsevier Inc. All rights reserved.

Phenotypic and Functional Heterogeneity of Bovine Blood Monocytes

PubMed Central

Hussen, Jamal; Düvel, Anna; Sandra, Olivier; Smith, David; Sheldon, Iain Martin; Zieger, Peter; Schuberth, Hans-Joachim

2013-01-01

Murine and human peripheral blood monocytes are heterogeneous in size, granularity, nuclear morphology, phenotype and function. Whether and how bovine blood monocytes follow this pattern was analyzed in this study. Flow cytometrically, classical monocytes (cM) CD14+ CD16−, intermediate monocytes (intM) CD14+ CD16+ and nonclassical monocytes (ncM) CD14+ CD16+ were identified, with cM being the predominant subset (89%). cM showed a significant lower expression of CD172a, intM expressed the highest level of MHC class II molecules, and ncM were low positive for CD163. Compared to cM and intM, ncM showed a significantly reduced phagocytosis capacity, a significantly reduced generation of reactive oxygen species, and reduced mRNA expression of CXCL8, CXCL1 and IL-1β after LPS stimulation. Based on IL-1β secretion after LPS/ATP stimulation, the inflammasome could be activated in cM and intM, but not in ncM. IFNγ increased the expression of CD16 selectively on cM and induced a shift from cM into intM in vitro. In summary, bovine CD172a-positive mononuclear cells define three monocyte subsets with distinct phenotypic and functional differences. Bovine cM and intM share homologies with their human counterparts, whereas bovine ncM are not inflammatory monocytes. PMID:23967219

IgG1 memory B cells keep the memory of IgE responses.

PubMed

He, Jin-Shu; Subramaniam, Sharrada; Narang, Vipin; Srinivasan, Kandhadayar; Saunders, Sean P; Carbajo, Daniel; Wen-Shan, Tsao; Hidayah Hamadee, Nur; Lum, Josephine; Lee, Andrea; Chen, Jinmiao; Poidinger, Michael; Zolezzi, Francesca; Lafaille, Juan J; Curotto de Lafaille, Maria A

2017-09-21

The unique differentiation of IgE cells suggests unconventional mechanisms of IgE memory. IgE germinal centre cells are transient, most IgE cells are plasma cells, and high affinity IgE is produced by the switching of IgG1 cells to IgE. Here we investigate the function of subsets of IgG1 memory B cells in IgE production and find that two subsets of IgG1 memory B cells, CD80 + CD73 + and CD80 - CD73 - , contribute distinctively to the repertoires of high affinity pathogenic IgE and low affinity non-pathogenic IgE. Furthermore, repertoire analysis indicates that high affinity IgE and IgG1 plasma cells differentiate from rare CD80 + CD73 + high affinity memory clones without undergoing further mutagenesis. By identifying the cellular origin of high affinity IgE and the clonal selection of high affinity memory B cells into the plasma cell fate, our findings provide fundamental insights into the pathogenesis of allergies, and on the mechanisms of antibody production in memory B cell responses.IgE is an important mediator of protective immunity as well as allergic reaction, but how high affinity IgE antibodies are produced in memory responses is not clear. Here the authors show that IgE can be generated via class-switch recombination in IgG1 memory B cells without additional somatic hypermutation.

Collaborative-group testing improves learning and knowledge retention of human physiology topics in second-year medical students.

PubMed

Vázquez-García, Mario

2018-06-01

The present study examined the relationship between second-year medical students' group performance and individual performance in a collaborative-learning environment. In recent decades, university professors in the scientific and humanistic disciplines have successfully put into practice different modalities of collaborative approaches to teaching. Essentially, collaborative approach refers to a variety of techniques that involves the joint intellectual effort of a small group of students, which encourages interaction and discussion among students and professors. The present results show the efficacy of collaborative learning, which, furthermore, allowed students to participate actively in the physiology class. Average student's grades were significantly higher when they engaged in single-best-response, multiple-choice tests as a student team, compared with taking the same examinations individually. The method improved notably knowledge retention, as learning is more effective when performed in the context of collaborative partnership. A selected subset of questions answered wrongly in an initial test, both individually and collectively, was used on a second test to examine student retention of studied material. Grade averages were significantly improved, both individually and groupwise, when students responded to the subset of questions a second time, 1, 2, or 3 wk after the first attempt. These results suggest that the collaborative approach to teaching allowed a more effective understanding of course content, which meant an improved capacity for retention of human physiology knowledge.

IL-12-dependent inducible expression of the CD94/NKG2A inhibitory receptor regulates CD94/NKG2C+ NK cell function.

PubMed

Sáez-Borderías, Andrea; Romo, Neus; Magri, Giuliana; Gumá, Mónica; Angulo, Ana; López-Botet, Miguel

2009-01-15

The inhibitory CD94/NKG2A and activating CD94/NKG2C killer lectin-like receptors specific for HLA-E have been reported to be selectively expressed by discrete NK and T cell subsets. In the present study, minor proportions of NK and T cells coexpressing both CD94/NKG2A and CD94/NKG2C were found in fresh peripheral blood from adult blood donors. Moreover, CD94/NKG2A surface expression was transiently detected upon in vitro stimulation of CD94/NKG2C+ NK cells in the presence of irradiated allogeneic PBMC or rIL-12. A similar effect was observed upon coculture of NKG2C+ NK clones with human CMV-infected autologous dendritic cell cultures, and it was prevented by an anti-IL-12 mAb. NKG2A inhibited the cytolytic activity of NKG2C+ NK clones upon engagement either by a specific mAb or upon interaction with a transfectant of the HLA class I-deficient 721.221 cell line expressing HLA-E. These data indicate that beyond its constitutive expression by an NK cell subset, NKG2A may be also transiently displayed by CD94/NKG2C+ NK cells under the influence of IL-12, providing a potential negative regulatory feedback mechanism.

Selecting informative subsets of sparse supermatrices increases the chance to find correct trees.

PubMed

Misof, Bernhard; Meyer, Benjamin; von Reumont, Björn Marcus; Kück, Patrick; Misof, Katharina; Meusemann, Karen

2013-12-03

Character matrices with extensive missing data are frequently used in phylogenomics with potentially detrimental effects on the accuracy and robustness of tree inference. Therefore, many investigators select taxa and genes with high data coverage. Drawbacks of these selections are their exclusive reliance on data coverage without consideration of actual signal in the data which might, thus, not deliver optimal data matrices in terms of potential phylogenetic signal. In order to circumvent this problem, we have developed a heuristics implemented in a software called mare which (1) assesses information content of genes in supermatrices using a measure of potential signal combined with data coverage and (2) reduces supermatrices with a simple hill climbing procedure to submatrices with high total information content. We conducted simulation studies using matrices of 50 taxa × 50 genes with heterogeneous phylogenetic signal among genes and data coverage between 10-30%. With matrices of 50 taxa × 50 genes with heterogeneous phylogenetic signal among genes and data coverage between 10-30% Maximum Likelihood (ML) tree reconstructions failed to recover correct trees. A selection of a data subset with the herein proposed approach increased the chance to recover correct partial trees more than 10-fold. The selection of data subsets with the herein proposed simple hill climbing procedure performed well either considering the information content or just a simple presence/absence information of genes. We also applied our approach on an empirical data set, addressing questions of vertebrate systematics. With this empirical dataset selecting a data subset with high information content and supporting a tree with high average boostrap support was most successful if information content of genes was considered. Our analyses of simulated and empirical data demonstrate that sparse supermatrices can be reduced on a formal basis outperforming the usually used simple selections of taxa and genes with high data coverage.

Mapping invasive species and spectral mixture relationships with neotropical woody formations in southeastern Brazil

NASA Astrophysics Data System (ADS)

Amaral, Cibele H.; Roberts, Dar A.; Almeida, Teodoro I. R.; Souza Filho, Carlos R.

2015-10-01

Biological invasion substantially contributes to the increasing extinction rates of native vegetative species. The remote detection and mapping of invasive species is critical for environmental monitoring. This study aims to assess the performance of a Multiple Endmember Spectral Mixture Analysis (MESMA) applied to imaging spectroscopy data for mapping Dendrocalamus sp. (bamboo) and Pinus elliottii L. (slash pine), which are invasive plant species, in a Brazilian neotropical landscape within the tropical Brazilian savanna biome. The work also investigates the spectral mixture between these exotic species and the native woody formations, including woodland savanna, submontane and alluvial seasonal semideciduous forests (SSF). Visible to Shortwave Infrared (VSWIR) imaging spectroscopy data at one-meter spatial resolution were atmospherically corrected and subset into the different spectral ranges (VIS-NIR1: 530-919 nm; and NIR2-SWIR: 1141-2352 nm). The data were further normalized via continuum removal (CR). Multiple endmember selection methods, including Interactive Endmember Selection (IES), Endmember average root mean square error (EAR), Minimum average spectral angle (MASA) and Count-based (CoB) (collectively called EMC), were employed to create endmember libraries for the targeted vegetation classes. The performance of the MESMA was assessed at the pixel and crown scales. Statistically significant differences (α = 0.05) were observed between overall accuracies that were obtained at various spectral ranges. The infrared region (IR) was critical for detecting the vegetation classes using spectral data. The invasive species endmembers exhibited spectral patterns in the IR that were not observed in the native formations. Bamboo was characterized as having a high green vegetation (GV) fraction, lower non-photosynthetic vegetation (NPV) and a low shade fraction, while pine exhibited higher NPV and shade fractions. The invasive species showed a statistically significant larger number of spectra erroneously assigned to the woodland savanna class versus the alluvial and submontane SSF classes. Consequently, the invasive species tended to be overestimated, especially in the woodland savanna. Bamboo was best classified using the VSWIR(CR) data with the EMC endmember selection method (User's accuracy and Producer's accuracy = 98.11% and 72.22%, respectively). Pine was best classified using NIR2-SWIR(CR) data with the IES selected endmembers (97.06% and 62.26%, respectively). The results obtained during the two-endmember modeling were fully translated into the three-endmember unmixed images. The sub-pixel invasive species abundance analysis showed that MESMA performs well when unmixing at the pixel scale and for mapping invasive species fractions in a complex neotropical environment, at pixel and crown scales with 1-m spatial resolution data.

Renorming c0 and closed, bounded, convex sets with fixed point property for affine nonexpansive mappings

NASA Astrophysics Data System (ADS)

Nezir, Veysel; Mustafa, Nizami

2017-04-01

In 2008, P.K. Lin provided the first example of a nonreflexive space that can be renormed to have fixed point property for nonexpansive mappings. This space was the Banach space of absolutely summable sequences l1 and researchers aim to generalize this to c0, Banach space of null sequences. Before P.K. Lin's intriguing result, in 1979, Goebel and Kuczumow showed that there is a large class of non-weak* compact closed, bounded, convex subsets of l1 with fixed point property for nonexpansive mappings. Then, P.K. Lin inspired by Goebel and Kuczumow's ideas to give his result. Similarly to P.K. Lin's study, Hernández-Linares worked on L1 and in his Ph.D. thesis, supervisored under Maria Japón, showed that L1 can be renormed to have fixed point property for affine nonexpansive mappings. Then, related questions for c0 have been considered by researchers. Recently, Nezir constructed several equivalent norms on c0 and showed that there are non-weakly compact closed, bounded, convex subsets of c0 with fixed point property for affine nonexpansive mappings. In this study, we construct a family of equivalent norms containing those developed by Nezir as well and show that there exists a large class of non-weakly compact closed, bounded, convex subsets of c0 with fixed point property for affine nonexpansive mappings.

High expression of AID and active class switch recombination might account for a more aggressive disease in unmutated CLL patients: link with an activated microenvironment in CLL disease.

PubMed

Palacios, Florencia; Moreno, Pilar; Morande, Pablo; Abreu, Cecilia; Correa, Agustín; Porro, Valentina; Landoni, Ana Ines; Gabus, Raul; Giordano, Mirta; Dighiero, Guillermo; Pritsch, Otto; Oppezzo, Pablo

2010-06-03

Interaction of chronic lymphocytic leukemia (CLL) B cells with tissue microenvironment has been suggested to favor disease progression by promoting malignant B-cell growth. Previous work has shown expression in peripheral blood (PB) of CLL B cells of activation-induced cytidine deaminase (AID) among CLL patients with an unmutated (UM) profile of immunoglobulin genes and with ongoing class switch recombination (CSR) process. Because AID expression results from interaction with activated tissue microenvironment, we speculated whether the small subset with ongoing CSR is responsible for high levels of AID expression and could be derived from this particular microenvironment. In this work, we quantified AID expression and ongoing CSR in PB of 50 CLL patients and characterized the expression of different molecules related to microenvironment interaction. Our results show that among UM patients (1) high AID expression is restricted to the subpopulation of tumoral cells ongoing CSR; (2) this small subset expresses high levels of proliferation, antiapoptotic and progression markers (Ki-67, c-myc, Bcl-2, CD49d, and CCL3/4 chemokines). Overall, this work outlines the importance of a cellular subset in PB of UM CLL patients with a poor clinical outcome, high AID levels, and ongoing CSR, whose presence might be a hallmark of a recent contact with the microenvironment.

Generating a Simulated Fluid Flow over a Surface Using Anisotropic Diffusion

NASA Technical Reports Server (NTRS)

Rodriguez, David L. (Inventor); Sturdza, Peter (Inventor)

2016-01-01

A fluid-flow simulation over a computer-generated surface is generated using a diffusion technique. The surface is comprised of a surface mesh of polygons. A boundary-layer fluid property is obtained for a subset of the polygons of the surface mesh. A gradient vector is determined for a selected polygon, the selected polygon belonging to the surface mesh but not one of the subset of polygons. A maximum and minimum diffusion rate is determined along directions determined using the gradient vector corresponding to the selected polygon. A diffusion-path vector is defined between a point in the selected polygon and a neighboring point in a neighboring polygon. An updated fluid property is determined for the selected polygon using a variable diffusion rate, the variable diffusion rate based on the minimum diffusion rate, maximum diffusion rate, and the gradient vector.

An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population.

PubMed

Niño-Sandoval, Tania Camila; Guevara Perez, Sonia V; González, Fabio A; Jaque, Robinson Andrés; Infante-Contreras, Clementina

2016-04-01

The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the mandibular position only through craniomaxillary measures, for this task, different modeling techniques have been performed, but they only contemplate a straight facial profile that belong to skeletal pattern Class I, but the 24.5% corresponding to the Colombian skeletal patterns Class II and III are not taking into account, besides, craniofacial measures do not follow a parametric trend or a normal distribution. The aim of this study was to employ an automatic non-parametric method as the Support Vector Machines to classify skeletal patterns through craniomaxillary variables, in order to simulate the natural mandibular position on a contemporary Colombian sample. Lateral cephalograms (229) of Colombian young adults of both sexes were collected. Landmark coordinates protocols were used to create craniomaxillary variables. A Support Vector Machine with a linear kernel classifier model was trained on a subset of the available data and evaluated over the remaining samples. The weights of the model were used to select the 10 best variables for classification accuracy. An accuracy of 74.51% was obtained, defined by Pr-A-N, N-Pr-A, A-N-Pr, A-Te-Pr, A-Pr-Rhi, Rhi-A-Pr, Pr-A-Te, Te-Pr-A, Zm-A-Pr and PNS-A-Pr angles. The Class Precision and the Class Recall showed a correct distinction of the Class II from the Class III and vice versa. Support Vector Machines created an important model of classification of skeletal patterns using craniomaxillary variables that are not commonly used in the literature and could be applicable to the 24.5% of the contemporary Colombian sample. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Use of genetic algorithm for the selection of EEG features

NASA Astrophysics Data System (ADS)

Asvestas, P.; Korda, A.; Kostopoulos, S.; Karanasiou, I.; Ouzounoglou, A.; Sidiropoulos, K.; Ventouras, E.; Matsopoulos, G.

2015-09-01

Genetic Algorithm (GA) is a popular optimization technique that can detect the global optimum of a multivariable function containing several local optima. GA has been widely used in the field of biomedical informatics, especially in the context of designing decision support systems that classify biomedical signals or images into classes of interest. The aim of this paper is to present a methodology, based on GA, for the selection of the optimal subset of features that can be used for the efficient classification of Event Related Potentials (ERPs), which are recorded during the observation of correct or incorrect actions. In our experiment, ERP recordings were acquired from sixteen (16) healthy volunteers who observed correct or incorrect actions of other subjects. The brain electrical activity was recorded at 47 locations on the scalp. The GA was formulated as a combinatorial optimizer for the selection of the combination of electrodes that maximizes the performance of the Fuzzy C Means (FCM) classification algorithm. In particular, during the evolution of the GA, for each candidate combination of electrodes, the well-known (Σ, Φ, Ω) features were calculated and were evaluated by means of the FCM method. The proposed methodology provided a combination of 8 electrodes, with classification accuracy 93.8%. Thus, GA can be the basis for the selection of features that discriminate ERP recordings of observations of correct or incorrect actions.

On Subset Selection Procedures for Poisson Processes and Some Applications to the Binomial and Multinomial Problems

DTIC Science & Technology

1976-07-01

PURDUE UNIVERSITY DEPARTMENT OF STATISTICS DIVISION OF MATHEMATICAL SCIENCES ON SUBSET SELECTION PROCEDURES FOR POISSON PROCESSES AND SOME...Mathematical Sciences Mimeograph Series #457, July 1976 This research was supported by the Office of Naval Research under Contract NOOO14-75-C-0455 at Purdue...11 CON PC-111 riFIC-F ,A.F ANO ADDPFS Office of INaval ResearchJu#07 Washington, DC07 36AE 14~~~ rjCr; NF A ’ , A FAA D F 6 - I S it 9 i 1, - ,1 I

Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition.

PubMed

Zhang, Zhao; Zhao, Mingbo; Chow, Tommy W S

2012-12-01

In this work, sub-manifold projections based semi-supervised dimensionality reduction (DR) problem learning from partial constrained data is discussed. Two semi-supervised DR algorithms termed Marginal Semi-Supervised Sub-Manifold Projections (MS³MP) and orthogonal MS³MP (OMS³MP) are proposed. MS³MP in the singular case is also discussed. We also present the weighted least squares view of MS³MP. Based on specifying the types of neighborhoods with pairwise constraints (PC) and the defined manifold scatters, our methods can preserve the local properties of all points and discriminant structures embedded in the localized PC. The sub-manifolds of different classes can also be separated. In PC guided methods, exploring and selecting the informative constraints is challenging and random constraint subsets significantly affect the performance of algorithms. This paper also introduces an effective technique to select the informative constraints for DR with consistent constraints. The analytic form of the projection axes can be obtained by eigen-decomposition. The connections between this work and other related work are also elaborated. The validity of the proposed constraint selection approach and DR algorithms are evaluated by benchmark problems. Extensive simulations show that our algorithms can deliver promising results over some widely used state-of-the-art semi-supervised DR techniques. Copyright © 2012 Elsevier Ltd. All rights reserved.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

On F-Algebras M p  (1 < p < ∞) of Holomorphic Functions

PubMed Central

Meštrović, Romeo

2014-01-01

We consider the classes M p (1 < p < ∞) of holomorphic functions on the open unit disk 𝔻 in the complex plane. These classes are in fact generalizations of the class M introduced by Kim (1986). The space M p equipped with the topology given by the metric ρ p defined by ρ p(f, g) = ||f − g||p = (∫ 0 2πlogp(1 + M(f − g)(θ))(dθ/2π))1/p, with f, g∈M p and Mf(θ) = sup0⩽r<1 ⁡|f(re iθ)|, becomes an F-space. By a result of Stoll (1977), the Privalov space N p (1 < p < ∞) with the topology given by the Stoll metric d p is an F-algebra. By using these two facts, we prove that the spaces M p and N p coincide and have the same topological structure. Consequently, we describe a general form of continuous linear functionals on M p (with respect to the metric ρ p). Furthermore, we give a characterization of bounded subsets of the spaces M p. Moreover, we give the examples of bounded subsets of M p that are not relatively compact. PMID:24672388

Profiling dendritic cell subsets in head and neck squamous cell tonsillar cancer and benign tonsils.

PubMed

Abolhalaj, Milad; Askmyr, David; Sakellariou, Christina Alexandra; Lundberg, Kristina; Greiff, Lennart; Lindstedt, Malin

2018-05-23

Dendritic cells (DCs) have a key role in orchestrating immune responses and are considered important targets for immunotherapy against cancer. In order to develop effective cancer vaccines, detailed knowledge of the micromilieu in cancer lesions is warranted. In this study, flow cytometry and human transcriptome arrays were used to characterize subsets of DCs in head and neck squamous cell tonsillar cancer and compare them to their counterparts in benign tonsils to evaluate subset-selective biomarkers associated with tonsillar cancer. We describe, for the first time, four subsets of DCs in tonsillar cancer: CD123 + plasmacytoid DCs (pDC), CD1c + , CD141 + , and CD1c - CD141 - myeloid DCs (mDC). An increased frequency of DCs and an elevated mDC/pDC ratio were shown in malignant compared to benign tonsillar tissue. The microarray data demonstrates characteristics specific for tonsil cancer DC subsets, including expression of immunosuppressive molecules and lower expression levels of genes involved in development of effector immune responses in DCs in malignant tonsillar tissue, compared to their counterparts in benign tonsillar tissue. Finally, we present target candidates selectively expressed by different DC subsets in malignant tonsils and confirm expression of CD206/MRC1 and CD207/Langerin on CD1c + DCs at protein level. This study descibes DC characteristics in the context of head and neck cancer and add valuable steps towards future DC-based therapies against tonsillar cancer.

Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

EPA Science Inventory

Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast™ chemical screening and prioritization research project. Metabolites from hES cultur...

Tissue distribution, regulation and intracellular localization of murine CD1 molecules.

PubMed

Mandal, M; Chen, X R; Alegre, M L; Chiu, N M; Chen, Y H; Castaño, A R; Wang, C R

1998-06-01

CD1 molecules are MHC-unlinked class Ib molecules consisting of classical (human CD 1a-c) and non-classical subsets (human CD1d and murine CD1). The characterization of non-classical subsets of CD1 is limited due to the lack of reagents. In this study, we have generated two new anti-mouse CD1 monoclonal antibodies, 3H3 and 5C6, by immunization of hamsters with purified CD1 protein. These antibodies recognize CD1-transfected cells and have no reactivity to cells isolated from CD1-/- mice. Both antibodies precipitate the 52 kDa heavy chain and 12 kDa beta2m from thymocytes and splenocytes by radio-immunoprecipitation. Deglycosylation of CD1 reduces molecular mass of the heavy chain by 7.5 kDa, which can be detected by 3H3 but not 5C6. 3H3 and 5C6 detect surface CD1 expression on cells from the thymus, spleen, lymph node and bone marrow, but not on intestinal epithelial cells. Developmentally, CD1 is expressed on thymocytes prior to TCR rearrangement and remains constant throughout thymic development. CD1 is expressed early in the fetal liver (day 14) and remains expressed in hepatocytes postnatally. These data support evidence of a role for CD1 in the selection and/or expansion of NK1- T cells of both thymic origin and extrathymic origin. Unlike classical class I molecules, murine CD1 levels are not affected by IFN-gamma, but like human CD1b can be up-regulated by IL-4 and GM-CSF although only moderately. Similar to human CD1b, murine CD1 is found by immunofluorescence microscopy on the cell surface, and in various intracellular vesicles, including early and late endosomes. Localization in endocytic compartments indicates that murine CD1 may be capable of binding endocytosed antigens.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets

PubMed Central

Gratzl, Samuel; Gehlenborg, Nils; Lex, Alexander; Pfister, Hanspeter; Streit, Marc

2016-01-01

Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics. PMID:26356916

Intraclonal Cell Expansion and Selection Driven by B Cell Receptor in Chronic Lymphocytic Leukemia

PubMed Central

Colombo, Monica; Cutrona, Giovanna; Reverberi, Daniele; Fabris, Sonia; Neri, Antonino; Fabbi, Marina; Quintana, Giovanni; Quarta, Giovanni; Ghiotto, Fabio; Fais, Franco; Ferrarini, Manlio

2011-01-01

The mutational status of the immunoglobulin heavy-chain variable region (IGHV) genes utilized by chronic lymphocytic leukemia (CLL) clones defines two disease subgroups. Patients with unmutated IGHV have a more aggressive disease and a worse outcome than patients with cells having somatic IGHV gene mutations. Moreover, up to 30% of the unmutated CLL clones exhibit very similar or identical B cell receptors (BcR), often encoded by the same IG genes. These “stereotyped” BcRs have been classified into defined subsets. The presence of an IGHV gene somatic mutation and the utilization of a skewed gene repertoire compared with normal B cells together with the expression of stereotyped receptors by unmutated CLL clones may indicate stimulation/selection by antigenic epitopes. This antigenic stimulation may occur prior to or during neoplastic transformation, but it is unknown whether this stimulation/selection continues after leukemogenesis has ceased. In this study, we focused on seven CLL cases with stereotyped BcR Subset #8 found among a cohort of 700 patients; in six, the cells expressed IgG and utilized IGHV4-39 and IGKV1-39/IGKV1D-39 genes, as reported for Subset #8 BcR. One case exhibited special features, including expression of IgM or IgG by different subclones consequent to an isotype switch, allelic inclusion at the IGH locus in the IgM-expressing cells and a particular pattern of cytogenetic lesions. Collectively, the data indicate a process of antigenic stimulation/selection of the fully transformed CLL cells leading to the expansion of the Subset #8 IgG-bearing subclone. PMID:21541442

Generating a Simulated Fluid Flow Over an Aircraft Surface Using Anisotropic Diffusion

NASA Technical Reports Server (NTRS)

Rodriguez, David L. (Inventor); Sturdza, Peter (Inventor)

2013-01-01

A fluid-flow simulation over a computer-generated aircraft surface is generated using a diffusion technique. The surface is comprised of a surface mesh of polygons. A boundary-layer fluid property is obtained for a subset of the polygons of the surface mesh. A pressure-gradient vector is determined for a selected polygon, the selected polygon belonging to the surface mesh but not one of the subset of polygons. A maximum and minimum diffusion rate is determined along directions determined using a pressure gradient vector corresponding to the selected polygon. A diffusion-path vector is defined between a point in the selected polygon and a neighboring point in a neighboring polygon. An updated fluid property is determined for the selected polygon using a variable diffusion rate, the variable diffusion rate based on the minimum diffusion rate, maximum diffusion rate, and angular difference between the diffusion-path vector and the pressure-gradient vector.

System and method for progressive band selection for hyperspectral images

NASA Technical Reports Server (NTRS)

Fisher, Kevin (Inventor)

2013-01-01

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for progressive band selection for hyperspectral images. A system having module configured to control a processor to practice the method calculates a virtual dimensionality of a hyperspectral image having multiple bands to determine a quantity Q of how many bands are needed for a threshold level of information, ranks each band based on a statistical measure, selects Q bands from the multiple bands to generate a subset of bands based on the virtual dimensionality, and generates a reduced image based on the subset of bands. This approach can create reduced datasets of full hyperspectral images tailored for individual applications. The system uses a metric specific to a target application to rank the image bands, and then selects the most useful bands. The number of bands selected can be specified manually or calculated from the hyperspectral image's virtual dimensionality.

Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

NASA Astrophysics Data System (ADS)

Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

High Aldehyde Dehydrogenase Activity Identifies a Subset of Human Mesenchymal Stromal Cells with Vascular Regenerative Potential.

PubMed

Sherman, Stephen E; Kuljanin, Miljan; Cooper, Tyler T; Putman, David M; Lajoie, Gilles A; Hess, David A

2017-06-01

During culture expansion, multipotent mesenchymal stromal cells (MSCs) differentially express aldehyde dehydrogenase (ALDH), an intracellular detoxification enzyme that protects long-lived cells against oxidative stress. Thus, MSC selection based on ALDH-activity may be used to reduce heterogeneity and distinguish MSC subsets with improved regenerative potency. After expansion of human bone marrow-derived MSCs, cell progeny was purified based on low versus high ALDH-activity (ALDH hi ) by fluorescence-activated cell sorting, and each subset was compared for multipotent stromal and provascular regenerative functions. Both ALDH l ° and ALDH hi MSC subsets demonstrated similar expression of stromal cell (>95% CD73 + , CD90 + , CD105 + ) and pericyte (>95% CD146 + ) surface markers and showed multipotent differentiation into bone, cartilage, and adipose cells in vitro. Conditioned media (CDM) generated by ALDH hi MSCs demonstrated a potent proliferative and prosurvival effect on human microvascular endothelial cells (HMVECs) under serum-free conditions and augmented HMVEC tube-forming capacity in growth factor-reduced matrices. After subcutaneous transplantation within directed in vivo angiogenesis assay implants into immunodeficient mice, ALDH hi MSC or CDM produced by ALDH hi MSC significantly augmented murine vascular cell recruitment and perfused vessel infiltration compared with ALDH l ° MSC. Although both subsets demonstrated strikingly similar mRNA expression patterns, quantitative proteomic analyses performed on subset-specific CDM revealed the ALDH hi MSC subset uniquely secreted multiple proangiogenic cytokines (vascular endothelial growth factor beta, platelet derived growth factor alpha, and angiogenin) and actively produced multiple factors with chemoattractant (transforming growth factor-β, C-X-C motif chemokine ligand 1, 2, and 3 (GRO), C-C motif chemokine ligand 5 (RANTES), monocyte chemotactic protein 1 (MCP-1), interleukin [IL]-6, IL-8) and matrix-modifying functions (tissue inhibitor of metalloprotinase 1 & 2 (TIMP1/2)). Collectively, MSCs selected for ALDH hi demonstrated enhanced proangiogenic secretory functions and represent a purified MSC subset amenable for vascular regenerative applications. Stem Cells 2017;35:1542-1553. © 2017 AlphaMed Press.

Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2015-12-01

The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

Comparative study of feature selection with ensemble learning using SOM variants

NASA Astrophysics Data System (ADS)

Filali, Ameni; Jlassi, Chiraz; Arous, Najet

2017-03-01

Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for every cluster from a dataset. The proposed method is another extension of the Random Forests approach using self-organizing maps (SOM) variants to unlabeled data that estimates the out-of-bag feature importance from a set of partitions. Every partition is created using a various bootstrap sample and a random subset of the features. Then, we show that the process internal estimates are used to measure variable pertinence in Random Forests are also applicable to feature selection in unsupervised learning. This approach aims to the dimensionality reduction, visualization and cluster characterization at the same time. Hence, we provide empirical results on nineteen benchmark data sets indicating that RFS can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art unsupervised methods, with a very limited subset of features. The approach proves promise to treat with very broad domains.

The Subset Sum game.

PubMed

Darmann, Andreas; Nicosia, Gaia; Pferschy, Ulrich; Schauer, Joachim

2014-03-16

In this work we address a game theoretic variant of the Subset Sum problem, in which two decision makers (agents/players) compete for the usage of a common resource represented by a knapsack capacity. Each agent owns a set of integer weighted items and wants to maximize the total weight of its own items included in the knapsack. The solution is built as follows: Each agent, in turn, selects one of its items (not previously selected) and includes it in the knapsack if there is enough capacity. The process ends when the remaining capacity is too small for including any item left. We look at the problem from a single agent point of view and show that finding an optimal sequence of items to select is an [Formula: see text]-hard problem. Therefore we propose two natural heuristic strategies and analyze their worst-case performance when (1) the opponent is able to play optimally and (2) the opponent adopts a greedy strategy. From a centralized perspective we observe that some known results on the approximation of the classical Subset Sum can be effectively adapted to the multi-agent version of the problem.

The Subset Sum game☆

PubMed Central

Darmann, Andreas; Nicosia, Gaia; Pferschy, Ulrich; Schauer, Joachim

2014-01-01

In this work we address a game theoretic variant of the Subset Sum problem, in which two decision makers (agents/players) compete for the usage of a common resource represented by a knapsack capacity. Each agent owns a set of integer weighted items and wants to maximize the total weight of its own items included in the knapsack. The solution is built as follows: Each agent, in turn, selects one of its items (not previously selected) and includes it in the knapsack if there is enough capacity. The process ends when the remaining capacity is too small for including any item left. We look at the problem from a single agent point of view and show that finding an optimal sequence of items to select is an NP-hard problem. Therefore we propose two natural heuristic strategies and analyze their worst-case performance when (1) the opponent is able to play optimally and (2) the opponent adopts a greedy strategy. From a centralized perspective we observe that some known results on the approximation of the classical Subset Sum can be effectively adapted to the multi-agent version of the problem. PMID:25844012

Visualization of self-delivering hydrophobically modified siRNA cellular internalization

PubMed Central

Ly, Socheata; Navaroli, Deanna M.; Didiot, Marie-Cécile; Cardia, James; Pandarinathan, Lakshmipathi; Alterman, Julia F.; Fogarty, Kevin; Standley, Clive; Lifshitz, Lawrence M.; Bellve, Karl D.; Prot, Matthieu; Echeverria, Dimas; Corvera, Silvia; Khvorova, Anastasia

2017-01-01

siRNAs are a new class of therapeutic modalities with promising clinical efficacy that requires modification or formulation for delivery to the tissue and cell of interest. Conjugation of siRNAs to lipophilic groups supports efficient cellular uptake by a mechanism that is not well characterized. Here we study the mechanism of internalization of asymmetric, chemically stabilized, cholesterol-modified siRNAs (sd-rxRNAs®) that efficiently enter cells and tissues without the need for formulation. We demonstrate that uptake is rapid with significant membrane association within minutes of exposure followed by the formation of vesicular structures and internalization. Furthermore, sd-rxRNAs are internalized by a specific class of early endosomes and show preferential association with epidermal growth factor (EGF) but not transferrin (Tf) trafficking pathways as shown by live cell TIRF and structured illumination microscopy (SIM). In fixed cells, we observe ∼25% of sd-rxRNA co-localizing with EGF and <5% with Tf, which is indicative of selective endosomal sorting. Likewise, preferential sd-rxRNA co-localization was demonstrated with EEA1 but not RBSN-containing endosomes, consistent with preferential EGF-like trafficking through EEA1-containing endosomes. sd-rxRNA cellular uptake is a two-step process, with rapid membrane association followed by internalization through a selective, saturable subset of the endocytic process. However, the mechanistic role of EEA1 is not yet known. This method of visualization can be used to better understand the kinetics and mechanisms of hydrophobic siRNA cellular uptake and will assist in further optimization of these types of compounds for therapeutic intervention. PMID:27899655

Enhancing Critical Infrastructure and Key Resources (CIKR) Level-0 Physical Process Security Using Field Device Distinct Native Attribute Features

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

Lopez, Juan; Liefer, Nathan C.; Busho, Colin R.

Here, the need for improved Critical Infrastructure and Key Resource (CIKR) security is unquestioned and there has been minimal emphasis on Level-0 (PHY Process) improvements. Wired Signal Distinct Native Attribute (WS-DNA) Fingerprinting is investigated here as a non-intrusive PHY-based security augmentation to support an envisioned layered security strategy. Results are based on experimental response collections from Highway Addressable Remote Transducer (HART) Differential Pressure Transmitter (DPT) devices from three manufacturers (Yokogawa, Honeywell, Endress+Hauer) installed in an automated process control system. Device discrimination is assessed using Time Domain (TD) and Slope-Based FSK (SB-FSK) fingerprints input to Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML)more » and Random Forest (RndF) classifiers. For 12 different classes (two devices per manufacturer at two distinct set points), both classifiers performed reliably and achieved an arbitrary performance benchmark of average cross-class percent correct of %C > 90%. The least challenging cross-manufacturer results included near-perfect %C ≈ 100%, while the more challenging like-model (serial number) discrimination results included 90%< %C < 100%, with TD Fingerprinting marginally outperforming SB-FSK Fingerprinting; SB-FSK benefits from having less stringent response alignment and registration requirements. The RndF classifier was most beneficial and enabled reliable selection of dimensionally reduced fingerprint subsets that minimize data storage and computational requirements. The RndF selected feature sets contained 15% of the full-dimensional feature sets and only suffered a worst case %CΔ = 3% to 4% performance degradation.« less

Contrasting evolutionary histories of MHC class I and class II loci in grouse—Effects of selection and gene conversion

USGS Publications Warehouse

Minias, Piotr; Bateson, Zachary W.; Whittingham, Linda A.; Johnson, Jeff A.; Oyler-McCance, Sara J.; Dunn, Peter O.

2016-01-01

Genes of the major histocompatibility complex (MHC) encode receptor molecules that are responsible for recognition of intracellular and extracellular pathogens (class I and class II genes, respectively) in vertebrates. Given the different roles of class I and II MHC genes, one might expect the strength of selection to differ between these two classes. Different selective pressures may also promote different rates of gene conversion at each class. Despite these predictions, surprisingly few studies have looked at differences between class I and II genes in terms of both selection and gene conversion. Here, we investigated the molecular evolution of MHC class I and II genes in five closely related species of prairie grouse (Centrocercus and Tympanuchus) that possess one class I and two class II loci. We found striking differences in the strength of balancing selection acting on MHC class I versus class II genes. More than half of the putative antigen-binding sites (ABS) of class II were under positive or episodic diversifying selection, compared with only 10% at class I. We also found that gene conversion had a stronger role in shaping the evolution of MHC class II than class I. Overall, the combination of strong positive (balancing) selection and frequent gene conversion has maintained higher diversity of MHC class II than class I in prairie grouse. This is one of the first studies clearly demonstrating that macroevolutionary mechanisms can act differently on genes involved in the immune response against intracellular and extracellular pathogens.

Contrasting evolutionary histories of MHC class I and class II loci in grouse—effects of selection and gene conversion

PubMed Central

Minias, P; Bateson, Z W; Whittingham, L A; Johnson, J A; Oyler-McCance, S; Dunn, P O

2016-01-01

Genes of the major histocompatibility complex (MHC) encode receptor molecules that are responsible for recognition of intracellular and extracellular pathogens (class I and class II genes, respectively) in vertebrates. Given the different roles of class I and II MHC genes, one might expect the strength of selection to differ between these two classes. Different selective pressures may also promote different rates of gene conversion at each class. Despite these predictions, surprisingly few studies have looked at differences between class I and II genes in terms of both selection and gene conversion. Here, we investigated the molecular evolution of MHC class I and II genes in five closely related species of prairie grouse (Centrocercus and Tympanuchus) that possess one class I and two class II loci. We found striking differences in the strength of balancing selection acting on MHC class I versus class II genes. More than half of the putative antigen-binding sites (ABS) of class II were under positive or episodic diversifying selection, compared with only 10% at class I. We also found that gene conversion had a stronger role in shaping the evolution of MHC class II than class I. Overall, the combination of strong positive (balancing) selection and frequent gene conversion has maintained higher diversity of MHC class II than class I in prairie grouse. This is one of the first studies clearly demonstrating that macroevolutionary mechanisms can act differently on genes involved in the immune response against intracellular and extracellular pathogens. PMID:26860199

Ambient Sound-Based Collaborative Localization of Indeterministic Devices

PubMed Central

Kamminga, Jacob; Le, Duc; Havinga, Paul

2016-01-01

Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5° and a standard deviation of 2.3°. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android. PMID:27649176

Prospective study to determine early hypertrophy of the contra-lateral liver lobe after unilobar, Yttrium-90, selective internal radiation therapy in patients with hepatocellular carcinoma.

PubMed

Teo, Jin Yao; Allen, John Carson; Ng, David Chee Eng; Abdul Latiff, Julianah Bee; Choo, Su Pin; Tai, David Wai-Meng; Low, Albert Su Chong; Cheah, Foong Koon; Chang, Jason Pik Eu; Kam, Juinn Huar; Lee, Victor T W; Chung, Alexander Yaw Fui; Chan, Chung Yip; Chow, Pierce Kah Hoe; Goh, Brian K P

2018-05-01

Liver resection is a major curative option in patients presenting with hepatocellular carcinoma. An inadequate functional liver remnant is a major limiting factor precluding liver resection. In recent years, hypertrophy of the functional liver remnant after selective internal radiation therapy hypertrophy has been observed, but the degree of hypertrophy in the early postselective internal radiation therapy period has not been well studied. We conducted a prospective study on patients undergoing unilobar, Yttrium-90 selective internal radiation therapy for hepatocellular carcinoma to evaluate early hypertrophy at 4-6 weeks and 8-12 weeks after selective internal radiation therapy. In the study, 24 eligible patients were recruited and had serial volumetric measurements performed. The median age was 66 years (38-75 years). All patients were either Child-Pugh Class A or B, and 6/24 patients had documented, clinically relevant portal hypertension; 15 of the 24 patients were hepatitis B positive. At 4-6 weeks, modest hypertrophy was seen (median 3%; range -12 to 42%) and this increased at 8-12 weeks (median 9%; range -12 to 179%). No preprocedural factors predictive of hypertrophy were identified. Hypertrophy of the functional liver remnant after selective internal radiation therapy with Yttrium-90 occurred in a subset of patients but was modest and unpredictable in the early stages. Selective internal radiation therapy cannot be recommended as a standard treatment modality to induce early hypertrophy for patients with hepatocellular carcinoma. (Surgery 2017;160:XXX-XXX.). Copyright © 2017 Elsevier Inc. All rights reserved.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

PubMed

Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

2017-11-27

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Delineation of soil temperature regimes from HCMM data

NASA Technical Reports Server (NTRS)

Day, R. L.; Petersen, G. W. (Principal Investigator)

1981-01-01

Supplementary data including photographs as well as topographic, geologic, and soil maps were obtained and evaluated for ground truth purposes and control point selection. A study area (approximately 450 by 450 pixels) was subset from LANDSAT scene No. 2477-17142. Geometric corrections and scaling were performed. Initial enhancement techniques were initiated to aid control point selection and soils interpretation. The SUBSET program was modified to read HCMM tapes and HCMM data were reformated so that they are compatible with the ORSER system. Initial NMAP products of geometrically corrected and scaled raw data tapes (unregistered) of the study were produced.

Decision Aids for Airborne Intercept Operations in Advanced Aircrafts

NASA Technical Reports Server (NTRS)

Madni, A.; Freedy, A.

1981-01-01

A tactical decision aid (TDA) for the F-14 aircrew, i.e., the naval flight officer and pilot, in conducting a multitarget attack during the performance of a Combat Air Patrol (CAP) role is presented. The TDA employs hierarchical multiattribute utility models for characterizing mission objectives in operationally measurable terms, rule based AI-models for tactical posture selection, and fast time simulation for maneuver consequence prediction. The TDA makes aspect maneuver recommendations, selects and displays the optimum mission posture, evaluates attackable and potentially attackable subsets, and recommends the 'best' attackable subset along with the required course perturbation.

Synthesis, antimalarial properties, and SAR studies of alkoxyurea-based HDAC inhibitors.

PubMed

Hansen, Finn K; Skinner-Adams, Tina S; Duffy, Sandra; Marek, Linda; Sumanadasa, Subathdrage D M; Kuna, Krystina; Held, Jana; Avery, Vicky M; Andrews, Katherine T; Kurz, Thomas

2014-03-01

Histone deacetylase (HDAC) inhibitors are an emerging class of potential antimalarial drugs. We investigated the antiplasmodial properties of 16 alkoxyurea-based HDAC inhibitors containing various cap and zinc binding groups (ZBGs). Ten compounds displayed sub-micromolar activity against the 3D7 line of Plasmodium falciparum. Structure-activity relationship studies revealed that a hydroxamic acid ZBG is crucial for antiplasmodial activity, and that the introduction of bulky alkyl substituents to cap groups increases potency against asexual blood-stage parasites. We also demonstrate that selected compounds cause hyperacetylation of P. falciparum histone H4, indicating inhibition of one or more PfHDACs. To assess the selectivity of alkoxyurea-based HDAC inhibitors for parasite over normal mammalian cells, the cytotoxicity of representative compounds was evaluated against neonatal foreskin fibroblast (NFF) cells. The most active compound, 6-((3-(4-(tert-butyl)phenyl)ureido)oxy)-N-hydroxyhexanamide (1 e, Pf3D7 IC50 : 0.16 μM) was 31-fold more toxic against the asexual blood stages than towards normal mammalian cells. Moreover, a subset of four structurally diverse HDAC inhibitors revealed moderate activity against late-stage (IV-V) gametocytes. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization of the myeloid-derived suppressor cell subset regulated by NK cells in malignant lymphoma.

PubMed

Sato, Yusuke; Shimizu, Kanako; Shinga, Jun; Hidaka, Michihiro; Kawano, Fumio; Kakimi, Kazuhiro; Yamasaki, Satoru; Asakura, Miki; Fujii, Shin-Ichiro

2015-03-01

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population with the ability to suppress immune responses and are currently classified into three distinct MDSC subsets: monocytic, granulocytic and non-monocytic, and non-granulocytic MDSCs. Although NK cells provide an important first-line defense against newly transformed cancer cells, it is unknown whether NK cells can regulate MDSC populations in the context of cancer. In this study, we initially found that the frequency of MDSCs in non-Hodgkin lymphoma (NHL) patients was increased and inversely correlated with that of NK cells, but not that of T cells. To investigate the regulation of MDSC subsets by NK cells, we used an EL4 murine lymphoma model and found the non-monocytic and non-granulocytic MDSC subset, i.e., Gr1 + CD11b + Ly6G med Ly6C med MDSC, is increased after NK cell depletion. The MDSC population that expresses MHC class II, CD80, CD124, and CCR2 is regulated mainly by CD27 + CD11b + NK cells. In addition, this MDSC subset produces some immunosuppressive cytokines, including IL-10 but not nitric oxide (NO) or arginase. We also examined two subsets of MDSCs (CD14 + HLA-DR - and CD14 - HLA-DR - MDSC) in NHL patients and found that higher IL-10-producing CD14 + HLA-DR - MDSC subset can be seen in lymphoma patients with reduced NK cell frequency in peripheral blood. Our analyses of MDSCs in this study may enable a better understanding of how MDSCs manipulate the tumor microenvironment and are regulated by NK cells in patients with lymphoma.

Characterization of the myeloid-derived suppressor cell subset regulated by NK cells in malignant lymphoma

PubMed Central

Sato, Yusuke; Shimizu, Kanako; Shinga, Jun; Hidaka, Michihiro; Kawano, Fumio; Kakimi, Kazuhiro; Yamasaki, Satoru; Asakura, Miki; Fujii, Shin-ichiro

2015-01-01

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population with the ability to suppress immune responses and are currently classified into three distinct MDSC subsets: monocytic, granulocytic and non-monocytic, and non-granulocytic MDSCs. Although NK cells provide an important first-line defense against newly transformed cancer cells, it is unknown whether NK cells can regulate MDSC populations in the context of cancer. In this study, we initially found that the frequency of MDSCs in non-Hodgkin lymphoma (NHL) patients was increased and inversely correlated with that of NK cells, but not that of T cells. To investigate the regulation of MDSC subsets by NK cells, we used an EL4 murine lymphoma model and found the non-monocytic and non-granulocytic MDSC subset, i.e., Gr1+CD11b+Ly6GmedLy6Cmed MDSC, is increased after NK cell depletion. The MDSC population that expresses MHC class II, CD80, CD124, and CCR2 is regulated mainly by CD27+CD11b+NK cells. In addition, this MDSC subset produces some immunosuppressive cytokines, including IL-10 but not nitric oxide (NO) or arginase. We also examined two subsets of MDSCs (CD14+HLA-DR− and CD14− HLA-DR− MDSC) in NHL patients and found that higher IL-10-producing CD14+HLA-DR−MDSC subset can be seen in lymphoma patients with reduced NK cell frequency in peripheral blood. Our analyses of MDSCs in this study may enable a better understanding of how MDSCs manipulate the tumor microenvironment and are regulated by NK cells in patients with lymphoma. PMID:25949922

The differentiation and protective function of cytolytic CD4 T cells in influenza infection

USDA-ARS?s Scientific Manuscript database

CD4 T cells that recognize peptide antigen in the context of Class II MHC can differentiate into various subsets that are characterized by their helper functions. However, increasing evidence indicates that CD4 cells with direct cytolytic activity play a role in chronic, as well as, acute infections...

Association analysis using USDA diverse rice (Oryza sativa L.) germplasm collections to identify loci influencing grain quality traits

USDA-ARS?s Scientific Manuscript database

he USDA rice (Oryza sativa L.) core subset (RCS) was assembled to represent the genetic diversity of the entire USDA-ARS National Small Grains Collection and consists of 1,794 accessions from 114 countries. The USDA rice mini-core (MC) is a subset of 217 accessions from the RCS and was selected to ...

Targeting Stereotyped B Cell Receptors from Chronic Lymphocytic Leukemia Patients with Synthetic Antigen Surrogates.

PubMed

Sarkar, Mohosin; Liu, Yun; Qi, Junpeng; Peng, Haiyong; Morimoto, Jumpei; Rader, Christoph; Chiorazzi, Nicholas; Kodadek, Thomas

2016-04-01

Chronic lymphocytic leukemia (CLL) is a disease in which a single B-cell clone proliferates relentlessly in peripheral lymphoid organs, bone marrow, and blood. DNA sequencing experiments have shown that about 30% of CLL patients have stereotyped antigen-specific B-cell receptors (BCRs) with a high level of sequence homology in the variable domains of the heavy and light chains. These include many of the most aggressive cases that haveIGHV-unmutated BCRs whose sequences have not diverged significantly from the germ line. This suggests a personalized therapy strategy in which a toxin or immune effector function is delivered selectively to the pathogenic B-cells but not to healthy B-cells. To execute this strategy, serum-stable, drug-like compounds able to target the antigen-binding sites of most or all patients in a stereotyped subset are required. We demonstrate here the feasibility of this approach with the discovery of selective, high affinity ligands for CLL BCRs of the aggressive, stereotyped subset 7P that cross-react with the BCRs of several CLL patients in subset 7p, but not with BCRs from patients outside this subset. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Evolutionary dynamics of enzymes.

PubMed

Demetrius, L

1995-08-01

This paper codifies and rationalizes the large diversity in reaction rates and substrate specificity of enzymes in terms of a model which postulates that the kinetic properties of present-day enzymes are the consequence of the evolutionary force of mutation and selection acting on a class of primordial enzymes with poor catalytic activity and broad substrate specificity. Enzymes are classified in terms of their thermodynamic parameters, activation enthalpy delta H* and activation entropy delta S*, in their kinetically significant transition states as follows: type 1, delta H* > 0, delta S* < 0; type 2, delta H* < or = 0, delta S* < or = 0; type 3, delta H* > 0, delta S* > 0. We study the evolutionary dynamics of these three classes of enzymes subject to mutation, which acts at the level of the gene which codes for the enzyme and selection, which acts on the organism that contains the enzyme. Our model predicts the following evolutionary trends in the reaction rate and binding specificity for the three classes of molecules. In type 1 enzymes, evolution results in random, non-directional changes in the reaction rate and binding specificity. In type 2 and 3 enzymes, evolution results in a unidirectional increase in both the reaction rate and binding specificity. We exploit these results in order to codify the diversity in functional properties of present-day enzymes. Type 1 molecules will be described by intermediate reaction rates and broad substrate specificity. Type 2 enzymes will be characterized by diffusion-controlled rates and absolute substrate specificity. The type 3 catalysts can be further subdivided in terms of their activation enthalpy into two classes: type 3a (delta H* small) and type 3b (delta H* large). We show that type 3a will be represented by the same functional properties that identify type 2, namely, diffusion-controlled rates and absolute substrate specificity, whereas type 3b will be characterized by non-diffusion-controlled rates and absolute substrate specificity. We infer from this depiction of the three classes of enzymes, a general relation between the two functional properties, reaction rate and substrate specificity, namely, enzymes with diffusion-controlled rates have absolute substrate specificity. By appealing to energetic considerations, we furthermore show that enzymes with diffusion-controlled rates (types 2 and 3a) form a small subset of the class of all enzymes. This codification of present-day enzymes derived from an evolutionary model, essentially relates the structural properties of enzymes, as described by their thermodynamic parameters, to their functional properties, as represented by the reaction rate and substrate specificity.

Evaluation of the effect of selective serotonin-reuptake inhibitors on lymphocyte subsets in patients with a major depressive disorder.

PubMed

Hernandez, Maria Eugenia; Martinez-Fong, Daniel; Perez-Tapia, Mayra; Estrada-Garcia, Iris; Estrada-Parra, Sergio; Pavón, Lenin

2010-02-01

To date, only the effect of a short-term antidepressant treatment (<12 weeks) on neuroendocrinoimmune alterations in patients with a major depressive disorder has been evaluated. Our objective was to determine the effect of a 52-week long treatment with selective serotonin-reuptake inhibitors on lymphocyte subsets. The participants were thirty-one patients and twenty-two healthy volunteers. The final number of patients (10) resulted from selection and course, as detailed in the enrollment scheme. Methods used to psychiatrically analyze the participants included the Mini-International Neuropsychiatric Interview, Hamilton Depression Scale and Beck Depression Inventory. The peripheral lymphocyte subsets were measured in peripheral blood using flow cytometry. Before treatment, increased counts of natural killer (NK) cells in patients were statistically significant when compared with those of healthy volunteers (312+/-29 versus 158+/-30; cells/mL), but no differences in the populations of T and B cells were found. The patients showed remission of depressive episodes after 20 weeks of treatment along with an increase in NK cell and B cell populations, which remained increased until the end of the study. At the 52nd week of treatment, patients showed an increase in the counts of NK cells (396+/-101 cells/mL) and B cells (268+/-64 cells/mL) compared to healthy volunteers (NK, 159+/-30 cells/mL; B cells, 179+/-37 cells/mL). We conclude that long-term treatment with selective serotonin-reuptake inhibitors not only causes remission of depressive symptoms, but also affects lymphocyte subset populations. The physiopathological consequence of these changes remains to be determined.

Updating estimates of low streamflow statistics to account for possible trends

NASA Astrophysics Data System (ADS)

Blum, A. G.; Archfield, S. A.; Hirsch, R. M.; Vogel, R. M.; Kiang, J. E.; Dudley, R. W.

2017-12-01

Given evidence of both increasing and decreasing trends in low flows in many streams, methods are needed to update estimators of low flow statistics used in water resources management. One such metric is the 10-year annual low-flow statistic (7Q10) calculated as the annual minimum seven-day streamflow which is exceeded in nine out of ten years on average. Historical streamflow records may not be representative of current conditions at a site if environmental conditions are changing. We present a new approach to frequency estimation under nonstationary conditions that applies a stationary nonparametric quantile estimator to a subset of the annual minimum flow record. Monte Carlo simulation experiments were used to evaluate this approach across a range of trend and no trend scenarios. Relative to the standard practice of using the entire available streamflow record, use of a nonparametric quantile estimator combined with selection of the most recent 30 or 50 years for 7Q10 estimation were found to improve accuracy and reduce bias. Benefits of data subset selection approaches were greater for higher magnitude trends annual minimum flow records with lower coefficients of variation. A nonparametric trend test approach for subset selection did not significantly improve upon always selecting the last 30 years of record. At 174 stream gages in the Chesapeake Bay region, 7Q10 estimators based on the most recent 30 years of flow record were compared to estimators based on the entire period of record. Given the availability of long records of low streamflow, using only a subset of the flow record ( 30 years) can be used to update 7Q10 estimators to better reflect current streamflow conditions.

Collectively loading an application in a parallel computer

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

Aho, Michael E.; Attinella, John E.; Gooding, Thomas M.

Collectively loading an application in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: identifying, by a parallel computer control system, a subset of compute nodes in the parallel computer to execute a job; selecting, by the parallel computer control system, one of the subset of compute nodes in the parallel computer as a job leader compute node; retrieving, by the job leader compute node from computer memory, an application for executing the job; and broadcasting, by the job leader to the subset of compute nodes in the parallel computer, the application for executing the job.

Non-native Speech Perception Training Using Vowel Subsets: Effects of Vowels in Sets and Order of Training

PubMed Central

Nishi, Kanae; Kewley-Port, Diane

2008-01-01

Purpose Nishi and Kewley-Port (2007) trained Japanese listeners to perceive nine American English monophthongs and showed that a protocol using all nine vowels (fullset) produced better results than the one using only the three more difficult vowels (subset). The present study extended the target population to Koreans and examined whether protocols combining the two stimulus sets would provide more effective training. Method Three groups of five Korean listeners were trained on American English vowels for nine days using one of the three protocols: fullset only, first three days on subset then six days on fullset, or first six days on fullset then three days on subset. Participants' performance was assessed by pre- and post-training tests, as well as by a mid-training test. Results 1) Fullset training was also effective for Koreans; 2) no advantage was found for the two combined protocols over the fullset only protocol, and 3) sustained “non-improvement” was observed for training using one of the combined protocols. Conclusions In using subsets for training American English vowels, care should be taken not only in the selection of subset vowels, but also for the training orders of subsets. PMID:18664694

Ensemble Feature Learning of Genomic Data Using Support Vector Machine

PubMed Central

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R.; Braytee, Ali; Kennedy, Paul J.

2016-01-01

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. PMID:27304923

Antiviral CD8+ T Cells Restricted by Human Leukocyte Antigen Class II Exist during Natural HIV Infection and Exhibit Clonal Expansion.

PubMed

Ranasinghe, Srinika; Lamothe, Pedro A; Soghoian, Damien Z; Kazer, Samuel W; Cole, Michael B; Shalek, Alex K; Yosef, Nir; Jones, R Brad; Donaghey, Faith; Nwonu, Chioma; Jani, Priya; Clayton, Gina M; Crawford, Frances; White, Janice; Montoya, Alana; Power, Karen; Allen, Todd M; Streeck, Hendrik; Kaufmann, Daniel E; Picker, Louis J; Kappler, John W; Walker, Bruce D

2016-10-18

CD8 + T cell recognition of virus-infected cells is characteristically restricted by major histocompatibility complex (MHC) class I, although rare examples of MHC class II restriction have been reported in Cd4-deficient mice and a macaque SIV vaccine trial using a recombinant cytomegalovirus vector. Here, we demonstrate the presence of human leukocyte antigen (HLA) class II-restricted CD8 + T cell responses with antiviral properties in a small subset of HIV-infected individuals. In these individuals, T cell receptor β (TCRβ) analysis revealed that class II-restricted CD8 + T cells underwent clonal expansion and mediated killing of HIV-infected cells. In one case, these cells comprised 12% of circulating CD8 + T cells, and TCRα analysis revealed two distinct co-expressed TCRα chains, with only one contributing to binding of the class II HLA-peptide complex. These data indicate that class II-restricted CD8 + T cell responses can exist in a chronic human viral infection, and may contribute to immune control. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

Conformation-selective inhibitors reveal differences in the activation and phosphate-binding loops of the tyrosine kinases Abl and Src

PubMed Central

Hari, Sanjay B.; Perera, B. Gayani K.; Ranjitkar, Pratistha; Seeliger, Markus A.; Maly, Dustin J.

2013-01-01

Over the last decade, an increasingly diverse array of potent and selective inhibitors that target the ATP-binding sites of protein kinases have been developed. Many of these inhibitors, like the clinically approved drug imatinib (Gleevec), stabilize a specific catalytically inactive ATP-binding site conformation of their kinases targets. Imatinib is notable in that it is highly selective for its kinase target, Abl, over other closely-related tyrosine kinases, like Src. In addition, imatinib is highly sensitive to the phosphorylation state of Abl's activation loop, which is believed to be a general characteristic of all inhibitors that stabilize a similar inactive ATP-binding site conformation. In this report, we perform a systematic analysis of a diverse series of ATP-competitive inhibitors that stabilize a similar inactive ATP-binding site conformation as imatinib with the tyrosine kinases Src and Abl. In contrast to imatinib, many of these inhibitors have very similar potencies against Src and Abl. Furthermore, only a subset of this class of inhibitors is sensitive to the phosphorylation state of the activation loop of these kinases. In attempting to explain this observation, we have uncovered an unexpected correlation between Abl's activation loop and another flexible active site feature, called the phosphate-binding loop (p-loop). These studies shed light on how imatinib is able to obtain its high target selectivity and reveal how the conformational preference of flexible active site regions can vary between closely related kinases. PMID:24106839

Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

PubMed Central

Alamedine, D.; Khalil, M.; Marque, C.

2013-01-01

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification. PMID:24454536

HIV-1 protease cleavage site prediction based on two-stage feature selection method.

PubMed

Niu, Bing; Yuan, Xiao-Cheng; Roeper, Preston; Su, Qiang; Peng, Chun-Rong; Yin, Jing-Yuan; Ding, Juan; Li, HaiPeng; Lu, Wen-Cong

2013-03-01

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

The role of cocrystals in pharmaceutical science.

PubMed

Shan, Ning; Zaworotko, Michael J

2008-05-01

Pharmaceutical cocrystals, a subset of a long known but little-studied class of compounds, represent an emerging class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they can significantly diversify the number of crystal forms that exist for a particular active pharmaceutical ingredient (API), and they can lead to improvements in physical properties of clinical relevance. In this article we address pharmaceutical cocrystals from the perspective of design (crystal engineering) and present a series of case studies that demonstrate how they can enhance the solubility, bioavailability, and/or stability of API crystal forms.

Characterization of VPS34-IN1, a selective inhibitor of Vps34, reveals that the phosphatidylinositol 3-phosphate-binding SGK3 protein kinase is a downstream target of class III phosphoinositide 3-kinase.

PubMed

Bago, Ruzica; Malik, Nazma; Munson, Michael J; Prescott, Alan R; Davies, Paul; Sommer, Eeva; Shpiro, Natalia; Ward, Richard; Cross, Darren; Ganley, Ian G; Alessi, Dario R

2014-11-01

The Vps34 (vacuolar protein sorting 34) class III PI3K (phosphoinositide 3-kinase) phosphorylates PtdIns (phosphatidylinositol) at endosomal membranes to generate PtdIns(3)P that regulates membrane trafficking processes via its ability to recruit a subset of proteins possessing PtdIns(3)P-binding PX (phox homology) and FYVE domains. In the present study, we describe a highly selective and potent inhibitor of Vps34, termed VPS34-IN1, that inhibits Vps34 with 25 nM IC50 in vitro, but does not significantly inhibit the activity of 340 protein kinases or 25 lipid kinases tested that include all isoforms of class I as well as class II PI3Ks. Administration of VPS34-IN1 to cells induces a rapid dose-dependent dispersal of a specific PtdIns(3)P-binding probe from endosome membranes, within 1 min, without affecting the ability of class I PI3K to regulate Akt. Moreover, we explored whether SGK3 (serum- and glucocorticoid-regulated kinase-3), the only protein kinase known to interact specifically with PtdIns(3)P via its N-terminal PX domain, might be controlled by Vps34. Mutations disrupting PtdIns(3)P binding ablated SGK3 kinase activity by suppressing phosphorylation of the T-loop [PDK1 (phosphoinositide-dependent kinase 1) site] and hydrophobic motif (mammalian target of rapamycin site) residues. VPS34-IN1 induced a rapid ~50-60% loss of SGK3 phosphorylation within 1 min. VPS34-IN1 did not inhibit activity of the SGK2 isoform that does not possess a PtdIns(3)P-binding PX domain. Furthermore, class I PI3K inhibitors (GDC-0941 and BKM120) that do not inhibit Vps34 suppressed SGK3 activity by ~40%. Combining VPS34-IN1 and GDC-0941 reduced SGK3 activity ~80-90%. These data suggest SGK3 phosphorylation and hence activity is controlled by two pools of PtdIns(3)P. The first is produced through phosphorylation of PtdIns by Vps34 at the endosome. The second is due to the conversion of class I PI3K product, PtdIns(3,4,5)P3 into PtdIns(3)P, via the sequential actions of the PtdIns 5-phosphatases [SHIP1/2 (Src homology 2-domain-containing inositol phosphatase 1/2)] and PtdIns 4-phosphatase [INPP4B (inositol polyphosphate 4-phosphatase type II)]. VPS34-IN1 will be a useful probe to delineate physiological roles of the Vps34. Monitoring SGK3 phosphorylation and activity could be employed as a biomarker of Vps34 activity, in an analogous manner by which Akt is used to probe cellular class I PI3K activity. Combining class I (GDC-0941) and class III (VPS34-IN1) PI3K inhibitors could be used as a strategy to better analyse the roles and regulation of the elusive class II PI3K.

RNA-Interference Pathways Display High Rates of Adaptive Protein Evolution in Multiple Invertebrates

PubMed Central

Palmer, William H.; Hadfield, Jarrod D.; Obbard, Darren J.

2018-01-01

Conflict between organisms can lead to a reciprocal adaptation that manifests as an increased evolutionary rate in genes mediating the conflict. This adaptive signature has been observed in RNA-interference (RNAi) pathway genes involved in the suppression of viruses and transposable elements in Drosophila melanogaster, suggesting that a subset of Drosophila RNAi genes may be locked in an arms race with these parasites. However, it is not known whether rapid evolution of RNAi genes is a general phenomenon across invertebrates, or which RNAi genes generally evolve adaptively. Here we use population genomic data from eight invertebrate species to infer rates of adaptive sequence evolution, and to test for past and ongoing selective sweeps in RNAi genes. We assess rates of adaptive protein evolution across species using a formal meta-analytic framework to combine data across species and by implementing a multispecies generalized linear mixed model of mutation counts. Across species, we find that RNAi genes display a greater rate of adaptive protein substitution than other genes, and that this is primarily mediated by positive selection acting on the genes most likely to defend against viruses and transposable elements. In contrast, evidence for recent selective sweeps is broadly spread across functional classes of RNAi genes and differs substantially among species. Finally, we identify genes that exhibit elevated adaptive evolution across the analyzed insect species, perhaps due to concurrent parasite-mediated arms races. PMID:29437826

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

PubMed Central

Mas, Sergi; Gassó, Patricia; Morer, Astrid; Calvo, Anna; Bargalló, Nuria; Lafuente, Amalia; Lázaro, Luisa

2016-01-01

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder. PMID:27093171

A Gay Immigrant Student's Perspective: Unspeakable Acts in the Language Class

ERIC Educational Resources Information Center

Nelson, Cynthia D.

2010-01-01

This article focuses on a subset of the student cohort that has, until recently, been largely hidden from view in the literature of language education: gay immigrants. Little is known about what sorts of classroom experiences gay immigrant students find engaging or alienating, or why this sort of knowledge is needed. This case study uses interview…

Inclusive Practices in the Teaching of Mathematics: Supporting the Work of Effective Primary Teachers

ERIC Educational Resources Information Center

Clarke, Barbara; Faragher, Rhonda

2015-01-01

The practices of effective primary school teachers including students with Down syndrome in their mathematics classes are largely unexplored and many teachers feel unprepared to teach students with intellectual disabilities. A study with cohorts in Victoria and the ACT is underway and here we report a subset of findings concerning the support…

New insights into the targeting of a sub-set of tail-anchored proteins to the outer mitochondrial membrane

USDA-ARS?s Scientific Manuscript database

Tail-anchored (TA) proteins are a unique class of functionally diverse membrane proteins that are defined by their single C-terminal membrane-spanning domain and their ability to insert post-translationally into specific organelles with an Nout-Cin orientation. The molecular mechanisms by which TA p...

Call Me "Madame": Re-Presenting Culture in the French Language Classroom

ERIC Educational Resources Information Center

Siskin, H. Jay

2007-01-01

This study examines autobiographies of American teachers of French in order to make explicit their beliefs regarding French language and culture. The themes of class and power are prominent in these teachers' belief systems, as is the desire for self-transformation through mastery of French and miming a subset of French behaviors. These notions…

Sample size determination for bibliographic retrieval studies

PubMed Central

Yao, Xiaomei; Wilczynski, Nancy L; Walter, Stephen D; Haynes, R Brian

2008-01-01

Background Research for developing search strategies to retrieve high-quality clinical journal articles from MEDLINE is expensive and time-consuming. The objective of this study was to determine the minimal number of high-quality articles in a journal subset that would need to be hand-searched to update or create new MEDLINE search strategies for treatment, diagnosis, and prognosis studies. Methods The desired width of the 95% confidence intervals (W) for the lowest sensitivity among existing search strategies was used to calculate the number of high-quality articles needed to reliably update search strategies. New search strategies were derived in journal subsets formed by 2 approaches: random sampling of journals and top journals (having the most high-quality articles). The new strategies were tested in both the original large journal database and in a low-yielding journal (having few high-quality articles) subset. Results For treatment studies, if W was 10% or less for the lowest sensitivity among our existing search strategies, a subset of 15 randomly selected journals or 2 top journals were adequate for updating search strategies, based on each approach having at least 99 high-quality articles. The new strategies derived in 15 randomly selected journals or 2 top journals performed well in the original large journal database. Nevertheless, the new search strategies developed using the random sampling approach performed better than those developed using the top journal approach in a low-yielding journal subset. For studies of diagnosis and prognosis, no journal subset had enough high-quality articles to achieve the expected W (10%). Conclusion The approach of randomly sampling a small subset of journals that includes sufficient high-quality articles is an efficient way to update or create search strategies for high-quality articles on therapy in MEDLINE. The concentrations of diagnosis and prognosis articles are too low for this approach. PMID:18823538

Identification of FDA-approved oncology drugs with selective potency in high-risk childhood ependymoma.

PubMed

Donson, Andrew M; Amani, Vladimir; Warner, Elliot A; Griesinger, Andrea M; Witt, Davis A; Mulcahy Levy, Jean M; Hoffman, Lindsey M; Hankinson, Todd C; Handler, Michael H; Vibhakar, Rajeev; Dorris, Kathleen; Foreman, Nicholas K

2018-06-20

Children with ependymoma are cured in less than 50% of cases, with little improvement in outcome over the last several decades. Chemotherapy has not impacted survival in ependymoma, due in part to a lack of preclinical models that has precluded comprehensive drug testing. We recently developed two human ependymoma cell lines harboring high-risk phenotypes which provided us with an opportunity to execute translational studies. ependymoma and other pediatric brain tumor cell lines were subject to a large-scale comparative drug screen of ependymoma -approved oncology drugs for rapid clinical application. The results of this in vitro study were combined with in silico prediction of drug sensitivity to identify ependymoma -selective compounds, which were validated by dose curve and time course modelling. Mechanisms of ependymoma -selective antitumor effect were further investigated using transcriptome and proteome analyses. We identified three classes of oncology drugs that showed ependymoma -selective anti-tumor effect, namely (i) fluorinated pyrimidines (5-fluorouracil, carmofur and floxuridine), (ii) retinoids (bexarotene, tretinoin and isotretinoin), and (iii) a subset of small-molecule multi-receptor tyrosine kinase inhibitors (axitinib, imatinib and pazopanib). Axitinib's anti-tumor mechanism in ependymoma cell lines involved inhibition of PDGFR-alpha and PDGFR-beta and was associated with reduced mitosis-related gene expression and cellular senescence. The clinically available, ependymoma -selective oncology drugs identified by our study have the potential to critically inform design of upcoming clinical studies in ependymoma, in particular for those children with recurrent ependymoma who are in the greatest need of novel therapeutic approaches. Copyright ©2018, American Association for Cancer Research.

Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.

PubMed

Cao, Peng; Liu, Xiaoli; Yang, Jinzhu; Zhao, Dazhe; Huang, Min; Zhang, Jian; Zaiane, Osmar

2017-12-01

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response). To better capture the complicated but more flexible relationship, we propose a multi-kernel based dimensionality reduction and over-sampling approaches. We combined Marginal Fisher Analysis with ℓ 2,1 -norm based multi-kernel learning (MKMFA) to achieve the sparsity of region-of-interest (ROI), which leads to simultaneously selecting a subset of the relevant brain regions and learning a dimensionality transformation. Meanwhile, a multi-kernel over-sampling (MKOS) was developed to generate synthetic instances in the optimal kernel space induced by MKMFA, so as to compensate for the class imbalanced distribution. We comprehensively evaluate the proposed models for the diagnostic classification (binary class and multi-class classification) including all subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results not only demonstrate the proposed method has superior performance over multiple comparable methods, but also identifies relevant imaging biomarkers that are consistent with prior medical knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.

What MISR data are available for field experiments?

Atmospheric Science Data Center

2014-12-08

MISR data and imagery are available for many field campaigns. Select data products are subset for the region and dates of interest. Special gridded regional products may be available as well as Local Mode data for select targets...

Hantaviruses induce cell type- and viral species-specific host microRNA expression signatures

PubMed Central

Shin, Ok Sarah; Kumar, Mukesh; Yanagihara, Richard; Song, Jin-Won

2014-01-01

The mechanisms of hantavirus-induced modulation of host cellular immunity remain poorly understood. Recently, microRNAs (miRNAs) have emerged as a class of essential regulators of host immune response genes. To ascertain if differential host miRNA expression toward representative hantavirus species correlated with immune response genes, miRNA expression profiles were analyzed in human endothelial cells, macrophages and epithelial cells infected with pathogenic and nonpathogenic rodent- and shrew-borne hantaviruses. Distinct miRNA expression profiles were observed in a cell type- and viral species-specific pattern. A subset of miRNAs, including miR-151-5p and miR-1973, were differentially expressed between Hantaan virus and Prospect Hill virus. Pathway analyses confirmed that the targets of selected miRNAs were associated with inflammatory responses and innate immune receptor-mediated signaling pathways. Our data suggest that differential immune responses following hantavirus infection may be regulated in part by cellular miRNA through dysregulation of genes critical to the inflammatory process. PMID:24074584

Chemically triggered drug release from an antibody-drug conjugate leads to potent antitumour activity in mice.

PubMed

Rossin, Raffaella; Versteegen, Ron M; Wu, Jeremy; Khasanov, Alisher; Wessels, Hans J; Steenbergen, Erik J; Ten Hoeve, Wolter; Janssen, Henk M; van Onzen, Arthur H A M; Hudson, Peter J; Robillard, Marc S

2018-05-04

Current antibody-drug conjugates (ADCs) target internalising receptors on cancer cells leading to intracellular drug release. Typically, only a subset of patients with solid tumours has sufficient expression of such a receptor, while there are suitable non-internalising receptors and stroma targets. Here, we demonstrate potent therapy in murine tumour models using a non-internalising ADC that releases its drugs upon a click reaction with a chemical activator, which is administered in a second step. This was enabled by the development of a diabody-based ADC with a high tumour uptake and very low retention in healthy tissues, allowing systemic administration of the activator 2 days later, leading to efficient and selective activation throughout the tumour. In contrast, the analogous ADC comprising the protease-cleavable linker used in the FDA approved ADC Adcetris is not effective in these tumour models. This first-in-class ADC holds promise for a broader applicability of ADCs across patient populations.

Multiple comparison analysis testing in ANOVA.

PubMed

McHugh, Mary L

2011-01-01

The Analysis of Variance (ANOVA) test has long been an important tool for researchers conducting studies on multiple experimental groups and one or more control groups. However, ANOVA cannot provide detailed information on differences among the various study groups, or on complex combinations of study groups. To fully understand group differences in an ANOVA, researchers must conduct tests of the differences between particular pairs of experimental and control groups. Tests conducted on subsets of data tested previously in another analysis are called post hoc tests. A class of post hoc tests that provide this type of detailed information for ANOVA results are called "multiple comparison analysis" tests. The most commonly used multiple comparison analysis statistics include the following tests: Tukey, Newman-Keuls, Scheffee, Bonferroni and Dunnett. These statistical tools each have specific uses, advantages and disadvantages. Some are best used for testing theory while others are useful in generating new theory. Selection of the appropriate post hoc test will provide researchers with the most detailed information while limiting Type 1 errors due to alpha inflation.

Feature selection with harmony search.

PubMed

Diao, Ren; Shen, Qiang

2012-12-01

Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.

Adoptive therapy with chimeric antigen receptor-modified T cells of defined subset composition.

PubMed

Riddell, Stanley R; Sommermeyer, Daniel; Berger, Carolina; Liu, Lingfeng Steven; Balakrishnan, Ashwini; Salter, Alex; Hudecek, Michael; Maloney, David G; Turtle, Cameron J

2014-01-01

The ability to engineer T cells to recognize tumor cells through genetic modification with a synthetic chimeric antigen receptor has ushered in a new era in cancer immunotherapy. The most advanced clinical applications are in targeting CD19 on B-cell malignancies. The clinical trials of CD19 chimeric antigen receptor therapy have thus far not attempted to select defined subsets before transduction or imposed uniformity of the CD4 and CD8 cell composition of the cell products. This review will discuss the rationale for and challenges to using adoptive therapy with genetically modified T cells of defined subset and phenotypic composition.

Differential risk theory as a subset of social exchange theory: implications for making gay marriage culturally normative and for understanding stigma against homosexuals.

PubMed

Schumm, Walter R

2004-02-01

Differential risk theory, a subset of social exchange and equity theories, is proposed as an explanation for stigma towards homosexuals and as a basis for normative preferences for heterosexual marriage. Numerous gender differences involved in long-term relationships require members of such close relationships to assume greater interpersonal and social risks and thus costs, compared to same-gender relationships. Without compensating rewards or reduced costs, heterosexual relationships would be unfairly disadvantaged. Resistance to making gay marriage normative and/or equivalent legally to heterosexual marriage may be traced, rather than to homophobia, to societal attempts to maintain equity between classes of relationships characterized by inherent differential risks.

Flexible ordering of antibody class switch and V(D)J joining during B-cell ontogeny

PubMed Central

Kumar, Satyendra; Wuerffel, Robert; Achour, Ikbel; Lajoie, Bryan; Sen, Ranjan; Dekker, Job; Feeney, Ann J.; Kenter, Amy L.

2013-01-01

V(D)J joining is mediated by RAG recombinase during early B-lymphocyte development in the bone marrow (BM). Activation-induced deaminase initiates isotype switching in mature B cells of secondary lymphoid structures. Previous studies questioned the strict ontological partitioning of these processes. We show that pro-B cells undergo robust switching to a subset of immunoglobulin H (IgH) isotypes. Chromatin studies reveal that in pro-B cells, the spatial organization of the Igh locus may restrict switching to this subset of isotypes. We demonstrate that in the BM, V(D)J joining and switching are interchangeably inducible, providing an explanation for the hyper-IgE phenotype of Omenn syndrome. PMID:24240234

GDC-0941, a novel class I selective PI3K inhibitor, enhances the efficacy of docetaxel in human breast cancer models by increasing cell death in vitro and in vivo.

PubMed

Wallin, Jeffrey J; Guan, Jane; Prior, Wei Wei; Lee, Leslie B; Berry, Leanne; Belmont, Lisa D; Koeppen, Hartmut; Belvin, Marcia; Friedman, Lori S; Sampath, Deepak

2012-07-15

Docetaxel is a front-line standard-of-care chemotherapeutic drug for the treatment of breast cancer. Phosphoinositide 3-kinases (PI3K) are lipid kinases that regulate breast tumor cell growth, migration, and survival. The current study was intended to determine whether GDC-0941, an orally bioavailable class I selective PI3K inhibitor, enhances the antitumor activity of docetaxel in human breast cancer models in vitro and in vivo. A panel of 25 breast tumor cell lines representing HER2+, luminal, and basal subtypes were treated with GDC-0941, docetaxel, or the combination of both drugs and assayed for cellular viability, modulation of PI3K pathway markers, and apoptosis induction. Drug combination effects on cellular viability were also assessed in nontransformed MCF10A human mammary epithelial cells. Human xenografts of breast cancer cell lines and patient-derived tumors were used to assess efficacy of GDC-0941 and docetaxel in vivo. Combination of GDC-0941 and docetaxel decreased the cellular viability of breast tumor cell lines in vitro but to variable degrees of drug synergy. Compared with nontransformed MCF10A cells, the addition of both drugs resulted in stronger synergistic effects in a subset of tumor cell lines that were not predicted by breast cancer subtype. In xenograft models, GDC-0941 enhanced the antitumor activity of docetaxel with maximum combination efficacy observed within 1 hour of administering both drugs. GDC-0941 increased the rate of apoptosis in cells arrested in mitosis upon cotreatment with docetaxel. GDC-0941 augments the efficacy of docetaxel by increasing drug-induced apoptosis in breast cancer models.

Self-organized modularization in evolutionary algorithms.

PubMed

Dauscher, Peter; Uthmann, Thomas

2005-01-01

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

A study of the relationships between "highly qualified" status, instructional practices, and students' science achievement in three high poverty Louisiana school systems

NASA Astrophysics Data System (ADS)

Clayton, Michelle

Using a mixed methods research design, the author examined the relationships between "highly qualified" status, instructional practices, and students' science achievement for six third grade teachers in three high poverty Louisiana school systems. The study analyzed qualitative and quantitative data for three science classes taught by "highly qualified" teachers and three science classes taught by "non-highly qualified" teachers. The qualitative portion of the study was conducted through classroom observations, teacher interviews, and lesson plan reviews. The qualitative data was coded and triangulated to determine whether the instructional practices of each teacher were more "teacher-centered" or "student-centered." The qualitative data analysis indicated various patterns and consistencies in the instructional practices used by the "highly qualified" and "non-highly qualified" teachers selected for this study. The quantitative portion of the study involved analysis of the students' science achievement data for the six third grade science teachers selected for the study. Science achievement was measured by the third grade Integrated Louisiana Education Assessment Program (iLEAP) scores. A two-way ANOVA indicated that there were statistically significant differences in the mean scores of the three high poverty Louisiana school systems as well as the students taught by "highly qualified" and "non-highly qualified" teachers and the interactions between the two: F(2, 123) = 46.99, p < 0.01; F(1, 123) = 4.54, p = 0.035; F(2, 123) = 3.73, p = 0.027. A separate one-way ANOVA indicated that statistically significant differences existed between the six participating teachers in the study: F (5, 123) = 20.386, p < 0.01). Tukey's HSD post-hoc tests and homogeneous subset analyses were conducted in order to determine which teachers' scores significantly differed from each other.

How to Achieve Better Results Using Pass-Based Virtual Screening: Case Study for Kinase Inhibitors

NASA Astrophysics Data System (ADS)

Pogodin, Pavel V.; Lagunin, Alexey A.; Rudik, Anastasia V.; Filimonov, Dmitry A.; Druzhilovskiy, Dmitry S.; Nicklaus, Mark C.; Poroikov, Vladimir V.

2018-04-01

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of “active” and “inactive” compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

The Sociophonetic and Acoustic Vowel Dynamics of Michigan's Upper Peninsula English

NASA Astrophysics Data System (ADS)

Rankinen, Wil A.

The present sociophonetic study examines the English variety in Michigan's Upper Peninsula (UP) based upon a 130-speaker sample from Marquette County. The linguistic variables of interest include seven monophthongs and four diphthongs: 1) front lax, 2) low back, and 3) high back monophthongs and 4) short and 5) long diphthongs. The sample is stratified by the predictor variables of heritage-location, bilingualism, age, sex and class. The aim of the thesis is two fold: 1) to determine the extent of potential substrate effects on a 71-speaker older-aged bilingual and monolingual subset of these UP English speakers focusing on the predictor variables of heritage-location and bilingualism, and 2) to determine the extent of potential exogenous influences on an 85-speaker subset of UP English monolingual speakers by focusing on the predictor variables of heritage-location, age, sex and class. All data were extracted from a reading passage task collected during a sociolinguistic interview and measured instrumentally. The findings of this apparent-time data reveal the presence of lingering effects from substrate sources and developing effects from exogenous sources based upon American and Canadian models of diffusion. The linguistic changes-in-progress from above, led by middle-class females, are taking shape in the speech of UP residents of whom are propagating linguistic phenomena typically associated with varieties of Canadian English (i.e., low-back merger, Canadian shift, and Canadian raising); however, the findings also report resistance of such norms by working-class females. Finally, the data also reveal substrate effects demonstrating cases of dialect leveling and maintenance. As a result, the speech spoken in Michigan's Upper Peninsula can presently be described as a unique variety of English comprised of lingering substrate effects as well as exogenous effects modeled from both American and Canadian English linguistic norms.

Gα12 structural determinants of Hsp90 interaction are necessary for serum response element-mediated transcriptional activation.

PubMed

Montgomery, Ellyn R; Temple, Brenda R S; Peters, Kimberly A; Tolbert, Caitlin E; Booker, Brandon K; Martin, Joseph W; Hamilton, Tyler P; Tagliatela, Alicia C; Smolski, William C; Rogers, Stephen L; Jones, Alan M; Meigs, Thomas E

2014-04-01

The G12/13 class of heterotrimeric G proteins, comprising the α-subunits Gα12 and Gα13, regulates multiple aspects of cellular behavior, including proliferation and cytoskeletal rearrangements. Although guanine nucleotide exchange factors for the monomeric G protein Rho (RhoGEFs) are well characterized as effectors of this G protein class, a variety of other downstream targets has been reported. To identify Gα12 determinants that mediate specific protein interactions, we used a structural and evolutionary comparison between the G12/13, Gs, Gi, and Gq classes to identify "class-distinctive" residues in Gα12 and Gα13. Mutation of these residues in Gα12 to their deduced ancestral forms revealed a subset necessary for activation of serum response element (SRE)-mediated transcription, a G12/13-stimulated pathway implicated in cell proliferative signaling. Unexpectedly, this subset of Gα12 mutants showed impaired binding to heat-shock protein 90 (Hsp90) while retaining binding to RhoGEFs. Corresponding mutants of Gα13 exhibited robust SRE activation, suggesting a Gα12-specific mechanism, and inhibition of Hsp90 by geldanamycin or small interfering RNA-mediated lowering of Hsp90 levels resulted in greater downregulation of Gα12 than Gα13 signaling in SRE activation experiments. Furthermore, the Drosophila G12/13 homolog Concertina was unable to signal to SRE in mammalian cells, and Gα12:Concertina chimeras revealed Gα12-specific determinants of SRE activation within the switch regions and a C-terminal region. These findings identify Gα12 determinants of SRE activation, implicate Gα12:Hsp90 interaction in this signaling mechanism, and illuminate structural features that arose during evolution of Gα12 and Gα13 to allow bifurcated mechanisms of signaling to a common cell proliferative pathway.

Column Subset Selection, Matrix Factorization, and Eigenvalue Optimization

DTIC Science & Technology

2008-07-01

Pietsch and Grothendieck, which are regarded as basic instruments in modern functional analysis [Pis86]. • The methods for computing these... Pietsch factorization and the maxcut semi- definite program [GW95]. 1.2. Overview. We focus on the algorithmic version of the Kashin–Tzafriri theorem...will see that the desired subset is exposed by factoring the random submatrix. This factorization, which was invented by Pietsch , is regarded as a basic

Selection-Fusion Approach for Classification of Datasets with Missing Values

PubMed Central

Ghannad-Rezaie, Mostafa; Soltanian-Zadeh, Hamid; Ying, Hao; Dong, Ming

2010-01-01

This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values. PMID:20212921

Evaluation of a developmental hierarchy for breast cancer cells to assess risk-based patient selection for targeted treatment.

PubMed

Bliss, Sarah A; Paul, Sunirmal; Pobiarzyn, Piotr W; Ayer, Seda; Sinha, Garima; Pant, Saumya; Hilton, Holly; Sharma, Neha; Cunha, Maria F; Engelberth, Daniel J; Greco, Steven J; Bryan, Margarette; Kucia, Magdalena J; Kakar, Sham S; Ratajczak, Mariusz Z; Rameshwar, Pranela

2018-01-10

This study proposes that a novel developmental hierarchy of breast cancer (BC) cells (BCCs) could predict treatment response and outcome. The continued challenge to treat BC requires stratification of BCCs into distinct subsets. This would provide insights on how BCCs evade treatment and adapt dormancy for decades. We selected three subsets, based on the relative expression of octamer-binding transcription factor 4 A (Oct4A) and then analysed each with Affymetrix gene chip. Oct4A is a stem cell gene and would separate subsets based on maturation. Data analyses and gene validation identified three membrane proteins, TMEM98, GPR64 and FAT4. BCCs from cell lines and blood from BC patients were analysed for these three membrane proteins by flow cytometry, along with known markers of cancer stem cells (CSCs), CD44, CD24 and Oct4, aldehyde dehydrogenase 1 (ALDH1) activity and telomere length. A novel working hierarchy of BCCs was established with the most immature subset as CSCs. This group was further subdivided into long- and short-term CSCs. Analyses of 20 post-treatment blood indicated that circulating CSCs and early BC progenitors may be associated with recurrence or early death. These results suggest that the novel hierarchy may predict treatment response and prognosis.

Microbiota of the Small Intestine Is Selectively Engulfed by Phagocytes of the Lamina Propria and Peyer’s Patches

PubMed Central

Morikawa, Masatoshi; Tsujibe, Satoshi; Kiyoshima-Shibata, Junko; Watanabe, Yohei; Kato-Nagaoka, Noriko; Shida, Kan; Matsumoto, Satoshi

2016-01-01

Phagocytes such as dendritic cells and macrophages, which are distributed in the small intestinal mucosa, play a crucial role in maintaining mucosal homeostasis by sampling the luminal gut microbiota. However, there is limited information regarding microbial uptake in a steady state. We investigated the composition of murine gut microbiota that is engulfed by phagocytes of specific subsets in the small intestinal lamina propria (SILP) and Peyer’s patches (PP). Analysis of bacterial 16S rRNA gene amplicon sequences revealed that: 1) all the phagocyte subsets in the SILP primarily engulfed Lactobacillus (the most abundant microbe in the small intestine), whereas CD11bhi and CD11bhiCD11chi cell subsets in PP mostly engulfed segmented filamentous bacteria (indigenous bacteria in rodents that are reported to adhere to intestinal epithelial cells); and 2) among the Lactobacillus species engulfed by the SILP cell subsets, L. murinus was engulfed more frequently than L. taiwanensis, although both these Lactobacillus species were abundant in the small intestine under physiological conditions. These results suggest that small intestinal microbiota is selectively engulfed by phagocytes that localize in the adjacent intestinal mucosa in a steady state. These observations may provide insight into the crucial role of phagocytes in immune surveillance of the small intestinal mucosa. PMID:27701454

Microbiota of the Small Intestine Is Selectively Engulfed by Phagocytes of the Lamina Propria and Peyer's Patches.

PubMed

Morikawa, Masatoshi; Tsujibe, Satoshi; Kiyoshima-Shibata, Junko; Watanabe, Yohei; Kato-Nagaoka, Noriko; Shida, Kan; Matsumoto, Satoshi

2016-01-01

Phagocytes such as dendritic cells and macrophages, which are distributed in the small intestinal mucosa, play a crucial role in maintaining mucosal homeostasis by sampling the luminal gut microbiota. However, there is limited information regarding microbial uptake in a steady state. We investigated the composition of murine gut microbiota that is engulfed by phagocytes of specific subsets in the small intestinal lamina propria (SILP) and Peyer's patches (PP). Analysis of bacterial 16S rRNA gene amplicon sequences revealed that: 1) all the phagocyte subsets in the SILP primarily engulfed Lactobacillus (the most abundant microbe in the small intestine), whereas CD11bhi and CD11bhiCD11chi cell subsets in PP mostly engulfed segmented filamentous bacteria (indigenous bacteria in rodents that are reported to adhere to intestinal epithelial cells); and 2) among the Lactobacillus species engulfed by the SILP cell subsets, L. murinus was engulfed more frequently than L. taiwanensis, although both these Lactobacillus species were abundant in the small intestine under physiological conditions. These results suggest that small intestinal microbiota is selectively engulfed by phagocytes that localize in the adjacent intestinal mucosa in a steady state. These observations may provide insight into the crucial role of phagocytes in immune surveillance of the small intestinal mucosa.

NKT Cell Subsets Can Exert Opposing Effects in Autoimmunity, Tumor Surveillance and Inflammation

PubMed Central

Viale, Rachael; Ware, Randle; Maricic, Igor; Chaturvedi, Varun; Kumar, Vipin

2014-01-01

The innate-like natural killer T (NKT) cells are essential regulators of immunity. These cells comprise at least two distinct subsets and recognize different lipid antigens presented by the MHC class I like molecules CD1d. The CD1d-dependent recognition pathway of NKT cells is highly conserved from mouse to humans. While most type I NKT cells can recognize αGalCer and express a semi-invariant T cell receptor (TCR), a major population of type II NKT cells reactive to sulfatide utilizes an oligoclonal TCR. Furthermore TCR recognition features of NKT subsets are also distinctive with almost parallel as opposed to perpendicular footprints on the CD1d molecules for the type I and type II NKT cells respectively. Here we present a view based upon the recent studies in different clinical and experimental settings that while type I NKT cells are more often pathogenic, they may also be regulatory. On the other hand, sulfatide-reactive type II NKT cells mostly play an inhibitory role in the control of autoimmune and inflammatory diseases. Since the activity and cytokine secretion profiles of NKT cell subsets can be modulated differently by lipid ligands or their analogs, novel immunotherapeutic strategies are being developed for their differential activation for potential intervention in inflammatory diseases. PMID:25288922

Nanolaminate microfluidic device for mobility selection of particles

DOEpatents

Surh, Michael P [Livermore, CA; Wilson, William D [Pleasanton, CA; Barbee, Jr., Troy W.; Lane, Stephen M [Oakland, CA

2006-10-10

A microfluidic device made from nanolaminate materials that are capable of electrophoretic selection of particles on the basis of their mobility. Nanolaminate materials are generally alternating layers of two materials (one conducting, one insulating) that are made by sputter coating a flat substrate with a large number of layers. Specific subsets of the conducting layers are coupled together to form a single, extended electrode, interleaved with other similar electrodes. Thereby, the subsets of conducting layers may be dynamically charged to create time-dependent potential fields that can trap or transport charge colloidal particles. The addition of time-dependence is applicable to all geometries of nanolaminate electrophoretic and electrochemical designs from sinusoidal to nearly step-like.

Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures

PubMed Central

Natsoulis, Georges; El Ghaoui, Laurent; Lanckriet, Gert R.G.; Tolley, Alexander M.; Leroy, Fabrice; Dunlea, Shane; Eynon, Barrett P.; Pearson, Cecelia I.; Tugendreich, Stuart; Jarnagin, Kurt

2005-01-01

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as “rewards” for the class-of-interest) while others have a negative contribution (act as “penalties”) to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class. PMID:15867433

Organic mixed conductors for bioelectronic applications (Conference Presentation)

NASA Astrophysics Data System (ADS)

Rivnay, Jonathan

2016-09-01

Direct measurement and stimulation of electrophysiological activity is a staple of neural and cardiac health monitoring, diagnosis and/or therapy. The ability to sensitively detect these signals can be enhanced by organic electronic materials that show mixed conduction properties (both electronic and ionic transport) in order to bridge the inherent mismatch that is prevalent between biological systems and traditional microelectronic materials/devices. Organic electrochemical transistors (OECTs) are one class of devices that utilize organic mixed conductors as the transistor channel, and have shown considerable promise as amplifying transducers due to their stability in aqueous conditions and high transconductance. These devices are fabricated in flexible, conformable form factors for in vivo recordings of epileptic activity, and for cutaneous EEG and ECG recordings in human subjects. The majority of high performance devices are based on conducting polymers such as poly(3,4-ethylenedioxythiophene) :poly(styrenesulfonate), PEDOT:PSS. By investigating PEDOT-based materials and devices, we are able to construct design rules for new formulations/materials. Introducing glycolated side chains to carefully selected semiconducting polymer backbones has enabled a new class high performance bioelectronic materials that feature high volumetric capacitance, transconductance >10mS (device dimensions ca. 10um), and steep subthreshold switching characteristics. A sub-set of these materials outperform PEDOT:PSS and shows significant promise for low power in vitro and in vivo biosensing applications.

Characterization of Earth as an exoplanet on the basis of VIRTIS-Venus Express data analysis.

NASA Astrophysics Data System (ADS)

Oliva, Fabrizio; Piccioni, Giuseppe; D'Aversa, Emiliano; Bellucci, Giancarlo; Sindoni, Giuseppe; Grassi, Davide; Filacchione, Gianrico; Tosi, Federico; Capaccioni, Fabrizio

2017-04-01

The Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS, Piccioni et al., 2007) on board the Venus Express spacecraft observed the planet Earth several times in the course of the mission. In particular, a subset of 48 observations has been taken from a distance at which our planet is imaged at sub-pixel size, as exoplanets are observed using current technologies. We studied this full subset to understand which spectral signatures, related to different surface and cloud types, can be identified from the integrated planet spectrum. As expected, we found that the cloud coverage has a key role in the identification of surface features and that vegetation is very difficult to be detected. To validate our results we built a simple tool capable to simulate observations of an Earth-like planet as seen from a VIRTIS-like spectrometer in the 0.3 - 5.0 μm range. The illumination and viewing geometries, along with the spectrometer instantaneous field of view and spectral grid and sampling, can be defined by the user. The spectral endmembers used to generate the planet have been selected from an observation of Earth registered from the instrument VIRTIS on board the ESA mission Rosetta, with similar characteristics, during the third flyby of the spacecraft around our planet, occurred in November 2009. Hence, we simulated planets made of: vegetation, desert, ocean, water ice clouds and liquid water clouds. Using different amounts for each spectral class we inferred the percentages that are required to identify each class when all the spectral information is integrated into a single pixel. The outcome of this analysis confirms that clouds are not a negligible issue in the research for spectral signatures, in particular those related to the habitability of a planet and its climate conditions, even when the cloud coverage is not so high. Acknowledgements: This study has been performed within the WOW project financed by INAF and thanks to the support from the Italian Space Agency to VIRTIS Venus Express and Rosetta. References Piccioni, G., et al., 2007. VIRTIS: The Visible and Infrared Thermal Imaging Spectrometer. ESA Special Publication, SP-1295, 1-27.

Offsite radiological consequence analysis for the bounding flammable gas accident

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

CARRO, C.A.

2003-03-19

The purpose of this analysis is to calculate the offsite radiological consequence of the bounding flammable gas accident. DOE-STD-3009-94, ''Preparation Guide for U.S. Department of Energy Nonreactor Nuclear Facility Documented Safety Analyses'', requires the formal quantification of a limited subset of accidents representing a complete set of bounding conditions. The results of these analyses are then evaluated to determine if they challenge the DOE-STD-3009-94, Appendix A, ''Evaluation Guideline,'' of 25 rem total effective dose equivalent in order to identify and evaluate safety class structures, systems, and components. The bounding flammable gas accident is a detonation in a single-shell tank (SST).more » A detonation versus a deflagration was selected for analysis because the faster flame speed of a detonation can potentially result in a larger release of respirable material. As will be shown, the consequences of a detonation in either an SST or a double-shell tank (DST) are approximately equal. A detonation in an SST was selected as the bounding condition because the estimated respirable release masses are the same and because the doses per unit quantity of waste inhaled are generally greater for SSTs than for DSTs. Appendix A contains a DST analysis for comparison purposes.« less

Selecting sequence variants to improve genomic predictions for dairy cattle

USDA-ARS?s Scientific Manuscript database

Millions of genetic variants have been identified by population-scale sequencing projects, but subsets are needed for routine genomic predictions or to include on genotyping arrays. Methods of selecting sequence variants were compared using both simulated sequence genotypes and actual data from run ...

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Eradication of melanomas by targeted elimination of a minor subset of tumor cells

PubMed Central

Schmidt, Patrick; Kopecky, Caroline; Hombach, Andreas; Zigrino, Paola; Mauch, Cornelia; Abken, Hinrich

2011-01-01

Proceeding on the assumption that all cancer cells have equal malignant capacities, current regimens in cancer therapy attempt to eradicate all malignant cells of a tumor lesion. Using in vivo targeting of tumor cell subsets, we demonstrate that selective elimination of a definite, minor tumor cell subpopulation is particularly effective in eradicating established melanoma lesions irrespective of the bulk of cancer cells. Tumor cell subsets were specifically eliminated in a tumor lesion by adoptive transfer of engineered cytotoxic T cells redirected in an antigen-restricted manner via a chimeric antigen receptor. Targeted elimination of less than 2% of the tumor cells that coexpress high molecular weight melanoma-associated antigen (HMW-MAA) (melanoma-associated chondroitin sulfate proteoglycan, MCSP) and CD20 lastingly eradicated melanoma lesions, whereas targeting of any random 10% tumor cell subset was not effective. Our data challenge the biological therapy and current drug development paradigms in the treatment of cancer. PMID:21282657

Minimal ensemble based on subset selection using ECG to diagnose categories of CAN.

PubMed

Abawajy, Jemal; Kelarev, Andrei; Yi, Xun; Jelinek, Herbert F

2018-07-01

Early diagnosis of cardiac autonomic neuropathy (CAN) is critical for reversing or decreasing its progression and prevent complications. Diagnostic accuracy or precision is one of the core requirements of CAN detection. As the standard Ewing battery tests suffer from a number of shortcomings, research in automating and improving the early detection of CAN has recently received serious attention in identifying additional clinical variables and designing advanced ensembles of classifiers to improve the accuracy or precision of CAN diagnostics. Although large ensembles are commonly proposed for the automated diagnosis of CAN, large ensembles are characterized by slow processing speed and computational complexity. This paper applies ECG features and proposes a new ensemble-based approach for diagnosis of CAN progression. We introduce a Minimal Ensemble Based On Subset Selection (MEBOSS) for the diagnosis of all categories of CAN including early, definite and atypical CAN. MEBOSS is based on a novel multi-tier architecture applying classifier subset selection as well as the training subset selection during several steps of its operation. Our experiments determined the diagnostic accuracy or precision obtained in 5 × 2 cross-validation for various options employed in MEBOSS and other classification systems. The experiments demonstrate the operation of the MEBOSS procedure invoking the most effective classifiers available in the open source software environment SageMath. The results of our experiments show that for the large DiabHealth database of CAN related parameters MEBOSS outperformed other classification systems available in SageMath and achieved 94% to 97% precision in 5 × 2 cross-validation correctly distinguishing any two CAN categories to a maximum of five categorizations including control, early, definite, severe and atypical CAN. These results show that MEBOSS architecture is effective and can be recommended for practical implementations in systems for the diagnosis of CAN progression. Copyright © 2018 Elsevier B.V. All rights reserved.

Selective skin sensitivity changes and sensory reweighting following short-duration space flight.

PubMed

Lowrey, Catherine R; Perry, Stephen D; Strzalkowski, Nicholas D J; Williams, David R; Wood, Scott J; Bent, Leah R

2014-03-15

Skin sensory input from the foot soles is coupled with vestibular input to facilitate body orientation in a gravitational environment. Anecdotal observations suggest that foot sole skin becomes hypersensitive following space flight. The veritable level of skin sensitivity and its impact on postural disequilibrium observed post space flight have not been documented. Skin sensitivity of astronauts (n = 11) was measured as vibration perception at the great toe, fifth metatarsal and heel. Frequencies targeted four classes of receptors: 3 and 25 Hz for slow-adapting (SA) receptors and 60 and 250 Hz for fast-adapting (FA) receptors. Data were collected pre- and post-space flight. We hypothesized that skin sensitivity would increase post-space flight and correlate to balance measures. Decreased skin sensitivity was found on landing day at 3 and 25 Hz on the great toe. Hypersensitivity was found for a subset of astronauts (n = 6) with significantly increased sensitivity to 250 Hz at the heel. This subset displayed a greater reduction in computerized dynamic posturography (CDP) equilibrium (EQ) scores (-54%) on landing vs. non-hypersensitive participants (-11%). Observed hyposensitivity of SA (pressure) receptors may indicate a strategy to reduce pressure input during periods of unloading. Hypersensitivity of FAs coupled with reduced EQ scores may reflect targeted sensory reweighting. Altered gravito-inertial environments reduce vestibular function in balance control which may trigger increased weighting of FAs (that signal foot contact, slips). Understanding modulations to skin sensitivity has translational implications for mitigating postural disequilibrium following space flight and for on-Earth preventative strategies for imbalance in older adults.

Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (Corchorus capsularis) under stress treatments

PubMed Central

Niu, Xiaoping; Qi, Jianmin; Zhang, Gaoyang; Xu, Jiantang; Tao, Aifen; Fang, Pingping; Su, Jianguang

2015-01-01

To accurately measure gene expression using quantitative reverse transcription PCR (qRT-PCR), reliable reference gene(s) are required for data normalization. Corchorus capsularis, an annual herbaceous fiber crop with predominant biodegradability and renewability, has not been investigated for the stability of reference genes with qRT-PCR. In this study, 11 candidate reference genes were selected and their expression levels were assessed using qRT-PCR. To account for the influence of experimental approach and tissue type, 22 different jute samples were selected from abiotic and biotic stress conditions as well as three different tissue types. The stability of the candidate reference genes was evaluated using geNorm, NormFinder, and BestKeeper programs, and the comprehensive rankings of gene stability were generated by aggregate analysis. For the biotic stress and NaCl stress subsets, ACT7 and RAN were suitable as stable reference genes for gene expression normalization. For the PEG stress subset, UBC, and DnaJ were sufficient for accurate normalization. For the tissues subset, four reference genes TUBβ, UBI, EF1α, and RAN were sufficient for accurate normalization. The selected genes were further validated by comparing expression profiles of WRKY15 in various samples, and two stable reference genes were recommended for accurate normalization of qRT-PCR data. Our results provide researchers with appropriate reference genes for qRT-PCR in C. capsularis, and will facilitate gene expression study under these conditions. PMID:26528312

Understanding the Biology of Antigen Cross-Presentation for the Design of Vaccines Against Cancer

PubMed Central

Fehres, Cynthia M.; Unger, Wendy W. J.; Garcia-Vallejo, Juan J.; van Kooyk, Yvette

2014-01-01

Antigen cross-presentation, the process in which exogenous antigens are presented on MHC class I molecules, is crucial for the generation of effector CD8+ T cell responses. Although multiple cell types are being described to be able to cross-present antigens, in vivo this task is mainly carried out by certain subsets of dendritic cells (DCs). Aspects such as the internalization route, the pathway of endocytic trafficking, and the simultaneous activation through pattern-recognition receptors have a determining influence in how antigens are handled for cross-presentation by DCs. In this review, we will summarize new insights in factors that affect antigen cross-presentation of human DC subsets, and we will discuss the possibilities to exploit antigen cross-presentation for immunotherapy against cancer. PMID:24782858

Mining nutrigenetics patterns related to obesity: use of parallel multifactor dimensionality reduction.

PubMed

Karayianni, Katerina N; Grimaldi, Keith A; Nikita, Konstantina S; Valavanis, Ioannis K

2015-01-01

This paper aims to enlighten the complex etiology beneath obesity by analysing data from a large nutrigenetics study, in which nutritional and genetic factors associated with obesity were recorded for around two thousand individuals. In our previous work, these data have been analysed using artificial neural network methods, which identified optimised subsets of factors to predict one's obesity status. These methods did not reveal though how the selected factors interact with each other in the obtained predictive models. For that reason, parallel Multifactor Dimensionality Reduction (pMDR) was used here to further analyse the pre-selected subsets of nutrigenetic factors. Within pMDR, predictive models using up to eight factors were constructed, further reducing the input dimensionality, while rules describing the interactive effects of the selected factors were derived. In this way, it was possible to identify specific genetic variations and their interactive effects with particular nutritional factors, which are now under further study.

Optimisation algorithms for ECG data compression.

PubMed

Haugland, D; Heber, J G; Husøy, J H

1997-07-01

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.

pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery.

PubMed

Varma, Manthena V; Gardner, Iain; Steyn, Stefanus J; Nkansah, Paul; Rotter, Charles J; Whitney-Pickett, Carrie; Zhang, Hui; Di, Li; Cram, Michael; Fenner, Katherine S; El-Kattan, Ayman F

2012-05-07

The Biopharmaceutics Classification System (BCS) is a scientific framework that provides a basis for predicting the oral absorption of drugs. These concepts have been extended in the Biopharmaceutics Drug Disposition Classification System (BDDCS) to explain the potential mechanism of drug clearance and understand the effects of uptake and efflux transporters on absorption, distribution, metabolism, and elimination. The objective of present work is to establish criteria for provisional biopharmaceutics classification using pH-dependent passive permeability and aqueous solubility data generated from high throughput screening methodologies in drug discovery settings. The apparent permeability across monolayers of clonal cell line of Madin-Darby canine kidney cells, selected for low endogenous efflux transporter expression, was measured for a set of 105 drugs, with known BCS and BDDCS class. The permeability at apical pH 6.5 for acidic drugs and at pH 7.4 for nonacidic drugs showed a good correlation with the fraction absorbed in human (Fa). Receiver operating characteristic (ROC) curve analysis was utilized to define the permeability class boundary. At permeability ≥ 5 × 10(-6) cm/s, the accuracy of predicting Fa of ≥ 0.90 was 87%. Also, this cutoff showed more than 80% sensitivity and specificity in predicting the literature permeability classes (BCS), and the metabolism classes (BDDCS). The equilibrium solubility of a subset of 49 drugs was measured in pH 1.2 medium, pH 6.5 phosphate buffer, and in FaSSIF medium (pH 6.5). Although dose was not considered, good concordance of the measured solubility with BCS and BDDCS solubility class was achieved, when solubility at pH 1.2 was used for acidic compounds and FaSSIF solubility was used for basic, neutral, and zwitterionic compounds. Using a cutoff of 200 μg/mL, the data set suggested a 93% sensitivity and 86% specificity in predicting both the BCS and BDDCS solubility classes. In conclusion, this study identified pH-dependent permeability and solubility criteria that can be used to assign provisional biopharmaceutics class at early stage of the drug discovery process. Additionally, such a classification system will enable discovery scientists to assess the potential limiting factors to oral absorption, as well as help predict the drug disposition mechanisms and potential drug-drug interactions.

Strategies to predict and improve eating quality of cooked beef using carcass and meat composition traits in Angus cattle.

PubMed

Mateescu, R G; Oltenacu, P A; Garmyn, A J; Mafi, G G; VanOverbeke, D L

2016-05-01

Product quality is a high priority for the beef industry because of its importance as a major driver of consumer demand for beef and the ability of the industry to improve it. A 2-prong approach based on implementation of a genetic program to improve eating quality and a system to communicate eating quality and increase the probability that consumers' eating quality expectations are met is outlined. The objectives of this study were 1) to identify the best carcass and meat composition traits to be used in a selection program to improve eating quality and 2) to develop a relatively small number of classes that reflect real and perceptible differences in eating quality that can be communicated to consumers and identify a subset of carcass and meat composition traits with the highest predictive accuracy across all eating quality classes. Carcass traits, meat composition, including Warner-Bratzler shear force (WBSF), intramuscular fat content (IMFC), trained sensory panel scores, and mineral composition traits of 1,666 Angus cattle were used in this study. Three eating quality indexes, EATQ1, EATQ2, and EATQ3, were generated by using different weights for the sensory traits (emphasis on tenderness, flavor, and juiciness, respectively). The best model for predicting eating quality explained 37%, 9%, and 19% of the variability of EATQ1, EATQ2, and EATQ3, and 2 traits, WBSF and IMFC, accounted for most of the variability explained by the best models. EATQ1 combines tenderness, juiciness, and flavor assessed by trained panels with 0.60, 0.15, and 0.25 weights, best describes North American consumers, and has a moderate heritability (0.18 ± 0.06). A selection index (I= -0.5[WBSF] + 0.3[IMFC]) based on phenotypic and genetic variances and covariances can be used to improve eating quality as a correlated trait. The 3 indexes (EATQ1, EATQ2, and EATQ3) were used to generate 3 equal (33.3%) low, medium, and high eating quality classes, and linear combinations of traits that best predict class membership were estimated using a predictive discriminant analysis. The best predictive model to classify new observations into low, medium, and high eating quality classes defined by the EATQ1 index included WBSF, IMFC, HCW, and marbling score and resulted in a total error rate of 47.06%, much lower than the 60.74% error rate when the prediction of class membership was based on the USDA grading system. The 2 best predictors were WBSF and IMFC, and they accounted for 97.2% of the variability explained by the best model.

Heteroresistance at the single-cell level: adapting to antibiotic stress through a population-based strategy and growth-controlled interphenotypic coordination.

PubMed

Wang, Xiaorong; Kang, Yu; Luo, Chunxiong; Zhao, Tong; Liu, Lin; Jiang, Xiangdan; Fu, Rongrong; An, Shuchang; Chen, Jichao; Jiang, Ning; Ren, Lufeng; Wang, Qi; Baillie, J Kenneth; Gao, Zhancheng; Yu, Jun

2014-02-11

Heteroresistance refers to phenotypic heterogeneity of microbial clonal populations under antibiotic stress, and it has been thought to be an allocation of a subset of "resistant" cells for surviving in higher concentrations of antibiotic. The assumption fits the so-called bet-hedging strategy, where a bacterial population "hedges" its "bet" on different phenotypes to be selected by unpredicted environment stresses. To test this hypothesis, we constructed a heteroresistance model by introducing a blaCTX-M-14 gene (coding for a cephalosporin hydrolase) into a sensitive Escherichia coli strain. We confirmed heteroresistance in this clone and that a subset of the cells expressed more hydrolase and formed more colonies in the presence of ceftriaxone (exhibited stronger "resistance"). However, subsequent single-cell-level investigation by using a microfluidic device showed that a subset of cells with a distinguishable phenotype of slowed growth and intensified hydrolase expression emerged, and they were not positively selected but increased their proportion in the population with ascending antibiotic concentrations. Therefore, heteroresistance--the gradually decreased colony-forming capability in the presence of antibiotic--was a result of a decreased growth rate rather than of selection for resistant cells. Using a mock strain without the resistance gene, we further demonstrated the existence of two nested growth-centric feedback loops that control the expression of the hydrolase and maximize population growth in various antibiotic concentrations. In conclusion, phenotypic heterogeneity is a population-based strategy beneficial for bacterial survival and propagation through task allocation and interphenotypic collaboration, and the growth rate provides a critical control for the expression of stress-related genes and an essential mechanism in responding to environmental stresses. Heteroresistance is essentially phenotypic heterogeneity, where a population-based strategy is thought to be at work, being assumed to be variable cell-to-cell resistance to be selected under antibiotic stress. Exact mechanisms of heteroresistance and its roles in adaptation to antibiotic stress have yet to be fully understood at the molecular and single-cell levels. In our study, we have not been able to detect any apparent subset of "resistant" cells selected by antibiotics; on the contrary, cell populations differentiate into phenotypic subsets with variable growth statuses and hydrolase expression. The growth rate appears to be sensitive to stress intensity and plays a key role in controlling hydrolase expression at both the bulk population and single-cell levels. We have shown here, for the first time, that phenotypic heterogeneity can be beneficial to a growing bacterial population through task allocation and interphenotypic collaboration other than partitioning cells into different categories of selective advantage.

The Atacama Cosmology Telescope: Physical Properties and Purity of a Galaxy Cluster Sample Selected Via the Sunyaev-Zel'Dovich Effect

NASA Technical Reports Server (NTRS)

Menanteau, Felipe; Gonzalez, Jorge; Juin, Jean-Baptiste; Marriage, Tobias; Reese, Erik D.; Acquaviva, Viviana; Aguirre, Paula; Appel, John Willam; Baker, Andrew J.; Barrientos, L. Felipe;

Label-aligned Multi-task Feature Learning for Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zu, Chen; Jie, Biao; Liu, Mingxia; Chen, Songcan

2015-01-01

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. PMID:26572145

Characterization of the CD4+ and CD8+ tumor infiltrating lymphocytes propagated with bispecific monoclonal antibodies.

PubMed

Wong, J T; Pinto, C E; Gifford, J D; Kurnick, J T; Kradin, R L

1989-11-15

To study the CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) in the antitumor response, we propagated these subsets directly from tumor tissues with anti-CD3:anti-CD8 (CD3,8) and anti-CD3:anti-CD4 (CD3,4) bispecific mAb (BSMAB). CD3,8 BSMAB cause selective cytolysis of CD8+ lymphocytes by bridging the CD8 molecules of target lymphocytes to the CD3 molecular complex of cytolytic T lymphocytes with concurrent activation and proliferation of residual CD3+CD4+ T lymphocytes. Similarly, CD3,4 BSMAB cause selective lysis of CD4+ lymphocytes whereas concurrently activating the residual CD3+CD8+ T cells. Small tumor fragments from four malignant melanoma and three renal cell carcinoma patients were cultured in medium containing CD3,8 + IL-2, CD3,4 + IL-2, or IL-2 alone. CD3,8 led to selective propagation of the CD4+ TIL whereas CD3,4 led to selective propagation of the CD8+ TIL from each of the tumors. The phenotypes of the TIL subset cultures were generally stable when assayed over a 1 to 3 months period and after further expansion with anti-CD3 mAb or lectins. Specific 51Cr release of labeled target cells that were bridged to the CD3 molecular complexes of TIL suggested that both CD4+ and CD8+ TIL cultures have the capacity of mediating cytolysis via their Ti/CD3 TCR complexes. In addition, both CD4+ and CD8+ TIL cultures from most patients caused substantial (greater than 20%) lysis of the NK-sensitive K562 cell line. The majority of CD4+ but not CD8+ TIL cultures also produced substantial lysis of the NK-resistant Daudi cell line. Lysis of the autologous tumor by the TIL subsets was assessed in two patients with malignant melanoma. The CD8+ TIL from one tumor demonstrated cytotoxic activity against the autologous tumor but negligible lysis of allogeneic melanoma targets. In conclusion, immunocompetent CD4+ and CD8+ TIL subsets can be isolated and expanded directly from small tumor fragments of malignant melanoma and renal cell carcinoma using BSMAB. The resultant TIL subsets can be further expanded for detailed studies or for adoptive immunotherapy.

Targeting a Common Collaborator in Cancer Development

PubMed Central

Myers, Andrea P.; Cantley, Lewis C.

2012-01-01

In this issue of Science Translational Medicine, Wallin et al. have identified a subset of breast and ovarian cancer cell lines that show synergistic response to the combination of doxorubicin and GDC-0941, a class IA phosphatidylinositol 3-kinase (PI3K) inhibitor. Here, we discuss the potential implications of these data on the clinical development of PI3K pathway inhibitors as cancer therapeutics. PMID:20826838

Building a Learning Progression for Celestial Motion: An Exploration of Students' Reasoning about the Seasons

ERIC Educational Resources Information Center

Plummer, Julia D.; Maynard, L.

2014-01-01

We present the development of a construct map addressing the reason for the seasons, as a subset of a larger learning progression on celestial motion. Five classes of 8th grade students (N?=?38) participated in a 10-day curriculum on the seasons. We revised a hypothetical seasons construct map using a Rasch model analysis of students'…

Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME).

PubMed

Badia, Jordi; Raspopovic, Stanisa; Carpaneto, Jacopo; Micera, Silvestro; Navarro, Xavier

2016-01-01

The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number of nerve fascicles with the least invasiveness and potential damage to the nerve. The transverse intrafascicular multichannel electrode (TIME), transversally inserted in the peripheral nerve, has been shown to be useful for the selective activation of subsets of axons, both at inter- and intra-fascicular levels, in the small sciatic nerve of the rat. In this study we assessed the capabilities of TIME for the selective recording of neural activity, considering the topographical selectivity and the distinction of neural signals corresponding to different sensory types. Topographical recording selectivity was proved by the differential recording of CNAPs from different subsets of nerve fibers, such as those innervating toes 2 and 4 of the hindpaw of the rat. Neural signals elicited by sensory stimuli applied to the rat paw were successfully recorded. Signal processing allowed distinguishing three different types of sensory stimuli such as tactile, proprioceptive and nociceptive ones with high performance. These findings further support the suitability of TIMEs for neuroprosthetic applications, by exploiting the transversal topographical structure of the peripheral nerves.

Is there a differential strength of specific HLA mismatches in kidney transplants?

PubMed

Sasaki, N; Idica, A; Terasaki, P

2008-05-01

In this article we attempted to identify whether there is a specific mismatched antigen that might be detrimental to kidney transplant outcome. The frequency of function versus failure of transplant cases was tallied within subpopulations among a subset of the 2006 United Network for Organ Sharing transplant dataset. We examined 7998 cadaveric and 11,420 living donor kidney transplants that were mismatched for a single class I antigen. When tested by five different criteria, the results were relatively similar for the HLA class I, A- and B-locus mismatches. HLA A1 was identified as the single most dominant immunogenic mismatch. However, when the P values were multiplied by 68, the number of comparisons, A1 was only marginally significant. We concluded that at least for class I specificities, the 68 specificities were about equal immunogenicity in kidney transplantation.

A Design for a Multi-Use Object Editor with Connections

DTIC Science & Technology

1991-10-01

Variables of Class Select 14 3.6.2 Methods of Class Select 14 3.6.3 Application States 14 3.7 Class Clipboard 15 3.8 Class Crestore 15 3.9 Conclusions 15 4...this is not directly related to cutting and past- ing between applications. 3.8 CLASS CRESTORE Class crestore holds a connection until all the

Solar System constraints on massless scalar-tensor gravity with positive coupling constant upon cosmological evolution of the scalar field

NASA Astrophysics Data System (ADS)

Anderson, David; Yunes, Nicolás

2017-09-01

Scalar-tensor theories of gravity modify general relativity by introducing a scalar field that couples nonminimally to the metric tensor, while satisfying the weak-equivalence principle. These theories are interesting because they have the potential to simultaneously suppress modifications to Einstein's theory on Solar System scales, while introducing large deviations in the strong field of neutron stars. Scalar-tensor theories can be classified through the choice of conformal factor, a scalar that regulates the coupling between matter and the metric in the Einstein frame. The class defined by a Gaussian conformal factor with a negative exponent has been studied the most because it leads to spontaneous scalarization (i.e. the sudden activation of the scalar field in neutron stars), which consequently leads to large deviations from general relativity in the strong field. This class, however, has recently been shown to be in conflict with Solar System observations when accounting for the cosmological evolution of the scalar field. We here study whether this remains the case when the exponent of the conformal factor is positive, as well as in another class of theories defined by a hyperbolic conformal factor. We find that in both of these scalar-tensor theories, Solar System tests are passed only in a very small subset of coupling parameter space, for a large set of initial conditions compatible with big bang nucleosynthesis. However, while we find that it is possible for neutron stars to scalarize, one must carefully select the coupling parameter to do so, and even then, the scalar charge is typically 2 orders of magnitude smaller than in the negative-exponent case. Our study suggests that future work on scalar-tensor gravity, for example in the context of tests of general relativity with gravitational waves from neutron star binaries, should be carried out within the positive coupling parameter class.

Better physical activity classification using smartphone acceleration sensor.

PubMed

Arif, Muhammad; Bilal, Mohsin; Kattan, Ahmed; Ahamed, S Iqbal

2014-09-01

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

On Some Multiple Decision Problems

DTIC Science & Technology

1976-08-01

parameter space. Some recent results in the area of subset selection formulation are Gnanadesikan and Gupta [28], Gupta and Studden [43], Gupta and...York, pp. 363-376. [27) Gnanadesikan , M. (1966). Some Selection and Ranking Procedures for Multivariate Normal Populations. Ph.D. Thesis. Dept. of...Statist., Purdue Univ., West Lafayette, Indiana 47907. [28) Gnanadesikan , M. and Gupta, S. S. (1970). Selection procedures for multivariate normal

A computer program for fast and easy typing of partial endoglucanase gene sequence into phylotypes and sequevars 1&2 (select agents) of Ralstonia solanacearum

USDA-ARS?s Scientific Manuscript database

The phytopathogen Ralstonia solanacearum is a species complex that contains a subset of strains that are quarantined or select agent pathogens. An unidentified R. solanacearum strain is considered a select agent in the US until proven otherwise, which can be done by phylogenetic analysis of a partia...

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.

PubMed

Wang, Shuaiqun; Aorigele; Kong, Wei; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

PubMed Central

Aorigele; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. PMID:27579323

Choosing "Something Else" as a Sexual Identity: Evaluating Response Options on the National Health Interview Survey.

PubMed

Eliason, Michele J; Streed, Carl G

2017-10-01

Researchers struggle to find effective ways to measure sexual and gender identities to determine whether there are health differences among subsets of the LGBTQ+ population. This study examines responses on the National Health Interview Survey (NHIS) sexual identity questions among 277 LGBTQ+ healthcare providers. Eighteen percent indicated that their sexual identity was "something else" on the first question, and 57% of those also selected "something else" on the second question. Half of the genderqueer/gender variant participants and 100% of transgender-identified participants selected "something else" as their sexual identity. The NHIS question does not allow all respondents in LGBTQ+ populations to be categorized, thus we are potentially missing vital health disparity information about subsets of the LGBTQ+ population.

Classification of natural products as sources of drugs according to the biopharmaceutics drug disposition classification system (BDDCS).

PubMed

Li, Ji; Larregieu, Caroline A; Benet, Leslie Z

2016-12-01

Natural products (NPs) are compounds that are derived from natural sources such as plants, animals, and micro-organisms. Therapeutics has benefited from numerous drug classes derived from natural product sources. The Biopharmaceutics Drug Disposition Classification System (BDDCS) was proposed to serve as a basis for predicting the importance of transporters and enzymes in determining drug bioavailability and disposition. It categorizes drugs into one of four biopharmaceutical classes according to their water solubility and extent of metabolism. The present paper reviews 109 drugs from natural product sources: 29% belong to class 1 (high solubility, extensive metabolism), 22% to class 2 (low solubility, extensive metabolism), 40% to class 3 (high solubility, poor metabolism), and 9% to class 4 (low solubility, poor metabolism). Herein we evaluated the characteristics of NPs in terms of BDDCS class for all 109 drugs as wells as for subsets of NPs drugs derived from plant sources as antibiotics. In the 109 NPs drugs, we compiled 32 drugs from plants, 50% (16) of total in class 1, 22% (7) in class 2 and 28% (9) in class 3, none found in class 4; Meantime, the antibiotics were found 5 (16%) in class 2, 22 (71%) in class 3, and 4 (13%) in class 4; no drug was found in class 1. Based on this classification, we anticipate BDDCS to serve as a useful adjunct in evaluating the potential characteristics of new natural products. Copyright © 2016 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Rodriguez, J. E.; Eiceman, G. A.

2000-01-01

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

Enhancing student retention of prerequisite knowledge through pre-class activities and in-class reinforcement.

PubMed

Taylor, Ann T S; Olofson, Eric L; Novak, Walter R P

2017-03-04

To foster the connection between biochemistry and the supporting prerequisite concepts, a collection of activities that explicitly link general and organic chemistry concepts to biochemistry ideas was written and either assigned as pre-class work or as recitation activities. We assessed student learning gains after using these activities alone, or in combination with regularly-integrated clicker and discussion questions. Learning gains were determined from student performance on pre- and post-tests covering key prerequisite concepts, biochemistry course exams, and student self-evaluation. Long-term retention of the material was assessed using a comprehensive exam given to a subset of the students. Our results show that using the pre-class exercises in combination with integrative questions was effective at improving student performance in both the short and long term. Similar results were obtained at both a large research institution with large class enrollments and at a private liberal arts college with moderate enrollments. © 2016 by The International Union of Biochemistry and Molecular Biology, 45(2):97-104, 2017. © 2016 The International Union of Biochemistry and Molecular Biology.

Distinguishing Different Strategies of Across-Dimension Attentional Selection

ERIC Educational Resources Information Center

Huang, Liqiang; Pashler, Harold

2012-01-01

Selective attention in multidimensional displays has usually been examined using search tasks requiring the detection of a single target. We examined the ability to perceive a spatial structure in multi-item subsets of a display that were defined either conjunctively or disjunctively. Observers saw two adjacent displays and indicated whether the…

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

A new class of ensemble conserving algorithms for approximate quantum dynamics: Theoretical formulation and model problems.

PubMed

Smith, Kyle K G; Poulsen, Jens Aage; Nyman, Gunnar; Rossky, Peter J

2015-06-28

We develop two classes of quasi-classical dynamics that are shown to conserve the initial quantum ensemble when used in combination with the Feynman-Kleinert approximation of the density operator. These dynamics are used to improve the Feynman-Kleinert implementation of the classical Wigner approximation for the evaluation of quantum time correlation functions known as Feynman-Kleinert linearized path-integral. As shown, both classes of dynamics are able to recover the exact classical and high temperature limits of the quantum time correlation function, while a subset is able to recover the exact harmonic limit. A comparison of the approximate quantum time correlation functions obtained from both classes of dynamics is made with the exact results for the challenging model problems of the quartic and double-well potentials. It is found that these dynamics provide a great improvement over the classical Wigner approximation, in which purely classical dynamics are used. In a special case, our first method becomes identical to centroid molecular dynamics.

Validation of use of subsets of teeth when applying the total mouth periodontal score (TMPS) system in dogs.

PubMed

Harvey, Colin E; Laster, Larry; Shofer, Frances S

2012-01-01

A total mouth periodontal score (TMPS) system in dogs has been described previously. Use of buccal and palatal/lingual surfaces of all teeth requires observation and recording of 120 gingivitis scores and 120 periodontitis scores. Although the result is a reliable, repeatable assessment of the extent of periodontal disease in the mouth, observing and recording 240 data points is time-consuming. Using data from a previously reported study of periodontal disease in dogs, correlation analysis was used to determine whether use of any of seven different subsets of teeth can generate TMPS subset gingivitis and periodontitis scores that are highly correlated with TMPS all-site, all-teeth scores. Overall, gingivitis scores were less highly correlated than periodontitis scores. The minimal tooth set with a significant intra-class correlation (> or = 0.9 of means of right and left sides) for both gingivitis scores and attachment loss measurements consisted of the buccal surface of the maxillary third incisor canine, third premolar fourth premolar; and first molar teeth; and, the mandibular canine, third premolar, fourth premolar and first molar teeth on one side (9 teeth, 15 root sites). Use of this subset of teeth, which reduces the number of data points per dog from 240 to 30 for gingivitis and periodontitis at each scoring episode, is recommended when calculating the gingivitis and periodontitis scores using the TMPS system.

A Metacommunity Framework for Enhancing the Effectiveness of Biological Monitoring Strategies

PubMed Central

Roque, Fabio O.; Cottenie, Karl

2012-01-01

Because of inadequate knowledge and funding, the use of biodiversity indicators is often suggested as a way to support management decisions. Consequently, many studies have analyzed the performance of certain groups as indicator taxa. However, in addition to knowing whether certain groups can adequately represent the biodiversity as a whole, we must also know whether they show similar responses to the main structuring processes affecting biodiversity. Here we present an application of the metacommunity framework for evaluating the effectiveness of biodiversity indicators. Although the metacommunity framework has contributed to a better understanding of biodiversity patterns, there is still limited discussion about its implications for conservation and biomonitoring. We evaluated the effectiveness of indicator taxa in representing spatial variation in macroinvertebrate community composition in Atlantic Forest streams, and the processes that drive this variation. We focused on analyzing whether some groups conform to environmental processes and other groups are more influenced by spatial processes, and on how this can help in deciding which indicator group or groups should be used. We showed that a relatively small subset of taxa from the metacommunity would represent 80% of the variation in community composition shown by the entire metacommunity. Moreover, this subset does not have to be composed of predetermined taxonomic groups, but rather can be defined based on random subsets. We also found that some random subsets composed of a small number of genera performed better in responding to major environmental gradients. There were also random subsets that seemed to be affected by spatial processes, which could indicate important historical processes. We were able to integrate in the same theoretical and practical framework, the selection of biodiversity surrogates, indicators of environmental conditions, and more importantly, an explicit integration of environmental and spatial processes into the selection approach. PMID:22937068

EnzML: multi-label prediction of enzyme classes using InterPro signatures

PubMed Central

2012-01-01

Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN). PMID:22533924

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

PubMed

Furlanello, Cesare; Serafini, Maria; Merler, Stefano; Jurman, Giuseppe

2003-11-06

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

Dynamics, integrability and topology for some classes of Kolmogorov Hamiltonian systems in R+4

NASA Astrophysics Data System (ADS)

Llibre, Jaume; Xiao, Dongmei

2017-02-01

In this paper we first give the sufficient and necessary conditions in order that two classes of polynomial Kolmogorov systems in R+4 are Hamiltonian systems. Then we study the integrability of these Hamiltonian systems in the Liouville sense. Finally, we investigate the global dynamics of the completely integrable Lotka-Volterra Hamiltonian systems in R+4. As an application of the invariant subsets of these systems, we obtain topological classifications of the 3-submanifolds in R+4 defined by the hypersurfaces axy + bzw + cx2 y + dxy2 + ez2 w + fzw2 = h, where a , b , c , d , e , f , w and h are real constants.

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

Studying the Role for CD4+ T Cell Subsets in Human Lupus

DTIC Science & Technology

2013-07-01

heterogeneous, ranging from self- originating uric acid , calcium pyrophosphate crystals, choles- terol crystals, ATP, and glucose to environment-derived...Tardivel, and J. Tschopp. 2006. Gout- associated uric acid crystals activate the NALP3 inflammasome. Nature 440: 237–241. 44. Napirei, M., H. Karsunky, B...patterns (PAMPs) commonly found in microorganisms (1, 2). Different classes of PRRs have been identified. These receptors include TLRs, retinoic acid

Exploring the Role of Microbiota in the Limiting of B1 and MZ B-Cell Numbers by Naturally Secreted Immunoglobulins.

PubMed

Mohr, Elodie; Lino, Andreia C

2017-01-01

Immunoglobulins (Igs)-or antibodies (Ab)-are important to combat foreign pathogens but also to the immune system homeostasis. We developed the AID -/- μS -/- mouse model devoid of total soluble Igs and suitable to monitor the role of Igs on immune homeostasis. We used this experimental system to uncover a negative feedback control of marginal zone (MZ) and B1 B cells numbers by naturally secreted Igs. We raised AID -/- μS -/- mice in germ-free conditions demonstrating that this effect of natural secreted Igs is independent of the microbiota. Herein, we provide a comprehensive description of the protocols to establish and use the AID -/- μS -/- mice to study the role of total secreted Igs or of different Ig classes. This study involves Igs injections to AID -/- μS -/- mice or establishment of AID -/- μS -/- mixed bone marrow chimeras that provide a powerful system to study AID -/- μS -/- B cells in the presence of stable concentrations of different Ig classes. While we describe flow cytometric and histological methods to analyze MZ and B1 B cell subsets, AID -/- μS -/- mice can be used to study the effects of natural Igs on other B cell subsets or immune cells.

Functional dissociation in sweet taste receptor neurons between and within taste organs of Drosophila

PubMed Central

Thoma, Vladimiros; Knapek, Stephan; Arai, Shogo; Hartl, Marion; Kohsaka, Hiroshi; Sirigrivatanawong, Pudith; Abe, Ayako; Hashimoto, Koichi; Tanimoto, Hiromu

2016-01-01

Finding food sources is essential for survival. Insects detect nutrients with external taste receptor neurons. Drosophila possesses multiple taste organs that are distributed throughout its body. However, the role of different taste organs in feeding remains poorly understood. By blocking subsets of sweet taste receptor neurons, we show that receptor neurons in the legs are required for immediate sugar choice. Furthermore, we identify two anatomically distinct classes of sweet taste receptor neurons in the leg. The axonal projections of one class terminate in the thoracic ganglia, whereas the other projects directly to the brain. These two classes are functionally distinct: the brain-projecting neurons are involved in feeding initiation, whereas the thoracic ganglia-projecting neurons play a role in sugar-dependent suppression of locomotion. Distinct receptor neurons for the same taste quality may coordinate early appetitive responses, taking advantage of the legs as the first appendages to contact food. PMID:26893070

Transcriptional profiling at whole population and single cell levels reveals somatosensory neuron molecular diversity

PubMed Central

Chiu, Isaac M; Barrett, Lee B; Williams, Erika K; Strochlic, David E; Lee, Seungkyu; Weyer, Andy D; Lou, Shan; Bryman, Gregory S; Roberson, David P; Ghasemlou, Nader; Piccoli, Cara; Ahat, Ezgi; Wang, Victor; Cobos, Enrique J; Stucky, Cheryl L; Ma, Qiufu; Liberles, Stephen D; Woolf, Clifford J

2014-01-01

The somatosensory nervous system is critical for the organism's ability to respond to mechanical, thermal, and nociceptive stimuli. Somatosensory neurons are functionally and anatomically diverse but their molecular profiles are not well-defined. Here, we used transcriptional profiling to analyze the detailed molecular signatures of dorsal root ganglion (DRG) sensory neurons. We used two mouse reporter lines and surface IB4 labeling to purify three major non-overlapping classes of neurons: 1) IB4+SNS-Cre/TdTomato+, 2) IB4−SNS-Cre/TdTomato+, and 3) Parv-Cre/TdTomato+ cells, encompassing the majority of nociceptive, pruriceptive, and proprioceptive neurons. These neurons displayed distinct expression patterns of ion channels, transcription factors, and GPCRs. Highly parallel qRT-PCR analysis of 334 single neurons selected by membership of the three populations demonstrated further diversity, with unbiased clustering analysis identifying six distinct subgroups. These data significantly increase our knowledge of the molecular identities of known DRG populations and uncover potentially novel subsets, revealing the complexity and diversity of those neurons underlying somatosensation. DOI: http://dx.doi.org/10.7554/eLife.04660.001 PMID:25525749

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

[The study on the changes of serum IL- 6, TNF-α and peripheral blood T lymphocyte subsets in the pregnant women during perinatal period].

PubMed

Li, Juan

2011-03-01

To study the change law of serum IL-6, TNF-α and peripheral blood T lymphocyte subsets in the pregnant women during perinatal period. 100 pregnant women in our hospital from November 2009 to October 2010 were selected as research object, and the serum IL-6, TNF-α and peripheral blood T lymphocyte subsets be-fore and at labor onset occurring, after delivery at the first and third day were analyzed and compared. According the study, the serum IL-6 and TNF-aat labor onset occurring were higher than those before labor onset and af-ter delivery at the first and third day , the CD3(+), CD4 (+), CD8(+) and CD4/CD8 decreased first and then increased, all P < 0. 05, there were significant differences. The changes of serum IL-6, TNF-α and peripheral blood T lymphocyte subsets in the pregnant women during perinatal period has a regular pattern, and it is worthy of.

MODIS Interactive Subsetting Tool (MIST)

NASA Astrophysics Data System (ADS)

McAllister, M.; Duerr, R.; Haran, T.; Khalsa, S. S.; Miller, D.

2008-12-01

In response to requests from the user community, NSIDC has teamed with the Oak Ridge National Laboratory Distributive Active Archive Center (ORNL DAAC) and the Moderate Resolution Data Center (MrDC) to provide time series subsets of satellite data covering stations in the Greenland Climate Network (GC-NET) and the International Arctic Systems for Observing the Atmosphere (IASOA) network. To serve these data NSIDC created the MODIS Interactive Subsetting Tool (MIST). MIST works with 7 km by 7 km subset time series of certain Version 5 (V005) MODIS products over GC-Net and IASOA stations. User- selected data are delivered in a text Comma Separated Value (CSV) file format. MIST also provides online analysis capabilities that include generating time series and scatter plots. Currently, MIST is a Beta prototype and NSIDC intends that user requests will drive future development of the tool. The intent of this poster is to introduce MIST to the MODIS data user audience and illustrate some of the online analysis capabilities.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

Natural Selection on Female Life-History Traits in Relation to Socio-Economic Class in Pre-Industrial Human Populations

PubMed Central

Pettay, Jenni E.; Helle, Samuli; Jokela, Jukka; Lummaa, Virpi

2007-01-01

Life-history theory predicts that resource scarcity constrains individual optimal reproductive strategies and shapes the evolution of life-history traits. In species where the inherited structure of social class may lead to consistent resource differences among family lines, between-class variation in resource availability should select for divergence in optimal reproductive strategies. Evaluating this prediction requires information on the phenotypic selection and quantitative genetics of life-history trait variation in relation to individual lifetime access to resources. Here, we show using path analysis how resource availability, measured as the wealth class of the family, affected the opportunity and intensity of phenotypic selection on the key life-history traits of women living in pre-industrial Finland during the 1800s and 1900s. We found the highest opportunity for total selection and the strongest selection on earlier age at first reproduction in women of the poorest wealth class, whereas selection favoured older age at reproductive cessation in mothers of the wealthier classes. We also found clear differences in female life-history traits across wealth classes: the poorest women had the lowest age-specific survival throughout their lives, they started reproduction later, delivered fewer offspring during their lifetime, ceased reproduction younger, had poorer offspring survival to adulthood and, hence, had lower fitness compared to the wealthier women. Our results show that the amount of wealth affected the selection pressure on female life-history in a pre-industrial human population. PMID:17622351

Using Multiple Endmember Spectral Mixture Analysis of MODIS Data for Computing the Fire Potential Index in Southern California

NASA Astrophysics Data System (ADS)

Schneider, P.; Roberts, D. A.

2007-12-01

The Fire Potential Index (FPI) is currently the only operationally used wildfire susceptibility index in the United States that incorporates remote sensing data in addition to meteorological information. Its remote sensing component utilizes relative greenness derived from a NDVI time series as a proxy for computing the ratio of live to dead vegetation. This study investigates the potential of Multiple Endmember Spectral Mixture Analysis (MESMA) as a more direct and physically reasonable way of computing the live ratio and applying it for the computation of the FPI. A time series of 16-day reflectance composites of Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to perform the analysis. Endmember selection for green vegetation (GV), non- photosynthetic vegetation (NPV) and soil was performed in two stages. First, a subset of suitable endmembers was selected from an extensive library of reference and image spectra for each class using Endmember Average Root Mean Square Error (EAR), Minimum Average Spectral Angle (MASA) and a count-based technique. Second, the most appropriate endmembers for the specific data set were selected from the subset by running a series of 2-endmember models on representative images and choosing the ones that modeled the majority of pixels. The final set of endmembers was used for running MESMA on southern California MODIS composites from 2000 to 2006. 3- and 4-endmember models were considered. The best model was chosen on a per-pixel basis according to the minimum root mean square error of the models at each level of complexity. Endmember fractions were normalized by the shade endmember to generate realistic fractions of GV and NPV. In order to validate the MESMA-derived GV fractions they were compared against live ratio estimates from RG. A significant spatial and temporal relationship between both measures was found, indicating that GV fraction has the potential to substitute RG in computing the FPI. To further test this hypothesis the live ratio estimates obtained from MESMA were used to compute daily FPI maps for southern California from 2001 to 2006. A validation with historical wildfire data from the MODIS Active Fire product was carried out over the same time period using logistic regression. Initial results show that MESMA-derived GV fraction can be used successfully for generating FPI maps of southern California.

The transcription factor NRSF contributes to epileptogenesis by selective repression of a subset of target genes

PubMed Central

McClelland, Shawn; Brennan, Gary P; Dubé, Celine; Rajpara, Seeta; Iyer, Shruti; Richichi, Cristina; Bernard, Christophe; Baram, Tallie Z

2014-01-01

The mechanisms generating epileptic neuronal networks following insults such as severe seizures are unknown. We have previously shown that interfering with the function of the neuron-restrictive silencer factor (NRSF/REST), an important transcription factor that influences neuronal phenotype, attenuated development of this disorder. In this study, we found that epilepsy-provoking seizures increased the low NRSF levels in mature hippocampus several fold yet surprisingly, provoked repression of only a subset (∼10%) of potential NRSF target genes. Accordingly, the repressed gene-set was rescued when NRSF binding to chromatin was blocked. Unexpectedly, genes selectively repressed by NRSF had mid-range binding frequencies to the repressor, a property that rendered them sensitive to moderate fluctuations of NRSF levels. Genes selectively regulated by NRSF during epileptogenesis coded for ion channels, receptors, and other crucial contributors to neuronal function. Thus, dynamic, selective regulation of NRSF target genes may play a role in influencing neuronal properties in pathological and physiological contexts. DOI: http://dx.doi.org/10.7554/eLife.01267.001 PMID:25117540

Dense mesh sampling for video-based facial animation

NASA Astrophysics Data System (ADS)

Peszor, Damian; Wojciechowska, Marzena

2016-06-01

The paper describes an approach for selection of feature points on three-dimensional, triangle mesh obtained using various techniques from several video footages. This approach has a dual purpose. First, it allows to minimize the data stored for the purpose of facial animation, so that instead of storing position of each vertex in each frame, one could store only a small subset of vertices for each frame and calculate positions of others based on the subset. Second purpose is to select feature points that could be used for anthropometry-based retargeting of recorded mimicry to another model, with sampling density beyond that which can be achieved using marker-based performance capture techniques. Developed approach was successfully tested on artificial models, models constructed using structured light scanner, and models constructed from video footages using stereophotogrammetry.

Optimized probability sampling of study sites to improve generalizability in a multisite intervention trial.

PubMed

Kraschnewski, Jennifer L; Keyserling, Thomas C; Bangdiwala, Shrikant I; Gizlice, Ziya; Garcia, Beverly A; Johnston, Larry F; Gustafson, Alison; Petrovic, Lindsay; Glasgow, Russell E; Samuel-Hodge, Carmen D

2010-01-01

Studies of type 2 translation, the adaption of evidence-based interventions to real-world settings, should include representative study sites and staff to improve external validity. Sites for such studies are, however, often selected by convenience sampling, which limits generalizability. We used an optimized probability sampling protocol to select an unbiased, representative sample of study sites to prepare for a randomized trial of a weight loss intervention. We invited North Carolina health departments within 200 miles of the research center to participate (N = 81). Of the 43 health departments that were eligible, 30 were interested in participating. To select a representative and feasible sample of 6 health departments that met inclusion criteria, we generated all combinations of 6 from the 30 health departments that were eligible and interested. From the subset of combinations that met inclusion criteria, we selected 1 at random. Of 593,775 possible combinations of 6 counties, 15,177 (3%) met inclusion criteria. Sites in the selected subset were similar to all eligible sites in terms of health department characteristics and county demographics. Optimized probability sampling improved generalizability by ensuring an unbiased and representative sample of study sites.

Fuzzy Subspace Clustering

NASA Astrophysics Data System (ADS)

Borgelt, Christian

In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

Hypergraph Based Feature Selection Technique for Medical Diagnosis.

PubMed

Somu, Nivethitha; Raman, M R Gauthama; Kirthivasan, Kannan; Sriram, V S Shankar

2016-11-01

The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K - Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K - Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.

Human natural killer cell receptors for HLA-class I molecules. Evidence that the Kp43 (CD94) molecule functions as receptor for HLA-B alleles

PubMed Central

1994-01-01

GL183 or EB6 (p58) molecules have been shown to function as receptors for different HLA-C alleles and to deliver an inhibitory signal to natural killer (NK) cells, thus preventing lysis of target cells. In this study, we analyzed a subset of NK cells characterized by a p58- negative surface phenotype. We show that p58-negative clones, although specific for class I molecules do not recognize HLA-C alleles. In addition, by the use of appropriate target cells transfected with different HLA-class I alleles we identified HLA-B7 as the protective element recognized by a fraction of p58-negative clones. In an attempt to identify the receptor molecules expressed by HLA-B7-specific clones, monoclonal antibodies (mAbs) were selected after mice immunization with such clones. Two of these mAbs, termed XA-88 and XA-185, and their F(ab')2 fragments, were found to reconstitute lysis of B7+ target cells by B7-specific NK clones. Both mAbs were shown to be directed against the recently clustered Kp43 molecule (CD94). Thus, mAb-mediated masking of Kp43 molecules interferes with recognition of HLA-B7 and results in target cell lysis. Moreover, in a redirected killing assay, the cross- linking of Kp43 molecules mediated by the XA185 mAb strongly inhibited the cytolytic activity of HLA-B7-specific NK clones, thus mimicking the functional effect of B7 molecules. Taken together, these data strongly suggest that Kp43 molecules function as receptors for HLA-B7 and that this receptor/ligand interaction results in inhibition of the NK- mediated cytolytic activity. Indirect immunofluorescence and FACS analysis of a large number of random NK clones showed that Kp43 molecules (a) were brightly expressed on a subset of p58-negative clones, corresponding to those specific for HLA-B7; (b) displayed a medium/low fluorescence in the p58-negative clones that are not B7- specific as well as in most p58+ NK clones; and (c) were brightly expressed as in the p58+ clone ET34 (GL183-/EB6+, Cw4-specific). Functional analysis revealed that Kp43 functioned as an inhibitory receptor only in NK clones displaying bright fluorescence. These studies also indicate that some NK clones (e.g., the ET34) can coexpress two distinct receptors (p58 and Kp43) for different class I alleles (Cw4 and B7). Finally, we show that Kp43 molecules function as receptors only for some HLA-B alleles and that still undefined receptor(s) must exist for other HLA-B alleles including B27. PMID:8046333

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Characterisation of an epigenetically altered CD4+ CD28+ Kir+ T cell subset in autoimmune rheumatic diseases by multiparameter flow cytometry

PubMed Central

Strickland, Faith M; Patel, Dipak; Somers, Emily; Robida, Aaron M; Pihalja, Michael; Swartz, Richard; Marder, Wendy; Richardson, Bruce

2016-01-01

Objectives Antigen-specific CD4+ T cells epigenetically modified with DNA methylation inhibitors overexpress genes normally suppressed by this mechanism, including CD11a, CD70, CD40L and the KIR gene family. The altered cells become autoreactive, losing restriction for nominal antigen and responding to self-class II major histocompatibility complex (MHC) molecules without added antigen, and are sufficient to cause a lupus-like disease in syngeneic mice. T cells overexpressing the same genes are found in patients with active lupus. Whether these genes are co-overexpressed on the same or different cells is unknown. The goal of this study was to determine whether these genes are overexpressed on the same or different T cells and whether this subset of CD4+ T cells is also present in patients with lupus and other rheumatic diseases. Methods Multicolour flow cytometry was used to compare CD11a, CD70, CD40L and KIR expression on CD3+CD4+CD28+ T cells to their expression on experimentally demethylated CD3+CD4+CD28+ T cells and CD3+CD4+CD28+ T cells from patients with active lupus and other autoimmune diseases. Results Experimentally demethylated CD4+ T cells and T cells from patients with active lupus have a CD3+CD4+CD28+CD11ahiCD70+CD40LhiKIR+ subset, and the subset size is proportional to lupus flare severity. A similar subset is found in patients with other rheumatic diseases including rheumatoid arthritis, systemic sclerosis and Sjögren's syndrome but not retroperitoneal fibrosis. Conclusions Patients with active autoimmune rheumatic diseases have a previously undescribed CD3+CD4+CD28+CD11ahiCD70+CD40LhiKIR+ T cell subset. This subset may play an important role in flares of lupus and related autoimmune rheumatic diseases, provide a biomarker for disease activity and serve as a novel therapeutic target for the treatment of lupus flares. PMID:27099767

Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs

PubMed Central

Gu, Qing; Deng, Jinsong; Wang, Ke; Lin, Yi; Li, Jun; Gan, Muye; Ma, Ligang; Hong, Yang

2014-01-01

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources. PMID:24919129

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

PubMed

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

Identification and assessment of potential water quality impact factors for drinking-water reservoirs.

PubMed

Gu, Qing; Deng, Jinsong; Wang, Ke; Lin, Yi; Li, Jun; Gan, Muye; Ma, Ligang; Hong, Yang

2014-06-10

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources.

Enantioselectivity in Candida antarctica lipase B: A molecular dynamics study

PubMed Central

Raza, Sami; Fransson, Linda; Hult, Karl

2001-01-01

A major problem in predicting the enantioselectivity of an enzyme toward substrate molecules is that even high selectivity toward one substrate enantiomer over the other corresponds to a very small difference in free energy. However, total free energies in enzyme-substrate systems are very large and fluctuate significantly because of general protein motion. Candida antarctica lipase B (CALB), a serine hydrolase, displays enantioselectivity toward secondary alcohols. Here, we present a modeling study where the aim has been to develop a molecular dynamics-based methodology for the prediction of enantioselectivity in CALB. The substrates modeled (seven in total) were 3-methyl-2-butanol with various aliphatic carboxylic acids and also 2-butanol, as well as 3,3-dimethyl-2-butanol with octanoic acid. The tetrahedral reaction intermediate was used as a model of the transition state. Investigative analyses were performed on ensembles of nonminimized structures and focused on the potential energies of a number of subsets within the modeled systems to determine which specific regions are important for the prediction of enantioselectivity. One category of subset was based on atoms that make up the core structural elements of the transition state. We considered that a more favorable energetic conformation of such a subset should relate to a greater likelihood for catalysis to occur, thus reflecting higher selectivity. The results of this study conveyed that the use of this type of subset was viable for the analysis of structural ensembles and yielded good predictions of enantioselectivity. PMID:11266619

Identifying Depressed Older Adults in Primary Care: A Secondary Analysis of a Multisite Randomized Controlled Trial

PubMed Central

Voils, Corrine I.; Olsen, Maren K.; Williams, John W.; for the IMPACT Study Investigators

2008-01-01

Objective: To determine whether a subset of depressive symptoms could be identified to facilitate diagnosis of depression in older adults in primary care. Method: Secondary analysis was conducted on 898 participants aged 60 years or older with major depressive disorder and/or dysthymic disorder (according to DSM-IV criteria) who participated in the Improving Mood–Promoting Access to Collaborative Treatment (IMPACT) study, a multisite, randomized trial of collaborative care for depression (recruitment from July 1999 to August 2001). Linear regression was used to identify a core subset of depressive symptoms associated with decreased social, physical, and mental functioning. The sensitivity and specificity, adjusting for selection bias, were evaluated for these symptoms. The sensitivity and specificity of a second subset of 4 depressive symptoms previously validated in a midlife sample was also evaluated. Results: Psychomotor changes, fatigue, and suicidal ideation were associated with decreased functioning and served as the core set of symptoms. Adjusting for selection bias, the sensitivity of these 3 symptoms was 0.012 and specificity 0.994. The sensitivity of the 4 symptoms previously validated in a midlife sample was 0.019 and specificity was 0.997. Conclusion: We identified 3 depression symptoms that were highly specific for major depressive disorder in older adults. However, these symptoms and a previously identified subset were too insensitive for accurate diagnosis. Therefore, we recommend a full assessment of DSM-IV depression criteria for accurate diagnosis. PMID:18311416

Creating a non-linear total sediment load formula using polynomial best subset regression model

NASA Astrophysics Data System (ADS)

Okcu, Davut; Pektas, Ali Osman; Uyumaz, Ali

2016-08-01

The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations.

Characterization of tumor-associated T-lymphocyte subsets and immune checkpoint molecules in head and neck squamous cell carcinoma

PubMed Central

Thelen, Martin; Reuter, Sabrina; Zentis, Peter; Shimabukuro-Vornhagen, Alexander; Theurich, Sebastian; Wennhold, Kerstin; Garcia-Marquez, Maria; Tharun, Lars; Quaas, Alexander; Schauss, Astrid; Isensee, Jörg; Hucho, Tim; Huebbers, Christian

2017-01-01

The composition of tumor-infiltrating lymphocytes (TIL) reflects biology and immunogenicity of cancer. Here, we characterize T-cell subsets and expression of immune checkpoint molecules in head and neck squamous cell carcinoma (HNSCC). We analyzed TIL subsets in primary tumors (n = 34), blood (peripheral blood mononuclear cells (PBMC); n = 34) and non-cancerous mucosa (n = 7) of 34 treatment-naïve HNSCC patients and PBMC of 15 healthy controls. Flow cytometry analyses revealed a highly variable T-cell infiltration mainly of an effector memory phenotype (CD45RA−/CCR7−). Naïve T cells (CD45RA+/CCR7+) were decreased in the microenvironment compared to PBMC of patients, while regulatory T cells (CD4+/CD25+/CD127low and CD4+/CD39+) were elevated. Furthermore, we performed digital image analyses of entire cross sections of HNSCC to define the ‘Immunoscore’ (CD3+ and CD8+ cell infiltration in tumor core and invasive margin) and quantified MHC class I expression on tumor cells by immunohistochemistry. Immune checkpoint molecules cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), programmed cell death 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1) were increased in TILs compared to peripheral T cells in flow-cytometric analysis. Human papillomavirus (HPV) positive tumors showed higher numbers of TILs, but a similar composition of T-cell subsets and checkpoint molecule expression compared to HPV negative tumors. Taken together, the tumor microenvironment of HNSCC is characterized by a strong infiltration of regulatory T cells and high checkpoint molecule expression on T-cell subsets. In view of increasingly used immunotherapies, a detailed knowledge of TILs and checkpoint molecule expression on TILs is of high translational relevance. PMID:28574843

FUSED REACTOR FUELS

DOEpatents

Mayer, S.W.

1962-11-13

This invention relates to a nuciear reactor fuel composition comprising (1) from about 0.01 to about 50 wt.% based on the total weight of said composition of at least one element selected from the class consisting of uranium, thorium, and plutonium, wherein said eiement is present in the form of at least one component selected from the class consisting of oxides, halides, and salts of oxygenated anions, with components comprising (2) at least one member selected from the class consisting of (a) sulfur, wherein the sulfur is in the form of at least one entity selected irom the class consisting of oxides of sulfur, metal sulfates, metal sulfites, metal halosulfonates, and acids of sulfur, (b) halogen, wherein said halogen is in the form of at least one compound selected from the class of metal halides, metal halosulfonates, and metal halophosphates, (c) phosphorus, wherein said phosphorus is in the form of at least one constituent selected from the class consisting of oxides of phosphorus, metal phosphates, metal phosphites, and metal halophosphates, (d) at least one oxide of a member selected from the class consisting of a metal and a metalloid wherein said oxide is free from an oxide of said element in (1); wherein the amount of at least one member selected from the class consisting of halogen and sulfur is at least about one at.% based on the amount of the sum of said sulfur, halogen, and phosphorus atom in said composition; and wherein the amount of said 2(a), 2(b) and 2(c) components in said composition which are free from said elements of uranium, thorium, arid plutonium, is at least about 60 wt.% based on the combined weight of the components of said composition which are free from said elements of uranium, thorium, and plutonium. (AEC)

Creation of mortality risk charts using 123I meta-iodobenzylguanidine heart-to-mediastinum ratio in patients with heart failure: 2- and 5-year risk models.

PubMed

Nakajima, Kenichi; Nakata, Tomoaki; Matsuo, Shinro; Jacobson, Arnold F

2016-10-01

(123)I meta-iodobenzylguanidine (MIBG) imaging has been extensively used for prognostication in patients with chronic heart failure (CHF). The purpose of this study was to create mortality risk charts for short-term (2 years) and long-term (5 years) prediction of cardiac mortality. Using a pooled database of 1322 CHF patients, multivariate analysis, including (123)I-MIBG late heart-to-mediastinum ratio (HMR), left ventricular ejection fraction (LVEF), and clinical factors, was performed to determine optimal variables for the prediction of 2- and 5-year mortality risk using subsets of the patients (n = 1280 and 933, respectively). Multivariate logistic regression analysis was performed to create risk charts. Cardiac mortality was 10 and 22% for the sub-population of 2- and 5-year analyses. A four-parameter multivariate logistic regression model including age, New York Heart Association (NYHA) functional class, LVEF, and HMR was used. Annualized mortality rate was <1% in patients with NYHA Class I-II and HMR ≥ 2.0, irrespective of age and LVEF. In patients with NYHA Class III-IV, mortality rate was 4-6 times higher for HMR < 1.40 compared with HMR ≥ 2.0 in all LVEF classes. Among the subset of patients with b-type natriuretic peptide (BNP) results (n = 491 and 359 for 2- and 5-year models, respectively), the 5-year model showed incremental value of HMR in addition to BNP. Both 2- and 5-year risk prediction models with (123)I-MIBG HMR can be used to identify low-risk as well as high-risk patients, which can be effective for further risk stratification of CHF patients even when BNP is available. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

Atlas ranking and selection for automatic segmentation of the esophagus from CT scans

NASA Astrophysics Data System (ADS)

Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M.; Garden, Adam S.; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence

2017-12-01

In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure’s inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback-Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).

77 FR 43879 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Designation of a Longer Period for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-26

... Proposed Rule Change Amending NYSE Arca Equities Rule 7.31(h) To Add a PL Select Order Type July 20, 2012...(h) to add a PL Select Order type. The proposed rule change was published for comment in the Federal... security at a specified, undisplayed price. The PL Select Order would be a subset of the PL Order that...

Gene selection for microarray data classification via subspace learning and manifold regularization.

PubMed

Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

2017-12-19

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

A small number of candidate gene SNPs reveal continental ancestry in African Americans

PubMed Central

KODAMAN, NURI; ALDRICH, MELINDA C.; SMITH, JEFFREY R.; SIGNORELLO, LISA B.; BRADLEY, KEVIN; BREYER, JOAN; COHEN, SARAH S.; LONG, JIRONG; CAI, QIUYIN; GILES, JUSTIN; BUSH, WILLIAM S.; BLOT, WILLIAM J.; MATTHEWS, CHARLES E.; WILLIAMS, SCOTT M.

2013-01-01

SUMMARY Using genetic data from an obesity candidate gene study of self-reported African Americans and European Americans, we investigated the number of Ancestry Informative Markers (AIMs) and candidate gene SNPs necessary to infer continental ancestry. Proportions of African and European ancestry were assessed with STRUCTURE (K=2), using 276 AIMs. These reference values were compared to estimates derived using 120, 60, 30, and 15 SNP subsets randomly chosen from the 276 AIMs and from 1144 SNPs in 44 candidate genes. All subsets generated estimates of ancestry consistent with the reference estimates, with mean correlations greater than 0.99 for all subsets of AIMs, and mean correlations of 0.99±0.003; 0.98± 0.01; 0.93±0.03; and 0.81± 0.11 for subsets of 120, 60, 30, and 15 candidate gene SNPs, respectively. Among African Americans, the median absolute difference from reference African ancestry values ranged from 0.01 to 0.03 for the four AIMs subsets and from 0.03 to 0.09 for the four candidate gene SNP subsets. Furthermore, YRI/CEU Fst values provided a metric to predict the performance of candidate gene SNPs. Our results demonstrate that a small number of SNPs randomly selected from candidate genes can be used to estimate admixture proportions in African Americans reliably. PMID:23278390

Parametric estimates for the receiver operating characteristic curve generalization for non-monotone relationships.

PubMed

Martínez-Camblor, Pablo; Pardo-Fernández, Juan C

2017-01-01

Diagnostic procedures are based on establishing certain conditions and then checking if those conditions are satisfied by a given individual. When the diagnostic procedure is based on a continuous marker, this is equivalent to fix a region or classification subset and then check if the observed value of the marker belongs to that region. Receiver operating characteristic curve is a valuable and popular tool to study and compare the diagnostic ability of a given marker. Besides, the area under the receiver operating characteristic curve is frequently used as an index of the global discrimination ability. This paper revises and widens the scope of the receiver operating characteristic curve definition by setting the classification subsets in which the final decision is based in the spotlight of the analysis. We revise the definition of the receiver operating characteristic curve in terms of particular classes of classification subsets and then focus on a receiver operating characteristic curve generalization for situations in which both low and high values of the marker are associated with more probability of having the studied characteristic. Parametric and non-parametric estimators of the receiver operating characteristic curve generalization are investigated. Monte Carlo studies and real data examples illustrate their practical performance.

A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization.

PubMed

Vafaee Sharbaf, Fatemeh; Mosafer, Sara; Moattar, Mohammad Hossein

2016-06-01

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy. CLA is applied due to its capability to learn and model complicated relationships. The selected features from the last phase are evaluated using ROC curve and the most effective while smallest feature subset is determined. The classifiers which are evaluated in the proposed framework are K-nearest neighbor; support vector machine and naïve Bayes. The proposed approach is evaluated on 4 microarray datasets. The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

A Screen for Novel Phosphoinositide 3-kinase Effector Proteins*

PubMed Central

Dixon, Miles J.; Gray, Alexander; Boisvert, François-Michel; Agacan, Mark; Morrice, Nicholas A.; Gourlay, Robert; Leslie, Nicholas R.; Downes, C. Peter; Batty, Ian H.

2011-01-01

Class I phosphoinositide 3-kinases exert important cellular effects through their two primary lipid products, phosphatidylinositol 3,4,5-trisphosphate and phosphatidylinositol 3,4-bisphosphate (PtdIns(3,4)P2). As few molecular targets for PtdIns(3,4)P2 have yet been identified, a screen for PI 3-kinase-responsive proteins that is selective for these is described. This features a tertiary approach incorporating a unique, primary recruitment of target proteins in intact cells to membranes selectively enriched in PtdIns(3,4)P2. A secondary purification of these proteins, optimized using tandem pleckstrin homology domain containing protein-1 (TAPP-1), an established PtdIns(3,4)P2 selective ligand, yields a fraction enriched in proteins of potentially similar lipid binding character that are identified by liquid chromatography-tandem MS. Thirdly, this approach is coupled to stable isotope labeling with amino acids in cell culture using differential isotope labeling of cells stimulated in the absence and presence of the PI 3-kinase inhibitor wortmannin. This provides a ratio-metric readout that distinguishes authentically responsive components from copurifying background proteins. Enriched fractions thus obtained from astrocytoma cells revealed a subset of proteins that exhibited ratios indicative of their initial, cellular responsiveness to PI 3-kinase activation. The inclusion among these of tandem pleckstrin homology domain containing protein-1, three isoforms of Akt, switch associated protein-70, early endosome antigen-1 and of additional proteins expressing recognized lipid binding domains demonstrates the utility of this strategy and lends credibility to the novel candidate proteins identified. The latter encompass a broad set of proteins that include the gene product of TBC1D2A, a putative Rab guanine nucleotide triphosphatase activating protein (GAP) and IQ motif containing GAP1, a potential tumor promoter. A sequence comparison of the former protein indicates the presence of a pleckstrin homology domain whose lipid binding character remains to be established. IQ motif containing GAP1 lacks known lipid interacting components and a preliminary analysis here indicates that this may exemplify a novel class of atypical phosphoinositide (aPI) binding domain. PMID:21263009

Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

PubMed

Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

2015-10-01

Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large carnivore species where landscape-scale resource selection data already exist.

‘In-Format’ screening of a novel bispecific antibody format reveals significant potency improvements relative to unformatted molecules

PubMed Central

Scott, Martin J.; Lee, Jennifer A.; Wake, Matthew S.; Batt, Kelly V.; Wattam, Trevor A.; Hiles, Ian D.; Batuwangala, Thil D.; Ashman, Claire I.; Steward, Michael

2017-01-01

ABSTRACT Bispecific antibodies (BsAbs) are emerging as an important class of biopharmaceutical. The majority of BsAbs are created from conventional antibodies or fragments engineered into more complex configurations. A recurring challenge in their development, however, is the identification of components that are optimised for inclusion in the final format in order to deliver both efficacy and robust biophysical properties. Using a modular BsAb format, the mAb-dAb, we assessed whether an ‘in-format’ screening approach, designed to select format-compatible domain antibodies, could expedite lead discovery. Human nerve growth factor (NGF) was selected as an antigen to validate the approach; domain antibody (dAb) libraries were screened, panels of binders identified, and binding affinities and potencies compared for selected dAbs and corresponding mAb-dAbs. A number of dAbs that exhibited high potency (IC50) when assessed in-format were identified. In contrast, the corresponding dAb monomers had ∼1000-fold lower potency than the formatted dAbs; such dAb monomers would therefore have been omitted from further characterization. Subsequent stoichiometric analyses of mAb-dAbs bound to NGF, or an additional target antigen (vascular endothelial growth factor), suggested different target binding modes; this indicates that the observed potency improvements cannot be attributed simply to an avidity effect offered by the mAb-dAb format. We conclude that, for certain antigens, screening naïve selection outputs directly in-format enables the identification of a subset of format-compatible dAbs, and that this offers substantial benefits in terms of molecular properties and development time. PMID:27786601

Evolution of major histocompatibility complex class I and class II genes in the brown bear

PubMed Central

2012-01-01

Background Major histocompatibility complex (MHC) proteins constitute an essential component of the vertebrate immune response, and are coded by the most polymorphic of the vertebrate genes. Here, we investigated sequence variation and evolution of MHC class I and class II DRB, DQA and DQB genes in the brown bear Ursus arctos to characterise the level of polymorphism, estimate the strength of positive selection acting on them, and assess the extent of gene orthology and trans-species polymorphism in Ursidae. Results We found 37 MHC class I, 16 MHC class II DRB, four DQB and two DQA alleles. We confirmed the expression of several loci: three MHC class I, two DRB, two DQB and one DQA. MHC class I also contained two clusters of non-expressed sequences. MHC class I and DRB allele frequencies differed between northern and southern populations of the Scandinavian brown bear. The rate of nonsynonymous substitutions (dN) exceeded the rate of synonymous substitutions (dS) at putative antigen binding sites of DRB and DQB loci and, marginally significantly, at MHC class I loci. Models of codon evolution supported positive selection at DRB and MHC class I loci. Both MHC class I and MHC class II sequences showed orthology to gene clusters found in the giant panda Ailuropoda melanoleuca. Conclusions Historical positive selection has acted on MHC class I, class II DRB and DQB, but not on the DQA locus. The signal of historical positive selection on the DRB locus was particularly strong, which may be a general feature of caniforms. The presence of MHC class I pseudogenes may indicate faster gene turnover in this class through the birth-and-death process. South–north population structure at MHC loci probably reflects origin of the populations from separate glacial refugia. PMID:23031405

Evolution of major histocompatibility complex class I and class II genes in the brown bear.

PubMed

Kuduk, Katarzyna; Babik, Wiesław; Bojarska, Katarzyna; Sliwińska, Ewa B; Kindberg, Jonas; Taberlet, Pierre; Swenson, Jon E; Radwan, Jacek

2012-10-02

Major histocompatibility complex (MHC) proteins constitute an essential component of the vertebrate immune response, and are coded by the most polymorphic of the vertebrate genes. Here, we investigated sequence variation and evolution of MHC class I and class II DRB, DQA and DQB genes in the brown bear Ursus arctos to characterise the level of polymorphism, estimate the strength of positive selection acting on them, and assess the extent of gene orthology and trans-species polymorphism in Ursidae. We found 37 MHC class I, 16 MHC class II DRB, four DQB and two DQA alleles. We confirmed the expression of several loci: three MHC class I, two DRB, two DQB and one DQA. MHC class I also contained two clusters of non-expressed sequences. MHC class I and DRB allele frequencies differed between northern and southern populations of the Scandinavian brown bear. The rate of nonsynonymous substitutions (dN) exceeded the rate of synonymous substitutions (dS) at putative antigen binding sites of DRB and DQB loci and, marginally significantly, at MHC class I loci. Models of codon evolution supported positive selection at DRB and MHC class I loci. Both MHC class I and MHC class II sequences showed orthology to gene clusters found in the giant panda Ailuropoda melanoleuca. Historical positive selection has acted on MHC class I, class II DRB and DQB, but not on the DQA locus. The signal of historical positive selection on the DRB locus was particularly strong, which may be a general feature of caniforms. The presence of MHC class I pseudogenes may indicate faster gene turnover in this class through the birth-and-death process. South-north population structure at MHC loci probably reflects origin of the populations from separate glacial refugia.

Design and Application of Drought Indexes in Highly Regulated Mediterranean Water Systems

NASA Astrophysics Data System (ADS)

Castelletti, A.; Zaniolo, M.; Giuliani, M.

2017-12-01

Costs of drought are progressively increasing due to the undergoing alteration of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, most of the traditional drought indexes fail in detecting critical events in highly regulated systems, which generally rely on ad-hoc formulations and cannot be generalized to different context. In this study, we contribute a novel framework for the design of a basin-customized drought index. This index represents a surrogate of the state of the basin and is computed by combining the available information about the water available in the system to reproduce a representative target variable for the drought condition of the basin (e.g., water deficit). To select the relevant variables and combinatione thereof, we use an advanced feature extraction algorithm called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS). W-QEISS relies on a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The accuracy objective is evaluated trough the calibration of an extreme learning machine of the water deficit for each candidate subset of variables, with the index selected from the resulting solutions identifying a suitable compromise between accuracy, cardinality, relevance, and redundancy. The approach is tested on Lake Como, Italy, a regulated lake mainly operated for irrigation supply. In the absence of an institutional drought monitoring system, we constructed the combined index using all the hydrological variables from the existing monitoring system as well as common drought indicators at multiple time aggregations. The soil moisture deficit in the root zone computed by a distributed-parameter water balance model of the agricultural districts is used as target variable. Numerical results show that our combined drought index succesfully reproduces the deficit. The index represents a valuable information for supporting appropriate drought management strategies, including the possibility of directly informing the lake operations about the drought conditions and improve the overall reliability of the irrigation supply system.

Estimation of fat-free mass in Asian neonates using bioelectrical impedance analysis

PubMed Central

Tint, Mya-Thway; Ward, Leigh C; Soh, Shu E; Aris, Izzuddin M; Chinnadurai, Amutha; Saw, Seang Mei; Gluckman, Peter D; Godfrey, Keith M; Chong, Yap-Seng; Kramer, Michael S; Yap, Fabian; Lingwood, Barbara; Lee, Yung Seng

2016-01-01

The aims of this study were to develop and validate a prediction equation of fat-free mass (FFM) based on bioelectrical impedance analysis (BIA) and anthropometry using air displacement plethysmography (ADP) as a reference in Asian neonates and to test the applicability of the prediction equations in independent Western cohort. A total of 173 neonates at birth and 140 at week-2 of age were included. Multiple linear regression analysis was performed to develop the prediction equations in a two-third randomly selected subset and validated on the remaining one-third subset at each time point and in an independent Queensland cohort. FFM measured by ADP was the dependent variable and anthropometric measures, sex and impedance quotient (L2/R50) were independent variables in the model. Accuracy of prediction equations were assessed using intra-class correlation and Bland-Altman analyses. L2/R50 was the significant predictor of FFM at week-2 but not at birth. Compared to the model using weight, sex and length, including L2/R50 slightly improved the prediction with a bias of 0.01kg with 2SD limits of agreement (LOA) (0.18, −0.20). Prediction explained 88.9% of variation but not beyond that of anthropometry. Applying these equations to Queensland cohort provided similar performance at the appropriate age. However, when the Queensland equations were applied to our cohort, the bias increased slightly but with similar LOA. BIA appears to have limited use in predicting FFM in the first few weeks of life compared to simple anthropometry in Asian populations. There is a need for population and age appropriate FFM prediction equations. PMID:26856420

Discrimination of nuclear explosions and earthquakes from teleseismic distances with a local network of short period seismic stations using artificial neural networks

NASA Astrophysics Data System (ADS)

Tiira, Timo

1996-10-01

Seismic discrimination capability of artificial neural networks (ANNs) was studied using earthquakes and nuclear explosions from teleseismic distances. The events were selected from two areas, which were analyzed separately. First, 23 nuclear explosions from Semipalatinsk and Lop Nor test sites were compared with 46 earthquakes from adjacent areas. Second, 39 explosions from Nevada test site were compared with 27 earthquakes from close-by areas. The basic discriminants were complexity, spectral ratio and third moment of frequency. The spectral discriminants were computed in five different ways to obtain all the information embedded in the signals, some of which were relatively weak. The discriminants were computed using data from six short period stations in Central and southern Finland. The spectral contents of the signals of both classes varied considerably between the stations. The 66 discriminants were formed into 65 optimum subsets of different sizes by using stepwise linear regression. A type of ANN called multilayer perceptron (MLP) was applied to each of the subsets. As a comparison the classification was repeated using linear discrimination analysis (LDA). Since the number of events was small the testing was made with the leave-one-out method. The ANN gave significantly better results than LDA. As a final tool for discrimination a combination of the ten neural nets with the best performance were used. All events from Central Asia were clearly discriminated and over 90% of the events from Nevada region were confidently discriminated. The better performance of ANNs was attributed to its ability to form complex decision regions between the groups and to its highly non-linear nature.

Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement.

PubMed

Teoh, Shao Thing; Kitamura, Miki; Nakayama, Yasumune; Putri, Sastia; Mukai, Yukio; Fukusaki, Eiichiro

2016-08-01

In recent years, the advent of high-throughput omics technology has made possible a new class of strain engineering approaches, based on identification of possible gene targets for phenotype improvement from omic-level comparison of different strains or growth conditions. Metabolomics, with its focus on the omic level closest to the phenotype, lends itself naturally to this semi-rational methodology. When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype. However, linear modeling techniques such as PLS require a consistent metabolite-phenotype trend across the samples, which may not be the case when outliers or multiple conflicting trends are present in the data. To address this, we proposed a data-mining strategy that utilizes random sample consensus (RANSAC) to select subsets of samples with consistent trends for construction of better regression models. By applying a combination of RANSAC and PLS (RANSAC-PLS) to a dataset from a previous study (gas chromatography/mass spectrometry metabolomics data and 1-butanol tolerance of 19 yeast mutant strains), new metabolites were indicated to be correlated with tolerance within certain subsets of the samples. The relevance of these metabolites to 1-butanol tolerance were then validated from single-deletion strains of corresponding metabolic genes. The results showed that RANSAC-PLS is a promising strategy to identify unique metabolites that provide additional hints for phenotype improvement, which could not be detected by traditional PLS modeling using the entire dataset. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Two-Photon Microscopy Imaging of thy1GFP-M Transgenic Mice: A Novel Animal Model to Investigate Brain Dendritic Cell Subsets In Vivo

PubMed Central

Laperchia, Claudia; Allegra Mascaro, Anna L.; Sacconi, Leonardo; Andrioli, Anna; Mattè, Alessandro; De Franceschi, Lucia; Grassi-Zucconi, Gigliola; Bentivoglio, Marina; Buffelli, Mario; Pavone, Francesco S.

2013-01-01

Transgenic mice expressing fluorescent proteins in specific cell populations are widely used for in vivo brain studies with two-photon fluorescence (TPF) microscopy. Mice of the thy1GFP-M line have been engineered for selective expression of green fluorescent protein (GFP) in neuronal populations. Here, we report that TPF microscopy reveals, at the brain surface of these mice, also motile non-neuronal GFP+ cells. We have analyzed the behavior of these cells in vivo and characterized in brain sections their immunophenotype. With TPF imaging, motile GFP+ cells were found in the meninges, subarachnoid space and upper cortical layers. The striking feature of these cells was their ability to move across the brain parenchyma, exhibiting evident shape changes during their scanning-like motion. In brain sections, GFP+ cells were immunonegative to antigens recognizing motile cells such as migratory neuroblasts, neuronal and glial precursors, mast cells, and fibroblasts. GFP+ non-neuronal cells exhibited instead the characteristic features and immunophenotype (CD11c and major histocompatibility complex molecule class II immunopositivity) of dendritic cells (DCs), and were immunonegative to the microglial marker Iba-1. GFP+ cells were also identified in lymph nodes and blood of thy1GFP-M mice, supporting their identity as DCs. Thus, TPF microscopy has here allowed the visualization for the first time of the motile behavior of brain DCs in situ. The results indicate that the thy1GFP-M mouse line provides a novel animal model for the study of subsets of these professional antigen-presenting cells in the brain. Information on brain DCs is still very limited and imaging in thy1GFP-M mice has a great potential for analyses of DC-neuron interaction in normal and pathological conditions. PMID:23409142

Estimation of fat-free mass in Asian neonates using bioelectrical impedance analysis.

PubMed

Tint, Mya-Thway; Ward, Leigh C; Soh, Shu E; Aris, Izzuddin M; Chinnadurai, Amutha; Saw, Seang Mei; Gluckman, Peter D; Godfrey, Keith M; Chong, Yap-Seng; Kramer, Michael S; Yap, Fabian; Lingwood, Barbara; Lee, Yung Seng

2016-03-28

The aims of this study were to develop and validate a prediction equation of fat-free mass (FFM) based on bioelectrical impedance analysis (BIA) and anthropometry using air-displacement plethysmography (ADP) as a reference in Asian neonates and to test the applicability of the prediction equations in an independent Western cohort. A total of 173 neonates at birth and 140 at two weeks of age were included. Multiple linear regression analysis was performed to develop the prediction equations in a two-third randomly selected subset and validated on the remaining one-third subset at each time point and in an independent Queensland cohort. FFM measured by ADP was the dependent variable, and anthropometric measures, sex and impedance quotient (L2/R50) were independent variables in the model. Accuracy of prediction equations was assessed using intra-class correlation and Bland-Altman analyses. L2/R50 was the significant predictor of FFM at week two but not at birth. Compared with the model using weight, sex and length, including L2/R50 slightly improved the prediction with a bias of 0·01 kg with 2 sd limits of agreement (LOA) (0·18, -0·20). Prediction explained 88·9 % of variation but not beyond that of anthropometry. Applying these equations to the Queensland cohort provided similar performance at the appropriate age. However, when the Queensland equations were applied to our cohort, the bias increased slightly but with similar LOA. BIA appears to have limited use in predicting FFM in the first few weeks of life compared with simple anthropometry in Asian populations. There is a need for population- and age-appropriate FFM prediction equations.

Automatic design of basin-specific drought indexes for highly regulated water systems

NASA Astrophysics Data System (ADS)

Zaniolo, Marta; Giuliani, Matteo; Castelletti, Andrea Francesco; Pulido-Velazquez, Manuel

2018-04-01

Socio-economic costs of drought are progressively increasing worldwide due to undergoing alterations of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, traditional drought indexes often fail at detecting critical events in highly regulated systems, where natural water availability is conditioned by the operation of water infrastructures such as dams, diversions, and pumping wells. Here, ad hoc index formulations are usually adopted based on empirical combinations of several, supposed-to-be significant, hydro-meteorological variables. These customized formulations, however, while effective in the design basin, can hardly be generalized and transferred to different contexts. In this study, we contribute FRIDA (FRamework for Index-based Drought Analysis), a novel framework for the automatic design of basin-customized drought indexes. In contrast to ad hoc empirical approaches, FRIDA is fully automated, generalizable, and portable across different basins. FRIDA builds an index representing a surrogate of the drought conditions of the basin, computed by combining all the relevant available information about the water circulating in the system identified by means of a feature extraction algorithm. We used the Wrapper for Quasi-Equally Informative Subset Selection (W-QEISS), which features a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The preferred variable subset is selected among the efficient solutions and used to formulate the final index according to alternative model structures. We apply FRIDA to the case study of the Jucar river basin (Spain), a drought-prone and highly regulated Mediterranean water resource system, where an advanced drought management plan relying on the formulation of an ad hoc state index is used for triggering drought management measures. The state index was constructed empirically with a trial-and-error process begun in the 1980s and finalized in 2007, guided by the experts from the Confederación Hidrográfica del Júcar (CHJ). Our results show that the automated variable selection outcomes align with CHJ's 25-year-long empirical refinement. In addition, the resultant FRIDA index outperforms the official State Index in terms of accuracy in reproducing the target variable and cardinality of the selected inputs set.

Why Drill Here? Teaching to Build Student Understanding of the Role Sediment Cores from Polar Regions play in Interpreting Climate Change

NASA Astrophysics Data System (ADS)

Pound, K. S.; St. John, K.; Krissek, L. A.; Jones, M. H.; Leckie, R. M.; Pyle, E. J.

2008-12-01

That the ocean basins provide a record of past global climate changes through their sediment cores is often a surprise or novel idea for students. Equally surprising to many students is the fact that current research is being undertaken in remote polar regions, even though sedimentary records already exist from the low and mid latitude regions. Students are often also perplexed about how decisions are made regarding the selection of drill sites in the polar regions. Using an inquiry-based approach we are developing a series of simple exercises that are scaffolded to build student understanding around the question "Why Drill Here?" The exercises are based on IODP Expedition 302 (ACEX) in the Arctic, and on the Antarctic Geological Drilling (ANDRILL) program, which are used as case studies. The "Why Drill Here?" question is addressed at multiple levels so students can formulate a scientific rationale behind selection of sites for seafloor drilling in the Arctic and Antarctic regions. Technological challenges and solutions to doing field-based science in polar regions are explored. Finally, a subset of research results are investigated and compared with the current scientific paradigm on Cenozoic climate evolution to demonstrate that science is an evolving process. These exercises can be adapted for use in a variety of Introductory Earth Science classes.

High affinity soluble ILT2 receptor: a potent inhibitor of CD8(+) T cell activation.

PubMed

Moysey, Ruth K; Li, Yi; Paston, Samantha J; Baston, Emma E; Sami, Malkit S; Cameron, Brian J; Gavarret, Jessie; Todorov, Penio; Vuidepot, Annelise; Dunn, Steven M; Pumphrey, Nicholas J; Adams, Katherine J; Yuan, Fang; Dennis, Rebecca E; Sutton, Deborah H; Johnson, Andy D; Brewer, Joanna E; Ashfield, Rebecca; Lissin, Nikolai M; Jakobsen, Bent K

2010-12-01

Using directed mutagenesis and phage display on a soluble fragment of the human immunoglobulin super-family receptor ILT2 (synonyms: LIR1, MIR7, CD85j), we have selected a range of mutants with binding affinities enhanced by up to 168,000-fold towards the conserved region of major histocompatibility complex (MHC) class I molecules. Produced in a dimeric form, either by chemical cross-linking with bivalent polyethylene glycol (PEG) derivatives or as a genetic fusion with human IgG Fc-fragment, the mutants exhibited a further increase in ligand-binding strength due to the avidity effect, with resident half-times (t(1/2)) on the surface of MHC I-positive cells of many hours. The novel compounds antagonized the interaction of CD8 co-receptor with MHC I in vitro without affecting the peptide-specific binding of T-cell receptors (TCRs). In both cytokine-release assays and cell-killing experiments the engineered receptors inhibited the activation of CD8(+) cytotoxic T lymphocytes (CTLs) in the presence of their target cells, with subnanomolar potency and in a dose-dependent manner. As a selective inhibitor of CD8(+) CTL responses, the engineered high affinity ILT2 receptor presents a new tool for studying the activation mechanism of different subsets of CTLs and could have potential for the development of novel autoimmunity therapies.

Drug selection in French university hospitals: analysis of formularies for nine competitive pharmacological classes.

PubMed

Gallini, Adeline; Juillard-Condat, Blandine; Saux, Marie-Claude; Taboulet, Florence

2011-11-01

To give a panorama of the selectivity and agreement of French university hospitals' drug formularies (HDF) for nine competitive classes. All university hospitals were asked to send their HDF and selection criteria as of January 2009 for nine competitive pharmacological classes (proton pump inhibitors, serotonin antagonists, low molecular weight heparins, erythropoietins, angiotensin converting enzyme inhibitors, angiotensin II receptor antagonists, statins, α-adrenoreceptor antagonists and selective serotonin re-uptake inhibitors). Selectivity of HDF was estimated by the percentage of drug entities selected by the hospital within the pharmacological class. Agreement between hospitals was assessed with modified kappa coefficients for multi-raters. Twenty-one out of the 29 hospitals agreed to participate. These hospitals selected between 34% and 63% of the drug entities available for the nine classes, which represented 18 to 35 agents. Regarding the nature of chosen drug entities, the overall level of agreement was 'fair' and varied with pharmacological classes. Selection criteria were sent by only 12 hospitals. The technical component was the most important element in all hospitals. The weight of the economic component varied between 20% and 40% in the tender's grade. Large variations were seen in the number and nature of drugs selected by university hospitals which can be attributable to two successive decision-making processes (evaluation by the Drug and Therapeutics Committee followed by the purchasing process). © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles.

PubMed

Piao, Yongjun; Piao, Minghao; Ryu, Keun Ho

2017-01-01

Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data. In this article, we propose a feature subset-based ensemble method in which each model is learned from a different projection of the original feature space to classify multiple cancers. In our method, the feature relevance and redundancy are considered to generate multiple feature subsets, the base classifiers are learned from each independent miRNA subset, and the average posterior probability is used to combine the base classifiers. To test the performance of our method, we used bead-based and sequence-based miRNA expression datasets and conducted 10-fold and leave-one-out cross validations. The experimental results show that the proposed method yields good results and has higher prediction accuracy than popular ensemble methods. The Java program and source code of the proposed method and the datasets in the experiments are freely available at https://sourceforge.net/projects/mirna-ensemble/. Copyright © 2016 Elsevier Ltd. All rights reserved.

An overview of the Columbia Habitat Monitoring Program's (CHaMP) spatial-temporal design framework

EPA Science Inventory

We briefly review the concept of a master sample applied to stream networks in which a randomized set of stream sites is selected across a broad region to serve as a list of sites from which a subset of sites is selected to achieve multiple objectives of specific designs. The Col...

Selecting and Validating Tasks from a Kindergarten Screening Battery that Best Predict Third Grade Educational Placement

ERIC Educational Resources Information Center

Scott, Marcia Strong; Delgado, Christine F.; Tu, Shihfen; Fletcher, Kathryn L.

2005-01-01

In this study, predictive classification accuracy was used to select those tasks from a kindergarten screening battery that best identified children who, three years later, were classified as educable mentally handicapped or as having a specific learning disability. A subset of measures enabled correct classification of 91% of the children in…

Selecting climate change scenarios using impact-relevant sensitivities

Treesearch

Julie A. Vano; John B. Kim; David E. Rupp; Philip W. Mote

2015-01-01

Climate impact studies often require the selection of a small number of climate scenarios. Ideally, a subset would have simulations that both (1) appropriately represent the range of possible futures for the variable/s most important to the impact under investigation and (2) come from global climate models (GCMs) that provide plausible results for future climate in the...

[Varicocele and coincidental abacterial prostato-vesiculitis: negative role about the sperm output].

PubMed

Vicari, Enzo; La Vignera, Sandro; Tracia, Angelo; Cardì, Francesco; Donati, Angelo

2003-03-01

To evaluate the frequency and the role of a coincidentally expressed abacterial prostato-vesiculitis (PV) on sperm output in patients with left varicocele (Vr). We evaluated 143 selected infertile patients (mean age 27 years, range 21-43), with oligo- and/or astheno- and/or teratozoospermia (OAT) subdivided in two groups. Group A included 76 patients with previous varicocelectomy and persistent OAT. Group B included 67 infertile patients (mean age 26 years, range 21-37) with OAT and not varicocelectomized. Patients with Vr and coincidental didymo-epididymal ultrasound (US) abnormalities were excluded from the study. Following rectal prostato-vesicular ultrasonography, each group was subdivided in two subsets on the basis of the absence (group A: subset Vr-/PV-; and group B: subset Vr+/PV-) or the presence of an abacterial PV (group A: subset Vr-/PV+; group B: subset Vr+/PV+). Particularly, PV was present in 47.4% and 41.8% patients of groups A and B, respectively. This coincidental pathology was ipsilateral with Vr in the 61% of the cases. Semen analysis was performed in all patients. Patients of group A showed a total sperm number significantly higher than those found in group B. In presence of PV, sperm parameters were not significantly different between matched--subsets (Vr-/PV+ vs. Vr+/PV+). In absence of PV, the sperm density, the total sperm number and the percentage of forward motility from subset with previous varicocelectomy (Vr-/PV) exhibited values significantly higher than those found in the matched--subset (Vr+/PV-). Sperm analysis alone performed in patients with left Vr is not a useful prognostic post-varicocelectomy marker. Since following varicocelectomy a lack of sperm response could mask another coincidental pathology, the identification through US scans of a possible PV may be mandatory. On the other hand, an integrated uro-andrological approach, including US scans, allows to enucleate subsets of patients with Vr alone, who will have an expected better sperm response following Vr repair.

CD127 and CD25 expression defines CD4+ T cell subsets that are differentially depleted during HIV infection.

PubMed

Dunham, Richard M; Cervasi, Barbara; Brenchley, Jason M; Albrecht, Helmut; Weintrob, Amy; Sumpter, Beth; Engram, Jessica; Gordon, Shari; Klatt, Nichole R; Frank, Ian; Sodora, Donald L; Douek, Daniel C; Paiardini, Mirko; Silvestri, Guido

2008-04-15

Decreased CD4(+) T cell counts are the best marker of disease progression during HIV infection. However, CD4(+) T cells are heterogeneous in phenotype and function, and it is unknown how preferential depletion of specific CD4(+) T cell subsets influences disease severity. CD4(+) T cells can be classified into three subsets by the expression of receptors for two T cell-tropic cytokines, IL-2 (CD25) and IL-7 (CD127). The CD127(+)CD25(low/-) subset includes IL-2-producing naive and central memory T cells; the CD127(-)CD25(-) subset includes mainly effector T cells expressing perforin and IFN-gamma; and the CD127(low)CD25(high) subset includes FoxP3-expressing regulatory T cells. Herein we investigated how the proportions of these T cell subsets are changed during HIV infection. When compared with healthy controls, HIV-infected patients show a relative increase in CD4(+)CD127(-)CD25(-) T cells that is related to an absolute decline of CD4(+)CD127(+)CD25(low/-) T cells. Interestingly, this expansion of CD4(+)CD127(-) T cells was not observed in naturally SIV-infected sooty mangabeys. The relative expansion of CD4(+)CD127(-)CD25(-) T cells correlated directly with the levels of total CD4(+) T cell depletion and immune activation. CD4(+)CD127(-)CD25(-) T cells were not selectively resistant to HIV infection as levels of cell-associated virus were similar in all non-naive CD4(+) T cell subsets. These data indicate that, during HIV infection, specific changes in the fraction of CD4(+) T cells expressing CD25 and/or CD127 are associated with disease progression. Further studies will determine whether monitoring the three subsets of CD4(+) T cells defined based on the expression of CD25 and CD127 should be used in the clinical management of HIV-infected individuals.

Joint Hypermobility Classes in 9-Year-Old Children from the General Population and Anxiety Symptoms.

PubMed

Ezpeleta, Lourdes; Navarro, José Blas; Osa, Núria de la; Penelo, Eva; Bulbena, Antoni

2018-05-25

To obtain joint hypermobility classes in children from the general population and to study their characteristics in relation to anxiety measures. A total of 336 nine-year-old children from the general population were clinically assessed through 9 items of hypermobility, and their parents reported about the severity of anxiety symptoms. Latent class analysis was estimated to group the children according to the presence of hypermobility symptoms, and the obtained classes were related to anxiety. A 2-class solution, labeled as high hypermobility and low hypermobility, best fitted the data. Children in the high hypermobility group scored higher in separation anxiety, social phobia, physical injury fears, and total anxiety than did those in the low group. When applying the threshold reference scores to the total anxiety score, 7.4% of children in the high hypermobility group versus 6% in the low group were reported to experience clinical elevations on total anxiety. High symptoms of hypermobility are associated with higher scores in anxiety symptoms in children from the general population. Children with frequent symptoms of hypermobility may benefit from screening for anxiety symptoms because a subset of them are experiencing clinical elevations and may need comprehensive physical and psychological treatment.

Unsupervised Feature Selection Based on the Morisita Index for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhail

2017-04-01

Hyperspectral sensors are capable of acquiring images with hundreds of narrow and contiguous spectral bands. Compared with traditional multispectral imagery, the use of hyperspectral images allows better performance in discriminating between land-cover classes, but it also results in large redundancy and high computational data processing. To alleviate such issues, unsupervised feature selection techniques for redundancy minimization can be implemented. Their goal is to select the smallest subset of features (or bands) in such a way that all the information content of a data set is preserved as much as possible. The present research deals with the application to hyperspectral images of a recently introduced technique of unsupervised feature selection: the Morisita-Based filter for Redundancy Minimization (MBRM). MBRM is based on the (multipoint) Morisita index of clustering and on the Morisita estimator of Intrinsic Dimension (ID). The fundamental idea of the technique is to retain only the bands which contribute to increasing the ID of an image. In this way, redundant bands are disregarded, since they have no impact on the ID. Besides, MBRM has several advantages over benchmark techniques: in addition to its ability to deal with large data sets, it can capture highly-nonlinear dependences and its implementation is straightforward in any programming environment. Experimental results on freely available hyperspectral images show the good effectiveness of MBRM in remote sensing data processing. Comparisons with benchmark techniques are carried out and random forests are used to assess the performance of MBRM in reducing the data dimensionality without loss of relevant information. References [1] C. Traina Jr., A.J.M. Traina, L. Wu, C. Faloutsos, Fast feature selection using fractal dimension, in: Proceedings of the XV Brazilian Symposium on Databases, SBBD, pp. 158-171, 2000. [2] J. Golay, M. Kanevski, A new estimator of intrinsic dimension based on the multipoint Morisita index, Pattern Recognition 48(12), pp. 4070-4081, 2015. [3] J. Golay, M. Kanevski, Unsupervised feature selection based on the Morisita estimator of intrinsic dimension, arXiv:1608.05581, 2016.

Social interaction anxiety and personality traits predicting engagement in health risk sexual behaviors.

PubMed

Rahm-Knigge, Ryan L; Prince, Mark A; Conner, Bradley T

2018-06-01

Individuals with social interaction anxiety, a facet of social anxiety disorder, withdraw from or avoid social encounters and generally avoid risks. However, a subset engages in health risk sexual behavior (HRSB). Because sensation seeking, emotion dysregulation, and impulsivity predict engagement in HRSB among adolescents and young adults, the present study hypothesized that latent classes of social interaction anxiety and these personality traits would differentially predict likelihood of engagement in HRSB. Finite mixture modeling was used to discern four classes: two low social interaction anxiety classes distinguished by facets of emotion dysregulation, positive urgency, and negative urgency (Low SIAS High Urgency and Low SIAS Low Urgency) and two high social interaction anxiety classes distinguished by positive urgency, negative urgency, risk seeking, and facets of emotion dysregulation (High SIAS High Urgency and High SIAS Low Urgency). HRSB were entered into the model as auxiliary distal outcomes. Of importance to this study were findings that the High SIAS High Urgency class was more likely to engage in most identified HRSB than the High SIAS Low Urgency class. This study extends previous findings on the heterogeneity of social interaction anxiety by identifying the effects of social interaction anxiety and personality on engagement in HRSB. Copyright © 2018 Elsevier Ltd. All rights reserved.

Student performance on conceptual questions: Does instruction matter?

NASA Astrophysics Data System (ADS)

Heron, Paula R. L.

2013-01-01

As part of the tutorial component of introductory calculus-based physics at the University of Washington, students take weekly pretests that consist of conceptual questions. Pretests are so named because they precede each tutorial, but they are frequently administered after lecture instruction. Many variables associated with class composition and prior instruction (if any) could, in principle, affect student performance on these questions. Nonetheless, the results are often found to be "essentially the same" in all classes. With data available from a large number of classes, it is possible to characterize the typical variation quantitatively. In this paper three questions for which we have accumulated thousands of responses, from dozens of classes representing different conditions with respect to the textbook in use, the amount of prior instruction, etc., serve as examples. For each question, we examine the variation in student performance across all classes. We also compare subsets categorized according to the amount of relevant prior instruction each class had received. A preliminary analysis suggests that the variation in performance is essentially random. No statistically significant difference is observed between results obtained before relevant instruction begins and after it has been completed. The results provide evidence that exposure to concepts in lecture and textbook is not sufficient to ensure an improvement in performance on questions that require qualitative reasoning.

Gene transfer preferentially selects MHC class I positive tumour cells and enhances tumour immunogenicity.

PubMed

Hacker, Ulrich T; Schildhauer, Ines; Barroso, Margarita Céspedes; Kofler, David M; Gerner, Franz M; Mysliwietz, Josef; Buening, Hildegard; Hallek, Michael; King, Susan B S

2006-05-01

The modulated expression of MHC class I on tumour tissue is well documented. Although the effect of MHC class I expression on the tumorigenicity and immunogenicity of MHC class I negative tumour cell lines has been rigorously studied, less is known about the validity of gene transfer and selection in cell lines with a mixed MHC class I phenotype. To address this issue we identified a C26 cell subline that consists of distinct populations of MHC class I (H-2D/K) positive and negative cells. Transient transfection experiments using liposome-based transfer showed a lower transgene expression in MHC class I negative cells. In addition, MHC class I negative cells were more sensitive to antibiotic selection. This led to the generation of fully MHC class I positive cell lines. In contrast to C26 cells, all transfectants were rejected in vivo and induced protection against the parental tumour cells in rechallenge experiments. Tumour cell specificity of the immune response was demonstrated in in vitro cytokine secretion and cytotoxicity assays. Transfectants expressing CD40 ligand and hygromycin phosphotransferase were not more immunogenic than cells expressing hygromycin resistance alone. We suggest that the MHC class I positive phenotype of the C26 transfectants had a bearing on their immunogenicity, because selected MHC class I positive cells were more immunogenic than parental C26 cells and could induce specific anti-tumour immune responses. These data demonstrate that the generation of tumour cell transfectants can lead to the selection of subpopulations that show an altered phenotype compared to the parental cell line and display altered immunogenicity independent of selection marker genes or other immune modulatory genes. Our results show the importance of monitoring gene transfer in the whole tumour cell population, especially for the evaluation of in vivo therapies targeted to heterogeneous tumour cell populations.

Structures in color space

NASA Astrophysics Data System (ADS)

Petrov, Alexander P.

1996-09-01

Classic colorimetry and the traditionally used color space do not represent all perceived colors (for example, browns look dark yellow in colorimetric conditions of observation) so, the specific goal of this work is to suggest another concept of color and to prove that the corresponding set of colors is complete. The idea of our approach attributing color to surface patches (not to the light) immediately ties all the problems of color perception and vision geometry. The equivalence relation in the linear space of light fluxes F established by a procedure of colorimetry gives us a 3D color space H. By definition we introduce a sample (sigma) (surface patch) as a linear mapping (sigma) : L yields H, where L is a subspace of F called the illumination space. A Dedekind structure of partial order can be defined in the set of the samples: two samples (alpha) and (Beta) belong to one chromatic class if ker(alpha) equals ker(Beta) and (alpha) > (Beta) if ker(alpha) ker(Beta) . The maximal elements of this chain create the chromatic class BLACK. There can be given geometrical arguments for L to be 3D and it can be proved that in this case the minimal element of the above Dedekind structure is unique and the corresponding chromatic class is called WHITE containing the samples (omega) such that ker(omega) equals {0} L. Color is defined as mapping C: H yields H and assuming color constancy the complete set of perceived colors is proved to be isomorphic to a subset C of 3 X 3 matrices. This subset is convex, limited and symmetrical with E/2 as the center of symmetry. The problem of metrization of the color space C is discussed and a color metric related to shape, i.e., to vision geometry, is suggested.

Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images

PubMed Central

Yang, Zhengfan; Fang, Jia; Chittuluru, Johnathan; Asturias, Francisco J.; Penczek, Pawel A.

2012-01-01

SUMMARY Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering. PMID:22325773

AOIPS data base management systems support for GARP data sets

NASA Technical Reports Server (NTRS)

Gary, J. P.

1977-01-01

A data base management system is identified, developed to provide flexible access to data sets produced by GARP during its data systems tests. The content and coverage of the data base are defined and a computer-aided, interactive information storage and retrieval system, implemented to facilitate access to user specified data subsets, is described. The computer programs developed to provide the capability were implemented on the highly interactive, minicomputer-based AOIPS and are referred to as the data retrieval system (DRS). Implemented as a user interactive but menu guided system, the DRS permits users to inventory the data tape library and create duplicate or subset data sets based on a user selected window defined by time and latitude/longitude boundaries. The DRS permits users to select, display, or produce formatted hard copy of individual data items contained within the data records.

RNA interference of three up-regulated transcripts associated with insecticide resistance in an imidacloprid resistant population of Leptinotarsa decemlineata.

PubMed

Clements, Justin; Schoville, Sean; Peterson, Nathan; Huseth, Anders S; Lan, Que; Groves, Russell L

2017-01-01

The Colorado potato beetle, Leptinotarsa decemlineata (Say), is a major agricultural pest of potatoes in the Central Sands production region of Wisconsin. Previous studies have shown that populations of L. decemlineata have become resistant to many classes of insecticides, including the neonicotinoid insecticide, imidacloprid. Furthermore, L. decemlineata has multiple mechanisms of resistance to deal with a pesticide insult, including enhanced metabolic detoxification by cytochrome p450s and glutathione S-transferases. With recent advances in the transcriptomic analysis of imidacloprid susceptible and resistant L. decemlineata populations, it is possible to investigate the role of candidate genes involved in imidacloprid resistance. A recently annotated transcriptome analysis of L. decemlineata was obtained from select populations of L. decemlineata collected in the Central Sands potato production region, which revealed a subset of mRNA transcripts constitutively up-regulated in resistant populations. We hypothesize that a portion of the up-regulated transcripts encoding for genes within the resistant populations also encode for pesticide resistance and can be suppressed to re-establish a susceptible phenotype. In this study, a discrete set of three up-regulated targets were selected for RNA interference experiments using a resistant L. decemlineata population. Following the successful suppression of transcripts encoding for a cytochrome p450, a cuticular protein, and a glutathione synthetase protein in a select L. decemlineata population, we observed reductions in measured resistance to imidacloprid that strongly suggest these genes control essential steps in imidacloprid metabolism in these field populations. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Clinical-scale selection and viral transduction of human naïve and central memory CD8+ T cells for adoptive cell therapy of cancer patients.

PubMed

Casati, Anna; Varghaei-Nahvi, Azam; Feldman, Steven Alexander; Assenmacher, Mario; Rosenberg, Steven Aaron; Dudley, Mark Edward; Scheffold, Alexander

2013-10-01

The adoptive transfer of lymphocytes genetically engineered to express tumor-specific antigen receptors is a potent strategy to treat cancer patients. T lymphocyte subsets, such as naïve or central memory T cells, selected in vitro prior to genetic engineering have been extensively investigated in preclinical mouse models, where they demonstrated improved therapeutic efficacy. However, so far, this is challenging to realize in the clinical setting, since good manufacturing practices (GMP) procedures for complex cell sorting and genetic manipulation are limited. To be able to directly compare the immunological attributes and therapeutic efficacy of naïve (T(N)) and central memory (T(CM)) CD8(+) T cells, we investigated clinical-scale procedures for their parallel selection and in vitro manipulation. We also evaluated currently available GMP-grade reagents for stimulation of T cell subsets, including a new type of anti-CD3/anti-CD28 nanomatrix. An optimized protocol was established for the isolation of both CD8(+) T(N) cells (CD4(-)CD62L(+)CD45RA(+)) and CD8(+) T(CM) (CD4(-)CD62L(+)CD45RA(-)) from a single patient. The highly enriched T cell subsets can be efficiently transduced and expanded to large cell numbers, sufficient for clinical applications and equivalent to or better than current cell and gene therapy approaches with unselected lymphocyte populations. The GMP protocols for selection of T(N) and T(CM) we reported here will be the basis for clinical trials analyzing safety, in vivo persistence and clinical efficacy in cancer patients and will help to generate a more reliable and efficacious cellular product.

Normal Weight Obese syndrome: role of single nucleotide polymorphism of IL-1 5Ralpha and MTHFR 677C-->T genes in the relationship between body composition and resting metabolic rate.

PubMed

Di Renzo, L; Bigioni, M; Bottini, F G; Del Gobbo, V; Premrov, M G; Cianci, R; De Lorenzo, A

2006-01-01

We have identified a subset of metabolically obese, but normal weight individuals, with potentially increased risks of developing the metabolic syndrome, despite their normal body mass index. We determined the relationship among body fat distribution, resting metabolic rate (RMR), total body water amount (%TBW), selected gene polymorphism on interleukin-15 receptor-alpha (IL-15Ralpha) and methylenetetrahydrofolate reductase 677C-->T (MTHFR 677C-->T), to distinguish normal weight obese (NWO) from nonobese with a normal metabolic profile and obese individuals. We analysed anthropometric variables, body composition by Dual energy X-ray Absorptiometry (DXA), RMR by indirect calorimetry, %TBW by bioimpedence analysis (BIA), MTHFR 677C-->T and IL-15Ralpha genotypes of 128 clinically healthy Caucasian individuals. We compared a group of female, defined as NWO and characterised by a BMI < or = 25 kg/m(2) and FM > or = 30% with groups of others female, and males, represented by nonobese with a BMI < or = 25 kg/m(2) and FM < or = 30%, and preobese-obese individuals with BMI > or = 25 kg/m(2) and %FM > or = 30%; none of the males was classified as NWO. Significant correlations were found among body fat mass distribution, metabolic variables, percentage of total body water distribution and selected genetic variations. The variables that contributed significantly to the separation of classes were body tissue (Tissue), %TBW, RMR, the volumes of both oxygen (VO2) and carbon dioxide (VCO2). The distribution of MTHFR 677C-->T and IL-15 genotypes was significantly different between classes. Our data highlight that NWO individuals showed a significant relationship between the decrease in the basal metabolism (RMR), body fat mass increasing and total water amount. Possession of wild type homozygotes genotypes regarding IL-15Ralpha cytokine and 677C-->T MTHFR enzyme characterised NWO individuals.

Histone deacetylase 3 regulates the inflammatory gene expression programme of rheumatoid arthritis fibroblast-like synoviocytes

PubMed Central

Angiolilli, Chiara; Kabala, Pawel A; Van Baarsen, Iris M; Ferguson, Bradley S; García, Samuel; Malvar Fernandez, Beatriz; McKinsey, Timothy A; Tak, Paul P; Fossati, Gianluca; Mascagni, Paolo; Baeten, Dominique L; Reedquist, Kris A

2017-01-01

Objectives Non-selective histone deacetylase (HDAC) inhibitors (HDACi) have demonstrated anti-inflammatory properties in both in vitro and in vivo models of rheumatoid arthritis (RA). Here, we investigated the potential contribution of specific class I and class IIb HDACs to inflammatory gene expression in RA fibroblast-like synoviocytes (FLS). Methods RA FLS were incubated with pan-HDACi (ITF2357, givinostat) or selective HDAC1/2i, HDAC3/6i, HDAC6i and HDAC8i. Alternatively, FLS were transfected with HDAC3, HDAC6 or interferon (IFN)-α/β receptor alpha chain (IFNAR1) siRNA. mRNA expression of interleukin (IL)-1β-inducible genes was measured by quantitative PCR (qPCR) array and signalling pathway activation by immunoblotting and DNA-binding assays. Results HDAC3/6i, but not HDAC1/2i and HDAC8i, significantly suppressed the majority of IL-1β-inducible genes targeted by pan-HDACi in RA FLS. Silencing of HDAC3 expression reproduced the effects of HDAC3/6i on gene regulation, contrary to HDAC6-specific inhibition and HDAC6 silencing. Screening of the candidate signal transducers and activators of transcription (STAT)1 transcription factor revealed that HDAC3/6i abrogated STAT1 Tyr701 phosphorylation and DNA binding, but did not affect STAT1 acetylation. HDAC3 activity was required for type I IFN production and subsequent STAT1 activation in FLS. Suppression of type I IFN release by HDAC3/6i resulted in reduced expression of a subset of IFN-dependent genes, including the chemokines CXCL9 and CXCL11. Conclusions Inhibition of HDAC3 in RA FLS largely recapitulates the effects of pan-HDACi in suppressing inflammatory gene expression, including type I IFN production in RA FLS. Our results identify HDAC3 as a potential therapeutic target in the treatment of RA and type I IFN-driven autoimmune diseases. PMID:27457515

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data.

PubMed

Rodríguez, Jorge; Barrera-Animas, Ari Y; Trejo, Luis A; Medina-Pérez, Miguel Angel; Monroy, Raúl

2016-09-29

This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users.

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

PubMed Central

Rodríguez, Jorge; Barrera-Animas, Ari Y.; Trejo, Luis A.; Medina-Pérez, Miguel Angel; Monroy, Raúl

2016-01-01

This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users. PMID:27690054

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.

Changes in Classes of Injury-Related Risks and Consequences of Risk-Level Drinking: a Latent Transition Analysis.

PubMed

Cochran, Gerald; Field, Craig; Caetano, Raul

2015-07-01

Risk-level drinking, drinking and driving, and alcohol-related violence are risk factors that result in injuries. The current study sought to identify which subgroups of patients experience the most behavioral change following a brief intervention. A secondary analysis of data from a brief alcohol intervention study was conducted. The sample (N = 664) includes at-risk drinkers who experienced an injury and were admitted for care to a Level 1 trauma center. Injury-related items from the Short Inventory of Problems+6 were used to perform a latent transition analysis to describe class transitions participants experienced following discharge. Four classes emerged for the year before and after the current injury. Most individuals transitioned from higher-risk classes into those with lower risk. Some participants maintained risky profiles, and others increased risks and consequences. Drinking and driving remained a persistent problem among the study participants. Although a large portion of intervention recipients improved risks and consequences of alcohol use following discharge, more intensive intervention services may be needed for a subset of patients who showed little or no improvement.

Land cover heterogeneity and soil respiration in a west Greenland tundra landscape

NASA Astrophysics Data System (ADS)

Bradley-Cook, J. I.; Burzynski, A.; Hammond, C. R.; Virginia, R. A.

2011-12-01

Multiple direct and indirect pathways underlie the association between land cover classification, temperature and soil respiration. Temperature is a main control of the biological processes that constitute soil respiration, yet the effect of changing atmospheric temperatures on soil carbon flux is unresolved. This study examines associations amongst land cover, soil carbon characteristics, soil respiration, and temperature in an Arctic tundra landscape in western Greenland. We used a 1.34 meter resolution multi-spectral WorldView2 satellite image to conduct an unsupervised multi-staged ISODATA classification to characterize land cover heterogeneity. The four band image was taken on July 10th, 2010, and captures an 18 km by 15 km area in the vicinity of Kangerlussuaq. The four major terrestrial land cover classes identified were: shrub-dominated, graminoid-dominated, mixed vegetation, and bare soil. The bare soil class was comprised of patches where surface soil has been deflated by wind and ridge-top fellfield. We hypothesize that soil respiration and soil carbon storage are associated with land cover classification and temperature. We set up a hierarchical field sampling design to directly observe spatial variation between and within land cover classes along a 20 km temperature gradient extending west from Russell Glacier on the margin of the Greenland Ice Sheet. We used the land cover classification map and ground verification to select nine sites, each containing patches of the four land cover classes. Within each patch we collected soil samples from a 50 cm pit, quantified vegetation, measured active layer depth and determined landscape characteristics. From a subset of field sites we collected additional 10 cm surface soil samples to estimate soil heterogeneity within patches and measured soil respiration using a LiCor 8100 Infrared Gas Analyzer. Soil respiration rates varied with land cover classes, with values ranging from 0.2 mg C/m^2/hr in the bare soil class to over 5 mg C/m^2/hr in the graminoid-dominated class. These findings suggest that shifts in land cover vegetation types, especially soil and vegetation loss (e.g. from wind deflation), can alter landscape soil respiration. We relate soil respiration measurements to soil, vegetation, and permafrost characteristics to understand how ecosystem properties and processes vary at the landscape scale. A long-term goal of this research is to develop a spatially explicit model of soil organic matter, soil respiration, and temperature sensitivity of soil carbon dynamics for a western Greenland permafrost tundra ecosystems.

Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

PubMed

Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

Longitudinal analyses of correlated response efficiencies of fillet traits in Nile tilapia.

PubMed

Turra, E M; Fernandes, A F A; de Alvarenga, E R; Teixeira, E A; Alves, G F O; Manduca, L G; Murphy, T W; Silva, M A

2018-03-01

Recent studies with Nile tilapia have shown divergent results regarding the possibility of selecting on morphometric measurements to promote indirect genetic gains in fillet yield (FY). The use of indirect selection for fillet traits is important as these traits are only measurable after harvesting. Random regression models are a powerful tool in association studies to identify the best time point to measure and select animals. Random regression models can also be applied in a multiple trait approach to analyze indirect response to selection, which would avoid the need to sacrifice candidate fish. Therefore, the aim of this study was to investigate the genetic relationships between several body measurements, weight and fillet traits throughout the growth period and to evaluate the possibility of indirect selection for fillet traits in Nile tilapia. Data were collected from 2042 fish and was divided into two subsets. The first subset was used to estimate genetic parameters, including the permanent environmental effect for BW and body measurements (8758 records for each body measurement, as each fish was individually weighed and measured a maximum of six times). The second subset (2042 records for each trait) was used to estimate genetic correlations and heritabilities, which enabled the calculation of correlated response efficiencies between body measurements and the fillet traits. Heritability estimates across ages ranged from 0.05 to 0.5 for height, 0.02 to 0.48 for corrected length (CL), 0.05 to 0.68 for width, 0.08 to 0.57 for fillet weight (FW) and 0.12 to 0.42 for FY. All genetic correlation estimates between body measurements and FW were positive and strong (0.64 to 0.98). The estimates of genetic correlation between body measurements and FY were positive (except for CL at some ages), but weak to moderate (-0.08 to 0.68). These estimates resulted in strong and favorable correlated response efficiencies for FW and positive, but moderate for FY. These results indicate the possibility of achieving indirect genetic gains for FW and by selecting for morphometric traits, but low efficiency for FY when compared with direct selection.

Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ochilov, S.; Alam, M. S.; Bal, A.

2006-05-01

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

Dual MAPK inhibition is an effective therapeutic strategy for a subset of class II BRAF mutant melanoma.

PubMed

Dankner, Matthew; Lajoie, Mathieu; Moldoveanu, Dan; Nguyen, Tan-Trieu; Savage, Paul; Rajkumar, Shivshankari; Huang, Xiu; Lvova, Maria; Protopopov, Alexei; Vuzman, Dana; Hogg, David; Park, Morag; Guiot, Marie-Christine; Petrecca, Kevin; Mihalcioiu, Catalin; Watson, Ian R; Siegel, Peter M; Rose, April A N

2018-06-14

Dual MAPK pathway inhibition (dMAPKi) with BRAF and MEK inhibitors improves survival in BRAF V600E/K mutant melanoma, but the efficacy of dMAPKi in non-V600 BRAF mutant tumors is poorly understood. We sought to characterize the responsiveness of class II (enhanced kinase activity, dimerization dependent) BRAF mutant melanoma to dMAPKi. Tumors from patients with BRAF WT, V600E (class I) and L597S (class II) metastatic melanoma were used to generate patient-derived-xenografts (PDX). We assembled a panel of melanoma cell lines with class IIa (activation segment) or IIb (p-loop) mutations and compared these to wild-type or V600E/K BRAF mutant cells. Cell lines and PDXs were treated with BRAFi (vemurafenib, dabrafenib, encorafenib, LY3009120), MEKi (cobimetinib, trametinib, binimetinib) or the combination. We identified two patients with BRAF L597S metastatic melanoma who were treated with dMAPKi. BRAFi impaired MAPK signalling and cell growth in class I and II BRAF mutant cells. dMAPKi was more effective than either single MAPKi at inhibiting cell growth in all class II BRAF mutant cells tested. dMAPKi caused tumor regression in two melanoma PDXs with class II BRAF mutations, and prolonged survival of mice with class II BRAF mutant melanoma brain metastases. Two patients with BRAF L597S mutant melanoma clinically responded to dMAPKi. Class II BRAF mutant melanoma are growth inhibited by dMAPKi. Responses to dMAPKi have been observed in two patients with class II BRAF mutant melanoma. This data provides rationale for clinical investigation of dMAPKi in patients with class II BRAF mutant metastatic melanoma. Copyright ©2018, American Association for Cancer Research.

Efficient one-cycle affinity selection of binding proteins or peptides specific for a small-molecule using a T7 phage display pool.

PubMed

Takakusagi, Yoichi; Kuramochi, Kouji; Takagi, Manami; Kusayanagi, Tomoe; Manita, Daisuke; Ozawa, Hiroko; Iwakiri, Kanako; Takakusagi, Kaori; Miyano, Yuka; Nakazaki, Atsuo; Kobayashi, Susumu; Sugawara, Fumio; Sakaguchi, Kengo

2008-11-15

Here, we report an efficient one-cycle affinity selection using a natural-protein or random-peptide T7 phage pool for identification of binding proteins or peptides specific for small-molecules. The screening procedure involved a cuvette type 27-MHz quartz-crystal microbalance (QCM) apparatus with introduction of self-assembled monolayer (SAM) for a specific small-molecule immobilization on the gold electrode surface of a sensor chip. Using this apparatus, we attempted an affinity selection of proteins or peptides against synthetic ligand for FK506-binding protein (SLF) or irinotecan (Iri, CPT-11). An affinity selection using SLF-SAM and a natural-protein T7 phage pool successfully detected FK506-binding protein 12 (FKBP12)-displaying T7 phage after an interaction time of only 10 min. Extensive exploration of time-consuming wash and/or elution conditions together with several rounds of selection was not required. Furthermore, in the selection using a 15-mer random-peptide T7 phage pool and subsequent analysis utilizing receptor ligand contact (RELIC) software, a subset of SLF-selected peptides clearly pinpointed several amino-acid residues within the binding site of FKBP12. Likewise, a subset of Iri-selected peptides pinpointed part of the positive amino-acid region of residues from the Iri-binding site of the well-known direct targets, acetylcholinesterase (AChE) and carboxylesterase (CE). Our findings demonstrate the effectiveness of this method and general applicability for a wide range of small-molecules.

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

The Isolation and Enrichment of Large Numbers of Highly Purified Mouse Spleen Dendritic Cell Populations and Their In Vitro Equivalents.

PubMed

Vremec, David

2016-01-01

Dendritic cells (DCs) form a complex network of cells that initiate and orchestrate immune responses against a vast array of pathogenic challenges. Developmentally and functionally distinct DC subtypes differentially regulate T-cell function. Importantly it is the ability of DC to capture and process antigen, whether from pathogens, vaccines, or self-components, and present it to naive T cells that is the key to their ability to initiate an immune response. Our typical isolation procedure for DC from murine spleen was designed to efficiently extract all DC subtypes, without bias and without alteration to their in vivo phenotype, and involves a short collagenase digestion of the tissue, followed by selection for cells of light density and finally negative selection for DC. The isolation procedure can accommodate DC numbers that have been artificially increased via administration of fms-like tyrosine kinase 3 ligand (Flt3L), either directly through a series of subcutaneous injections or by seeding with an Flt3L secreting murine melanoma. Flt3L may also be added to bone marrow cultures to produce large numbers of in vitro equivalents of the spleen DC subsets. Total DC, or their subsets, may be further purified using immunofluorescent labeling and flow cytometric cell sorting. Cell sorting may be completely bypassed by separating DC subsets using a combination of fluorescent antibody labeling and anti-fluorochrome magnetic beads. Our procedure enables efficient separation of the distinct DC subsets, even in cases where mouse numbers or flow cytometric cell sorting time is limiting.

G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies

PubMed Central

Wang, Miaoyan; Jakobsdottir, Johanna; Smith, Albert V.; McPeek, Mary Sara

2017-01-01

In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study’s sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G-STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G-STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20–40% over the next best strategy, while maintaining correct type 1 error. G-STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G-STRATEGY to data on HDL and LDL from the AGES-Reykjavik and REFINE-Reykjavik studies, in which G-STRATEGY is able to closely-approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals. PMID:27256766

Studies of a Latent Class Signal Detection Model for Constructed Response Scoring II: Incomplete and Hierarchical Designs. Research Report. ETS RR-10-08

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2010-01-01

A basic consideration in large-scale assessments that use constructed response (CR) items, such as essays, is how to allocate the essays to the raters that score them. Designs that are used in practice are incomplete, in that each essay is scored by only a subset of the raters, and also unbalanced, in that the number of essays scored by each rater…

Ligation of CD8α on human natural killer cells prevents activation-induced apoptosis and enhances cytolytic activity

PubMed Central

Addison, Elena G; North, Janet; Bakhsh, Ismail; Marden, Chloe; Haq, Sumaira; Al-Sarraj, Samia; Malayeri, Reza; Wickremasinghe, R Gitendra; Davies, Jeffrey K; Lowdell, Mark W

2005-01-01

It has been previously shown that the subset of human natural killer (NK) cells which express CD8 in a homodimeric α/α form are more cytotoxic than their CD8– counterparts but the mechanisms behind this differential cytolytic activity remained unknown. Target cell lysis by CD8– NK cells is associated with high levels of effector cell apoptosis, which is in contrast to the significantly lower levels found in the CD8α+ cells after lysis of the same targets. We report that cross-linking of the CD8α chains on NK cells induces rapid rises in intracellular Ca2+ and increased expression of CD69 at the cell surface by initiating the influx of extracellular Ca2+ ions. We demonstrate that secretion of cytolytic enzymes initiates NK-cell apoptosis from which CD8α+ NK cells are protected by an influx of exogenous calcium following ligation of CD8 on the NK-cell surface. This ligation is through interaction with fellow NK cells in the cell conjugate and can occur when the target cells lack major histocompatibility complex (MHC) Class I expression. Protection from apoptosis is blocked by preincubation of the NK cells with anti-MHC Class I antibody. Thus, in contrast to the CD8– subset, CD8α+ NK cells are capable of sequential lysis of multiple target cells. PMID:16236125

The soluble pattern recognition receptor PTX3 links humoral innate and adaptive immune responses by helping marginal zone B cells

PubMed Central

Sintes, Jordi; Polentarutti, Nadia; Walland, A. Cooper; Yeiser, John R.; Cunha, Cristina; Lacerda, João F.; Salvatori, Giovanni; Blander, J. Magarian

2016-01-01

Pentraxin 3 (PTX3) is a fluid-phase pattern recognition receptor of the humoral innate immune system with ancestral antibody-like properties but unknown antibody-inducing function. In this study, we found binding of PTX3 to splenic marginal zone (MZ) B cells, an innate-like subset of antibody-producing lymphocytes strategically positioned at the interface between the circulation and the adaptive immune system. PTX3 was released by a subset of neutrophils that surrounded the splenic MZ and expressed an immune activation–related gene signature distinct from that of circulating neutrophils. Binding of PTX3 promoted homeostatic production of IgM and class-switched IgG antibodies to microbial capsular polysaccharides, which decreased in PTX3-deficient mice and humans. In addition, PTX3 increased IgM and IgG production after infection with blood-borne encapsulated bacteria or immunization with bacterial carbohydrates. This immunogenic effect stemmed from the activation of MZ B cells through a neutrophil-regulated pathway that elicited class switching and plasmablast expansion via a combination of T cell–independent and T cell–dependent signals. Thus, PTX3 may bridge the humoral arms of the innate and adaptive immune systems by serving as an endogenous adjuvant for MZ B cells. This property could be harnessed to develop more effective vaccines against encapsulated pathogens. PMID:27621420

Feature Selection and Pedestrian Detection Based on Sparse Representation.

PubMed

Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

2015-01-01

Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony.

Racial ideology and explanations for health inequalities among middle-class whites.

PubMed

Muntaner, C; Nagoshi, C; Diala, C

2001-01-01

Middle-class whites' explanations for racial inequalities in health can have a profound impact on the type of questions addressed in epidemiology and public health research. These explanations also constitute a subset of white racial ideology (i.e., racism) that in itself powerfully affects the health of non-whites. This study begins to examine the nature of attributions for racial inequalities in health among university students who by definition are likely to be involved in the research, policy, and service professions (the upper middle class). Investigation of the degree to which middle-class whites attribute racial inequalities in cardiovascular health (between themselves and African Americans, American Indians, or Asian Americans) to biological, social, or lifestyle factors reveals that whites tend to attribute their own health to lifestyle choice and to biology rather than to social factors. These results suggest that contemporary middle-class whites' "self-serving" explanations for racial inequalities in health are comprised of two beliefs: implicit biologism (race is an attribute of organisms rather than a social relation) and liberal belief in self-determination, choice, and individual responsibility--some of the core lay beliefs of the worldview that sustains neoliberal capitalism. Contemporary white middle-class explanations for racial inequalities in health appear to include assumptions that justify class inequality. Liberal approaches to racism in public health are bound to miss a key component of racial ideology that is currently used to justify racial and class inequalities.

VizieR Online Data Catalog: RR Lyraes in SDSS stripe 82 (Watkins+, 2009)

NASA Astrophysics Data System (ADS)

Watkins, L. L.; Evans, N. W.; Belokurov, V.; Smith, M. C.; Hewett, P. C.; Bramich, D. M.; Gilmore, G. F.; Irwin, M. J.; Vidrih, S.; Wyrzykowski, L.; Zucker, D. B.

2015-10-01

In this paper, we select first the variable objects in Stripe 82 and then the subset of RR Lyraes, using the Bramich et al. (2008MNRAS.386..887B, Cat. V/141) light-motion curve catalogue (LMCC) and HLC. We make a selection of the variable objects and an identification of RR Lyrae stars. (2 data files).

Effect of a Surprising Downward Shift in Reinforcer Value on Stimulus Over-Selectivity in a Simultaneous Discrimination Procedure

ERIC Educational Resources Information Center

Reynolds, Gemma; Reed, Phil

2013-01-01

Stimulus over-selectivity refers to the phenomenon whereby behavior is controlled by a subset of elements in the environment at the expense of other equally salient aspects of the environment. The experiments explored whether this cue interference effect was reduced following a surprising downward shift in reinforcer value. Experiment 1 revealed…

Using Parental Profiles to Predict Membership in a Subset of College Students Experiencing Excessive Alcohol Consequences: Findings From a Longitudinal Study

PubMed Central

Varvil-Weld, Lindsey; Mallett, Kimberly A.; Turrisi, Rob; Abar, Caitlin C.

2012-01-01

Objective: Previous research identified a high-risk subset of college students experiencing a disproportionate number of alcohol-related consequences at the end of their first year. With the goal of identifying pre-college predictors of membership in this high-risk subset, the present study used a prospective design to identify latent profiles of student-reported maternal and paternal parenting styles and alcohol-specific behaviors and to determine whether these profiles were associated with membership in the high-risk consequences subset. Method: A sample of randomly selected 370 incoming first-year students at a large public university reported on their mothers’ and fathers’ communication quality, monitoring, approval of alcohol use, and modeling of drinking behaviors and on consequences experienced across the first year of college. Results: Students in the high-risk subset comprised 15.5% of the sample but accounted for almost half (46.6%) of the total consequences reported by the entire sample. Latent profile analyses identified four parental profiles: positive pro-alcohol, positive anti-alcohol, negative mother, and negative father. Logistic regression analyses revealed that students in the negative-father profile were at greatest odds of being in the high-risk consequences subset at a follow-up assessment 1 year later, even after drinking at baseline was controlled for. Students in the positive pro-alcohol profile also were at increased odds of being in the high-risk subset, although this association was attenuated after baseline drinking was controlled for. Conclusions: These findings have important implications for the improvement of existing parent- and individual-based college student drinking interventions designed to reduce alcohol-related consequences. PMID:22456248

Using parental profiles to predict membership in a subset of college students experiencing excessive alcohol consequences: findings from a longitudinal study.

PubMed

Varvil-Weld, Lindsey; Mallett, Kimberly A; Turrisi, Rob; Abar, Caitlin C

2012-05-01

Previous research identified a high-risk subset of college students experiencing a disproportionate number of alcohol-related consequences at the end of their first year. With the goal of identifying pre-college predictors of membership in this high-risk subset, the present study used a prospective design to identify latent profiles of student-reported maternal and paternal parenting styles and alcohol-specific behaviors and to determine whether these profiles were associated with membership in the high-risk consequences subset. A sample of randomly selected 370 incoming first-year students at a large public university reported on their mothers' and fathers' communication quality, monitoring, approval of alcohol use, and modeling of drinking behaviors and on consequences experienced across the first year of college. Students in the high-risk subset comprised 15.5% of the sample but accounted for almost half (46.6%) of the total consequences reported by the entire sample. Latent profile analyses identified four parental profiles: positive pro-alcohol, positive anti-alcohol, negative mother, and negative father. Logistic regression analyses revealed that students in the negative-father profile were at greatest odds of being in the high-risk consequences subset at a follow-up assessment 1 year later, even after drinking at baseline was controlled for. Students in the positive pro-alcohol profile also were at increased odds of being in the high-risk subset, although this association was attenuated after baseline drinking was controlled for. These findings have important implications for the improvement of existing parent- and individual-based college student drinking interventions designed to reduce alcohol-related consequences.

Limited MHC class I intron 2 repertoire variation in bonobos.

PubMed

de Groot, Natasja G; Heijmans, Corrine M C; Helsen, Philippe; Otting, Nel; Pereboom, Zjef; Stevens, Jeroen M G; Bontrop, Ronald E

2017-10-01

Common chimpanzees (Pan troglodytes) experienced a selective sweep, probably caused by a SIV-like virus, which targeted their MHC class I repertoire. Based on MHC class I intron 2 data analyses, this selective sweep took place about 2-3 million years ago. As a consequence, common chimpanzees have a skewed MHC class I repertoire that is enriched for allotypes that are able to recognise conserved regions of the SIV proteome. The bonobo (Pan paniscus) shared an ancestor with common chimpanzees approximately 1.5 to 2 million years ago. To investigate whether the signature of this selective sweep is also detectable in bonobos, the MHC class I gene repertoire of two bonobo panels comprising in total 29 animals was investigated by Sanger sequencing. We identified 14 Papa-A, 20 Papa-B and 11 Papa-C alleles, of which eight, five and eight alleles, respectively, have not been reported previously. Within this pool of MHC class I variation, we recovered only 2 Papa-A, 3 Papa-B and 6 Papa-C intron 2 sequences. As compared to humans, bonobos appear to have an even more diminished MHC class I intron 2 lineage repertoire than common chimpanzees. This supports the notion that the selective sweep may have predated the speciation of common chimpanzees and bonobos. The further reduction of the MHC class I intron 2 lineage repertoire observed in bonobos as compared to the common chimpanzee may be explained by a founding effect or other subsequent selective processes.

Model selection for anomaly detection

NASA Astrophysics Data System (ADS)

Burnaev, E.; Erofeev, P.; Smolyakov, D.

2015-12-01

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.

A framework for identifying tailored subsets of climate projections for impact and adaptation studies

NASA Astrophysics Data System (ADS)

Vidal, Jean-Philippe; Hingray, Benoît

2014-05-01

In order to better understand the uncertainties in the climate of the next decades, an increasingly large number of increasingly diverse climate projections is being produced by the climate research community through coordinated initiatives (e.g., CMIP5, CORDEX), but also through more specific experiments at both the global scale (perturbed parameter ensembles) and the regional-to-local scale (empirical statistical downscaling ensembles). When significant efforts are put into making such projections available online, very few works focus on how to make such an enormous amount of information actually usable by the impact and adaptation community. Climate services should therefore include guidelines and recommendations for identifying subsets of climate projections that would have (1) a size manageable by downstream modelling approaches and (2) the relevant properties for informing adaptation strategies. This works proposes a generic framework for identifying tailored subsets of climate projections that would meet both the objectives and the constraints of a specific impact / adaptation study in a typical top-down approach. This decision framework builds on two main preliminary tasks that lead to critical choices in the selection strategy: (1) understanding the requirements of the specific impact / adaptation study, and (2) characterizing the (downscaled) climate projections dataset available. An impact / adaptation study has two types of requirements. First, the study may aim at various outcomes for a given climate-related feature: the best estimate of the future, the range of possible futures, a set of representative futures, or a statistically interpretable ensemble of futures. Second, impact models may come with specific constraints on climate input variables, like spatio-temporal and between-variables coherence. Additionally, when concurrent impact models are used, the most restrictive constraints have to be considered in order to be able to assess the uncertainty associated from this modelling step. Besides, the climate projection dataset available for a given study has several characteristics that will heavily condition the type of conclusions that can be reached. Indeed, the dataset at hand may or not sample different types of uncertainty (socio-economic, structural, parametric, along with internal variability). Moreover, these types are present at different steps in the well-known cascade of uncertainty, from the emission / concentration scenarios and the global climate to the regional-to-local climate. Critical choices for the selection are therefore conditioned on all features above. The type of selection (picking out, culling, or statistical sampling) is closely related to the study objectives and the uncertainty types present in the dataset. Moreover, grounds for picking out or culling projections may stem from global, regional or feature-specific present-day performance, representativeness, or covered range. An example use of this framework is a hierarchical selection for 3 classes of impact models among 3000 transient climate projections from different runs of 4 GCMs, statistically downscaled by 3 probabilistic methods, and made available for an integrated water resource adaptation study in the Durance catchment (southern French Alps). This work is part of the GICC R2D2-20501 project (Risk, water Resources and sustainable Development of the Durance catchment in 2050) and the EU FP7 COMPLEX2 project (Knowledge Based Climate Mitigation Systems for a Low Carbon Economy).

Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries †

PubMed Central

Gutiérrez-López-Franca, Carlos; Hervás, Ramón; Johnson, Esperanza

2018-01-01

This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the Extended Body-Angles Algorithm, which is able to analyze activities using only a single key sample. This system allows to select, for each considered activity, which are its relevant joints, which makes it possible to monitor the body of the user selecting only a subset of the same. But this feature of the system has both advantages and disadvantages. As a consequence, in the past we had some difficulties with the recognition of activities that only have a small subset of the joints of the body as relevant. The goal of this work, therefore, is to analyze the effect produced by the application of several strategies on the results of an activity recognition system based on skeletal tracking joint oriented devices. Strategies that we applied with the purpose of improve the recognition rates of the activities with a small subset of relevant joints. Through the results of this work, we aim to give the scientific community some first indications about which considered strategy is better. PMID:29789478

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

PubMed Central

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

2015-01-01

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

A tool for selecting SNPs for association studies based on observed linkage disequilibrium patterns.

PubMed

De La Vega, Francisco M; Isaac, Hadar I; Scafe, Charles R

2006-01-01

The design of genetic association studies using single-nucleotide polymorphisms (SNPs) requires the selection of subsets of the variants providing high statistical power at a reasonable cost. SNPs must be selected to maximize the probability that a causative mutation is in linkage disequilibrium (LD) with at least one marker genotyped in the study. The HapMap project performed a genome-wide survey of genetic variation with about a million SNPs typed in four populations, providing a rich resource to inform the design of association studies. A number of strategies have been proposed for the selection of SNPs based on observed LD, including construction of metric LD maps and the selection of haplotype tagging SNPs. Power calculations are important at the study design stage to ensure successful results. Integrating these methods and annotations can be challenging: the algorithms required to implement these methods are complex to deploy, and all the necessary data and annotations are deposited in disparate databases. Here, we present the SNPbrowser Software, a freely available tool to assist in the LD-based selection of markers for association studies. This stand-alone application provides fast query capabilities and swift visualization of SNPs, gene annotations, power, haplotype blocks, and LD map coordinates. Wizards implement several common SNP selection workflows including the selection of optimal subsets of SNPs (e.g. tagging SNPs). Selected SNPs are screened for their conversion potential to either TaqMan SNP Genotyping Assays or the SNPlex Genotyping System, two commercially available genotyping platforms, expediting the set-up of genetic studies with an increased probability of success.

Channel and feature selection in multifunction myoelectric control.

PubMed

Khushaba, Rami N; Al-Jumaily, Adel

2007-01-01

Real time controlling devices based on myoelectric singles (MES) is one of the challenging research problems. This paper presents a new approach to reduce the computational cost of real time systems driven by Myoelectric signals (MES) (a.k.a Electromyography--EMG). The new approach evaluates the significance of feature/channel selection on MES pattern recognition. Particle Swarm Optimization (PSO), an evolutionary computational technique, is employed to search the feature/channel space for important subsets. These important subsets will be evaluated using a multilayer perceptron trained with back propagation neural network (BPNN). Practical results acquired from tests done on six subjects' datasets of MES signals measured in a noninvasive manner using surface electrodes are presented. It is proved that minimum error rates can be achieved by considering the correct combination of features/channels, thus providing a feasible system for practical implementation purpose for rehabilitation of patients.

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2003-01-01

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Scalable amplification of strand subsets from chip-synthesized oligonucleotide libraries

PubMed Central

Schmidt, Thorsten L.; Beliveau, Brian J.; Uca, Yavuz O.; Theilmann, Mark; Da Cruz, Felipe; Wu, Chao-Ting; Shih, William M.

2015-01-01

Synthetic oligonucleotides are the main cost factor for studies in DNA nanotechnology, genetics and synthetic biology, which all require thousands of these at high quality. Inexpensive chip-synthesized oligonucleotide libraries can contain hundreds of thousands of distinct sequences, however only at sub-femtomole quantities per strand. Here we present a selective oligonucleotide amplification method, based on three rounds of rolling-circle amplification, that produces nanomole amounts of single-stranded oligonucleotides per millilitre reaction. In a multistep one-pot procedure, subsets of hundreds or thousands of single-stranded DNAs with different lengths can selectively be amplified and purified together. These oligonucleotides are used to fold several DNA nanostructures and as primary fluorescence in situ hybridization probes. The amplification cost is lower than other reported methods (typically around US$ 20 per nanomole total oligonucleotides produced) and is dominated by the use of commercial enzymes. PMID:26567534

Optimizing an Actuator Array for the Control of Multi-Frequency Noise in Aircraft Interiors

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.

1997-01-01

Techniques developed for selecting an optimized actuator array for interior noise reduction at a single frequency are extended to the multi-frequency case. Transfer functions for 64 actuators were obtained at 5 frequencies from ground testing the rear section of a fully trimmed DC-9 fuselage. A single loudspeaker facing the left side of the aircraft was the primary source. A combinatorial search procedure (tabu search) was employed to find optimum actuator subsets of from 2 to 16 actuators. Noise reduction predictions derived from the transfer functions were used as a basis for evaluating actuator subsets during optimization. Results indicate that it is necessary to constrain actuator forces during optimization. Unconstrained optimizations selected actuators which require unrealistically large forces. Two methods of constraint are evaluated. It is shown that a fast, but approximate, method yields results equivalent to an accurate, but computationally expensive, method.

Interactive radiographic image retrieval system.

PubMed

Kundu, Malay Kumar; Chowdhury, Manish; Das, Sudeb

2017-02-01

Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Technical Performance Scores are strongly associated with early mortality, postoperative adverse events, and intensive care unit length of stay-analysis of consecutive discharges for 2 years.

PubMed

Nathan, Meena; Karamichalis, John; Liu, Hua; Gauvreau, Kimberley; Colan, Steven; Saia, Matthew; Pigula, Frank; Fynn-Thompson, Francis; Emani, Sitaram; Baird, Christopher; Mayer, John E; del Nido, Pedro J

2014-01-01

Previous work in our institution has indicated that the Technical Performance Score (TPS) is highly associated with early outcomes in select subsets of procedures and age groups. We hypothesized that the TPS could predict early outcomes in a wide range of diagnoses and age groups. Consecutive patients discharged from January 2011 to March 2013 were prospectively evaluated. The TPS was assigned according to the discharge echocardiographic findings and the need for reinterventions in the anatomic area of interest. Case complexity was determined using Risk Adjustment for Congenital Heart Surgery (RACHS-1) categories. Early mortality and postoperative adverse events were recorded. Relationships between the TPS and outcomes were assessed after adjusting for the baseline patient characteristics. The median age of the 1926 patients was 1.8 years (range, 0 days to 68 years). Bypass was used in 1740 (90%); 322 (17%) were neonates, 520 (27%) infants, 873 (45%) children, 211 (11%) adults. TPS was class 1 (optimal) in 956 (50%), class 2 (adequate) in 584 (30%), and class 3 (inadequate) in 226 (12%); 160 patients (8%) could not be scored. A total of 51 early deaths (2.6%) and 111 adverse events (5.7%) occurred. On univariate analysis, age, RACHS-1 category, and TPS were significantly associated with mortality and the occurrence of adverse events. On multivariate modeling, class 3 (inadequate) TPS was strongly associated with mortality (odds ratio, 16.9; 95% confidence interval, 6.7-42.9; P < .001), adverse events (odds ratio, 6.9; 95% confidence interval, 4.1-11.6; P < .001), and postoperative intensive care unit length of stay (coefficient, 2.3; 95% confidence interval, 2.0-2.6; P < .001) after adjusting for other covariates. The TPS is strongly associated with early outcomes across a wide range of ages and disease complexity and can serve as important tool for self-assessment and quality improvement. Copyright © 2014 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.

Advancements in automated tissue segmentation pipeline for contrast-enhanced CT scans of adult and pediatric patients

NASA Astrophysics Data System (ADS)

Somasundaram, Elanchezhian; Kaufman, Robert; Brady, Samuel

2017-03-01

The development of a random forests machine learning technique is presented for fully-automated neck, chest, abdomen, and pelvis tissue segmentation of CT images using Trainable WEKA (Waikato Environment for Knowledge Analysis) Segmentation (TWS) plugin of FIJI (ImageJ, NIH). The use of a single classifier model to segment six tissue classes (lung, fat, muscle, solid organ, blood/contrast agent, bone) in the CT images is studied. An automated unbiased scheme to sample pixels from the training images and generate a balanced training dataset over the seven classes is also developed. Two independent training datasets are generated from a pool of 4 adult (>55 kg) and 3 pediatric patients (<=55 kg) with 7 manually contoured slices for each patient. Classifier training investigated 28 image filters comprising a total of 272 features. Highly correlated and insignificant features are eliminated using Correlated Feature Subset (CFS) selection with Best First Search (BFS) algorithms in WEKA. The 2 training models (from the 2 training datasets) had 74 and 71 input training features, respectively. The study also investigated the effect of varying the number of trees (25, 50, 100, and 200) in the random forest algorithm. The performance of the 2 classifier models are evaluated on inter-patient intra-slice, intrapatient inter-slice and inter-patient inter-slice test datasets. The Dice similarity coefficients (DSC) and confusion matrices are used to understand the performance of the classifiers across the tissue segments. The effect of number of features in the training input on the performance of the classifiers for tissue classes with less than optimal DSC values is also studied. The average DSC values for the two training models on the inter-patient intra-slice test data are: 0.98, 0.89, 0.87, 0.79, 0.68, and 0.84, for lung, fat, muscle, solid organ, blood/contrast agent, and bone, respectively. The study demonstrated that a robust segmentation accuracy for lung, muscle and fat tissue classes. For solid-organ, blood/contrast and bone, the performance of the segmentation pipeline improved significantly by using the advanced capabilities of WEKA. However, further improvements are needed to reduce the noise in the segmentation.

GPU-completeness: theory and implications

NASA Astrophysics Data System (ADS)

Lin, I.-Jong

2011-01-01

This paper formalizes a major insight into a class of algorithms that relate parallelism and performance. The purpose of this paper is to define a class of algorithms that trades off parallelism for quality of result (e.g. visual quality, compression rate), and we propose a similar method for algorithmic classification based on NP-Completeness techniques, applied toward parallel acceleration. We will define this class of algorithm as "GPU-Complete" and will postulate the necessary properties of the algorithms for admission into this class. We will also formally relate his algorithmic space and imaging algorithms space. This concept is based upon our experience in the print production area where GPUs (Graphic Processing Units) have shown a substantial cost/performance advantage within the context of HPdelivered enterprise services and commercial printing infrastructure. While CPUs and GPUs are converging in their underlying hardware and functional blocks, their system behaviors are clearly distinct in many ways: memory system design, programming paradigms, and massively parallel SIMD architecture. There are applications that are clearly suited to each architecture: for CPU: language compilation, word processing, operating systems, and other applications that are highly sequential in nature; for GPU: video rendering, particle simulation, pixel color conversion, and other problems clearly amenable to massive parallelization. While GPUs establishing themselves as a second, distinct computing architecture from CPUs, their end-to-end system cost/performance advantage in certain parts of computation inform the structure of algorithms and their efficient parallel implementations. While GPUs are merely one type of architecture for parallelization, we show that their introduction into the design space of printing systems demonstrate the trade-offs against competing multi-core, FPGA, and ASIC architectures. While each architecture has its own optimal application, we believe that the selection of architecture can be defined in terms of properties of GPU-Completeness. For a welldefined subset of algorithms, GPU-Completeness is intended to connect the parallelism, algorithms and efficient architectures into a unified framework to show that multiple layers of parallel implementation are guided by the same underlying trade-off.

Entanglement and nonclassical properties of hypergraph states

NASA Astrophysics Data System (ADS)

Gühne, Otfried; Cuquet, Martí; Steinhoff, Frank E. S.; Moroder, Tobias; Rossi, Matteo; Bruß, Dagmar; Kraus, Barbara; Macchiavello, Chiara

2014-08-01

Hypergraph states are multiqubit states that form a subset of the locally maximally entangleable states and a generalization of the well-established notion of graph states. Mathematically, they can conveniently be described by a hypergraph that indicates a possible generation procedure of these states; alternatively, they can also be phrased in terms of a nonlocal stabilizer formalism. In this paper, we explore the entanglement properties and nonclassical features of hypergraph states. First, we identify the equivalence classes under local unitary transformations for up to four qubits, as well as important classes of five- and six-qubit states, and determine various entanglement properties of these classes. Second, we present general conditions under which the local unitary equivalence of hypergraph states can simply be decided by considering a finite set of transformations with a clear graph-theoretical interpretation. Finally, we consider the question of whether hypergraph states and their correlations can be used to reveal contradictions with classical hidden-variable theories. We demonstrate that various noncontextuality inequalities and Bell inequalities can be derived for hypergraph states.

Identifiability Results for Several Classes of Linear Compartment Models.

PubMed

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

Odor-identity dependent motor programs underlie behavioral responses to odors

PubMed Central

Jung, Seung-Hye; Hueston, Catherine; Bhandawat, Vikas

2015-01-01

All animals use olfactory information to perform tasks essential to their survival. Odors typically activate multiple olfactory receptor neuron (ORN) classes and are therefore represented by the patterns of active ORNs. How the patterns of active ORN classes are decoded to drive behavior is under intense investigation. In this study, using Drosophila as a model system, we investigate the logic by which odors modulate locomotion. We designed a novel behavioral arena in which we could examine a fly’s locomotion under precisely controlled stimulus condition. In this arena, in response to similarly attractive odors, flies modulate their locomotion differently implying that odors have a more diverse effect on locomotion than was anticipated. Three features underlie odor-guided locomotion: First, in response to odors, flies modulate a surprisingly large number of motor parameters. Second, similarly attractive odors elicit changes in different motor programs. Third, different ORN classes modulate different subset of motor parameters. DOI: http://dx.doi.org/10.7554/eLife.11092.001 PMID:26439011

Identity and Diversity of Human Peripheral Th and T Regulatory Cells Defined by Single-Cell Mass Cytometry.

PubMed

Kunicki, Matthew A; Amaya Hernandez, Laura C; Davis, Kara L; Bacchetta, Rosa; Roncarolo, Maria-Grazia

2018-01-01

Human CD3 + CD4 + Th cells, FOXP3 + T regulatory (Treg) cells, and T regulatory type 1 (Tr1) cells are essential for ensuring peripheral immune response and tolerance, but the diversity of Th, Treg, and Tr1 cell subsets has not been fully characterized. Independent functional characterization of human Th1, Th2, Th17, T follicular helper (Tfh), Treg, and Tr1 cells has helped to define unique surface molecules, transcription factors, and signaling profiles for each subset. However, the adequacy of these markers to recapitulate the whole CD3 + CD4 + T cell compartment remains questionable. In this study, we examined CD3 + CD4 + T cell populations by single-cell mass cytometry. We characterize the CD3 + CD4 + Th, Treg, and Tr1 cell populations simultaneously across 23 memory T cell-associated surface and intracellular molecules. High-dimensional analysis identified several new subsets, in addition to the already defined CD3 + CD4 + Th, Treg, and Tr1 cell populations, for a total of 11 Th cell, 4 Treg, and 1 Tr1 cell subsets. Some of these subsets share markers previously thought to be selective for Treg, Th1, Th2, Th17, and Tfh cells, including CD194 (CCR4) + FOXP3 + Treg and CD183 (CXCR3) + T-bet + Th17 cell subsets. Unsupervised clustering displayed a phenotypic organization of CD3 + CD4 + T cells that confirmed their diversity but showed interrelation between the different subsets, including similarity between Th1-Th2-Tfh cell populations and Th17 cells, as well as similarity of Th2 cells with Treg cells. In conclusion, the use of single-cell mass cytometry provides a systems-level characterization of CD3 + CD4 + T cells in healthy human blood, which represents an important baseline reference to investigate abnormalities of different subsets in immune-mediated pathologies. Copyright © 2017 by The American Association of Immunologists, Inc.

Human attention filters for single colors.

PubMed

Sun, Peng; Chubb, Charles; Wright, Charles E; Sperling, George

2016-10-25

The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid-the center of gravity-of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attention-filter selectivity is virtually independent of discriminabilty.

AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

PubMed

Sun, Lei; Wang, Jun; Wei, Jinmao

2017-03-14

The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

Gene selection heuristic algorithm for nutrigenomics studies.

PubMed

Valour, D; Hue, I; Grimard, B; Valour, B

2013-07-15

Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.

An Adaptive Genetic Association Test Using Double Kernel Machines.

PubMed

Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis

2015-10-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.

Interactive learning in 2×2 normal form games by neural network agents

NASA Astrophysics Data System (ADS)

Spiliopoulos, Leonidas

2012-11-01

This paper models the learning process of populations of randomly rematched tabula rasa neural network (NN) agents playing randomly generated 2×2 normal form games of all strategic classes. This approach has greater external validity than the existing models in the literature, each of which is usually applicable to narrow subsets of classes of games (often a single game) and/or to fixed matching protocols. The learning prowess of NNs with hidden layers was impressive as they learned to play unique pure strategy equilibria with near certainty, adhered to principles of dominance and iterated dominance, and exhibited a preference for risk-dominant equilibria. In contrast, perceptron NNs were found to perform significantly worse than hidden layer NN agents and human subjects in experimental studies.

CryoEM and image sorting for flexible protein/DNA complexes.

PubMed

Villarreal, Seth A; Stewart, Phoebe L

2014-07-01

Intrinsically disordered regions of proteins and conformational flexibility within complexes can be critical for biological function. However, disorder, flexibility, and heterogeneity often hinder structural analyses. CryoEM and single particle image processing techniques offer the possibility of imaging samples with significant flexibility. Division of particle images into more homogenous subsets after data acquisition can help compensate for heterogeneity within the sample. We present the utility of an eigenimage sorting analysis for examining two protein/DNA complexes with significant conformational flexibility and heterogeneity. These complexes are integral to the non-homologous end joining pathway, and are involved in the repair of double strand breaks of DNA. Both complexes include the DNA-dependent protein kinase catalytic subunit (DNA-PKcs) and biotinylated DNA with bound streptavidin, with one complex containing the Ku heterodimer. Initial 3D reconstructions of the two DNA-PKcs complexes resembled a cryoEM structure of uncomplexed DNA-PKcs without additional density clearly attributable to the remaining components. Application of eigenimage sorting allowed division of the DNA-PKcs complex datasets into more homogeneous subsets. This led to visualization of density near the base of the DNA-PKcs that can be attributed to DNA, streptavidin, and Ku. However, comparison of projections of the subset structures with 2D class averages indicated that a significant level of heterogeneity remained within each subset. In summary, image sorting methods allowed visualization of extra density near the base of DNA-PKcs, suggesting that DNA binds in the vicinity of the base of the molecule and potentially to a flexible region of DNA-PKcs. Copyright © 2013 Elsevier Inc. All rights reserved.

The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support

PubMed Central

Malof, Jordan M.; Mazurowski, Maciej A.; Tourassi, Georgia D.

2013-01-01

Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of training cases can impact the performance of the resulting classifier as well as properties of the selected set. In this study, the experiments are performed using a dataset for the problem of detecting breast masses in screening mammograms. The classification problem was binary and we used a k-nearest neighbor classifier. The classifier’s performance was evaluated using the Receiver Operating Characteristic (ROC) area under the curve (AUC) measure. The experimental results indicate that although class imbalance reduces the performance of the derived classifier and the effectiveness of selection at improving overall classifier performance, case selection can still be beneficial, regardless of the level of class imbalance. PMID:21820273

Attentional Requirements for the Selection of Words from Different Grammatical Categories

ERIC Educational Resources Information Center

Ayora, Pauline; Janssen, Niels; Dell'Acqua, Roberto; Alario, F.-Xavier

2009-01-01

Two grammatical classes are commonly distinguished in psycholinguistic research. The open-class includes content words such as nouns, whereas the closed-class includes function words such as determiners. A standing issue is to identify whether these words are retrieved through similar or distinct selection mechanisms. We report a comparative…

Attenders versus Slackers: A Classroom Demonstration of Quasi-Experimentation and Self-Selecting Samples

ERIC Educational Resources Information Center

Stellmack, Mark A.

2013-01-01

Studies of the effects of class attendance on class performance typically are quasi-experimental because students choose whether or not to attend class; that is, the samples are self-selecting. The lack of random assignment prevents one from establishing a causal relationship between attendance and performance. Relating attendance to performance…

Bayesian Ensemble Trees (BET) for Clustering and Prediction in Heterogeneous Data

PubMed Central

Duan, Leo L.; Clancy, John P.; Szczesniak, Rhonda D.

2016-01-01

We propose a novel “tree-averaging” model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian Ensemble Trees (BET) and model them as a Dirichlet process. We show that BET determines the optimal number of trees by adapting to the data heterogeneity. Compared with the other ensemble methods, BET requires much fewer trees and shows equivalent prediction accuracy using weighted averaging. Moreover, each tree in BET provides variable selection criterion and interpretation for each subset. We developed an efficient estimating procedure with improved estimation strategies in both CART and mixture models. We demonstrate these advantages of BET with simulations and illustrate the approach with a real-world data example involving regression of lung function measurements obtained from patients with cystic fibrosis. Supplemental materials are available online. PMID:27524872

A neural network approach for enhancing information extraction from multispectral image data

USGS Publications Warehouse

Liu, J.; Shao, G.; Zhu, H.; Liu, S.

2005-01-01

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

Clusters of Antibiotic Resistance Genes Enriched Together Stay Together in Swine Agriculture

PubMed Central

Johnson, Timothy A.; Stedtfeld, Robert D.; Wang, Qiong; Cole, James R.; Hashsham, Syed A.; Looft, Torey; Zhu, Yong-Guan

2016-01-01

ABSTRACT   Antibiotic resistance is a worldwide health risk, but the influence of animal agriculture on the genetic context and enrichment of individual antibiotic resistance alleles remains unclear. Using quantitative PCR followed by amplicon sequencing, we quantified and sequenced 44 genes related to antibiotic resistance, mobile genetic elements, and bacterial phylogeny in microbiomes from U.S. laboratory swine and from swine farms from three Chinese regions. We identified highly abundant resistance clusters: groups of resistance and mobile genetic element alleles that cooccur. For example, the abundance of genes conferring resistance to six classes of antibiotics together with class 1 integrase and the abundance of IS6100-type transposons in three Chinese regions are directly correlated. These resistance cluster genes likely colocalize in microbial genomes in the farms. Resistance cluster alleles were dramatically enriched (up to 1 to 10% as abundant as 16S rRNA) and indicate that multidrug-resistant bacteria are likely the norm rather than an exception in these communities. This enrichment largely occurred independently of phylogenetic composition; thus, resistance clusters are likely present in many bacterial taxa. Furthermore, resistance clusters contain resistance genes that confer resistance to antibiotics independently of their particular use on the farms. Selection for these clusters is likely due to the use of only a subset of the broad range of chemicals to which the clusters confer resistance. The scale of animal agriculture and its wastes, the enrichment and horizontal gene transfer potential of the clusters, and the vicinity of large human populations suggest that managing this resistance reservoir is important for minimizing human risk. PMID:27073098

Immune mechanisms in organ allograft rejection. V. Pivotal role of the cytotoxic-suppressor T cell subset in the rejection of heart grafts bearing isolated class I disparities in the inbred rat

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

Lowry, R.P.; Forbes, R.D.; Blackburn, J.H.

1985-11-01

The cellular requirements for rejection of heart grafts bearing isolated major histocompatibility complex (MHC) subregion RT1A-encoded class I disparities was assessed by adoptive transfer. Sublethally irradiated (780 rads) (PVG X WF)F1 recipients of irradiated PVG-RT1r1 heart grafts were selectively reconstituted with spleen cells from syngeneic donors previously sensitized with two sequential PVG-RT1r1 skin grafts. PVG-RT1r1 heart grafts were rejected acutely in recipients reconstituted with 10 X 10(6) unfractionated immune spleen cells or inocula (5 X 10(6) cells) depleted of SIg+ cells, but additional depletion of cytotoxic T cells and their precursors resulted in marked prolongation of graft survival. Reducing themore » reconstituting inocula from 4 X 10(6) to 2.5 X 10(6) spleen cells prolonged graft survival to that observed in unreconstituted recipients. Additional studies were performed to define the immunologic basis for prolonged survival of PVG-RT1r1 heart grafts in homozygous PVG recipients. Although lymphoid cells of naive PVG failed to proliferate on coculture with irradiated PVG-RT1r1, bulk cultures yielding but weak and variable CTL generation, lymphoid cells from specifically sensitized PVG proliferated and generated greater cytotoxic T lymphocyte (CTL) activity under identical conditions, strongly suggesting, therefore, that prolonged heart graft survival in this strain combination is related to low CTL precursor frequency.« less

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

PubMed Central

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908

Accessing Information in Working Memory: Can the Focus of Attention Grasp Two Elements at the Same Time?

ERIC Educational Resources Information Center

Oberauer, Klaus; Bialkova, Svetlana

2009-01-01

Processing information in working memory requires selective access to a subset of working-memory contents by a focus of attention. Complex cognition often requires joint access to 2 items in working memory. How does the focus select 2 items? Two experiments with an arithmetic task and 1 with a spatial task investigate time demands for successive…

Forecasting Advancement Rates to Petty Officer Third Class for U.S. Navy Hospital Corpsmen

DTIC Science & Technology

2014-06-01

variable. c. Designation of Data Subsets for Cross-Validation In order to maintain the integrity of the analysis and test the fitted models’ predictive...two models, an H-L goodness-of-fit test is conducted on the 1,524 individual Sailors within the designated test data set; the results of which are...the total number of sea months, the proportion of vacancies to test takers, Armed Forces Qualification Test score, and performance mark average

FDTD Simulation of Novel Polarimetric and Directional Reflectance and Transmittance Measurements from Optical Nano- and Micro-Structured Materials

DTIC Science & Technology

2012-03-22

structures and lead to better designs. 84 Appendix A. Particle Swarm Optimization Algorithm In order to validate the need for a new BSDF model ...24 9. Hierarchy representation of a subset of ScatMech BSDF library model classes...polarimetric BRDF at λ=4.3μm of SPP structures with Λ=1.79μm (left), 2μm (middle) and 2.33μm (right). All components are normalized by dividing by s0

The XC chemokine receptor 1 is a conserved selective marker of mammalian cells homologous to mouse CD8α+ dendritic cells

PubMed Central

Crozat, Karine; Guiton, Rachel; Contreras, Vanessa; Feuillet, Vincent; Dutertre, Charles-Antoine; Ventre, Erwan; Vu Manh, Thien-Phong; Baranek, Thomas; Storset, Anne K.; Marvel, Jacqueline; Boudinot, Pierre; Hosmalin, Anne; Schwartz-Cornil, Isabelle

2010-01-01

Human BDCA3+ dendritic cells (DCs) were suggested to be homologous to mouse CD8α+ DCs. We demonstrate that human BDCA3+ DCs are more efficient than their BDCA1+ counterparts or plasmacytoid DCs (pDCs) in cross-presenting antigen and activating CD8+ T cells, which is similar to mouse CD8α+ DCs as compared with CD11b+ DCs or pDCs, although with more moderate differences between human DC subsets. Yet, no specific marker was known to be shared between homologous DC subsets across species. We found that XC chemokine receptor 1 (XCR1) is specifically expressed and active in mouse CD8α+, human BDCA3+, and sheep CD26+ DCs and is conserved across species. The mRNA encoding the XCR1 ligand chemokine (C motif) ligand 1 (XCL1) is selectively expressed in natural killer (NK) and CD8+ T lymphocytes at steady-state and is enhanced upon activation. Moreover, the Xcl1 mRNA is selectively expressed at high levels in central memory compared with naive CD8+ T lymphocytes. Finally, XCR1−/− mice have decreased early CD8+ T cell responses to Listeria monocytogenes infection, which is associated with higher bacterial loads early in infection. Therefore, XCR1 constitutes the first conserved specific marker for cell subsets homologous to mouse CD8α+ DCs in higher vertebrates and promotes their ability to activate early CD8+ T cell defenses against an intracellular pathogenic bacteria. PMID:20479118

Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

PubMed Central

Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor

2015-01-01

In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. PMID:26712757

Pathways to Disease: The Biological Consequences of Social Adversity on Asthma in Minority Youth

DTIC Science & Technology

2016-10-01

the microbiome, and teleomere length and relate these biomarkers to the measured exposures to adversity and stress. The selection of and methods to...granted approval from the HRPO at the end of December 2015. After selecting a subset of our study population for evaluation, we experienced a second delay...of almost three months in setting up our account for Clinical lab testing. Since we were able to prepare the selected samples for biomarker testing

Purinergic receptor immunoreactivity in the rostral ventromedial medulla.

PubMed

Close, L N; Cetas, J S; Heinricher, M M; Selden, N R

2009-01-23

The rostral ventromedial medulla (RVM) has long been recognized to play a pivotal role in nociceptive modulation. Pro-nociception within the RVM is associated with a distinct functional class of neurons, ON-cells that begin to discharge immediately before nocifensive reflexes. Anti-nociceptive function within the RVM, including the analgesic response to opiates, is associated with another distinct class, OFF-cells, which pause immediately prior to nocifensive reflexes. A third class of RVM neurons, NEUTRAL-cells, does not alter firing in association with nocifensive reflexes. ON-, OFF- and NEUTRAL-cells show differential responsiveness to various behaviorally relevant neuromodulators, including purinergic ligands. Iontophoresis of semi-selective P2X ligands, which are associated with nociceptive transmission in the spinal cord and dorsal root ganglia, preferentially activate ON-cells. By contrast, P2Y ligands activate OFF-cells and P1 ligands suppress the firing of NEUTRAL cells. The current study investigates the distribution of P2X, P2Y and P1 receptor immunoreactivity in RVM neurons of Sprague-Dawley rats. Co-localization with tryptophan hydroxylase (TPH), a well-established marker for serotonergic neurons was also studied. Immunoreactivity for the four purinergic receptor subtypes examined was abundant in all anatomical subdivisions of the RVM. By contrast, TPH-immunoreactivity was restricted to a relatively small subset of RVM neurons concentrated in the nucleus raphe magnus and pallidus, as expected. There was a significant degree of co-localization of each purinergic receptor subtype with TPH-immunoreactivity. This co-localization was most pronounced for P2Y1 receptor immunoreactivity, although this was the least abundant among the different purinergic receptor subtypes examined. Immunoreactivity for multiple purinergic receptor subtypes was often co-localized in single neurons. These results confirm the physiological finding that purinergic receptors are widely expressed in the RVM. Purinergic neurotransmission in this region may play an important role in nociception and/or nociceptive modulation, as at other levels of the neuraxis.

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.

CD127 and CD25 Expression Defines CD4+ T Cell Subsets That Are Differentially Depleted during HIV Infection1

PubMed Central

Dunham, Richard M.; Cervasi, Barbara; Brenchley, Jason M.; Albrecht, Helmut; Weintrob, Amy; Sumpter, Beth; Engram, Jessica; Gordon, Shari; Klatt, Nichole R.; Frank, Ian; Sodora, Donald L.; Douek, Daniel C.; Paiardini, Mirko; Silvestri, Guido

2009-01-01

Decreased CD4+ T cell counts are the best marker of disease progression during HIV infection. However, CD4+ T cells are heterogeneous in phenotype and function, and it is unknown how preferential depletion of specific CD4+ T cell subsets influences disease severity. CD4+ T cells can be classified into three subsets by the expression of receptors for two T cell-tropic cytokines, IL-2 (CD25) and IL-7 (CD127). The CD127+CD25low/− subset includes IL-2-producing naive and central memory T cells; the CD127−CD25− subset includes mainly effector T cells expressing perforin and IFN-γ; and the CD127lowCD25high subset includes FoxP3-expressing regulatory T cells. Herein we investigated how the proportions of these T cell subsets are changed during HIV infection. When compared with healthy controls, HIV-infected patients show a relative increase in CD4+CD127−CD25− T cells that is related to an absolute decline of CD4+CD127+CD25low/− T cells. Interestingly, this expansion of CD4+CD127− T cells was not observed in naturally SIV-infected sooty mangabeys. The relative expansion of CD4+CD127−CD25− T cells correlated directly with the levels of total CD4+ T cell depletion and immune activation. CD4+CD127−CD25− T cells were not selectively resistant to HIV infection as levels of cell-associated virus were similar in all non-naive CD4+ T cell subsets. These data indicate that, during HIV infection, specific changes in the fraction of CD4+ T cells expressing CD25 and/or CD127 are associated with disease progression. Further studies will determine whether monitoring the three subsets of CD4+ T cells defined based on the expression of CD25 and CD127 should be used in the clinical management of HIV-infected individuals. PMID:18390743

Pan-Cancer Molecular Classes Transcending Tumor Lineage Across 32 Cancer Types, Multiple Data Platforms, and over 10,000 Cases.

PubMed

Chen, Fengju; Zhang, Yiqun; Gibbons, Don L; Deneen, Benjamin; Kwiatkowski, David J; Ittmann, Michael; Creighton, Chad J

2018-05-01

Purpose: The Cancer Genome Atlas data resources represent an opportunity to explore commonalities across cancer types involving multiple molecular levels, but tumor lineage and histology can represent a barrier in moving beyond differences related to cancer type. Experimental Design: On the basis of gene expression data, we classified 10,224 cancers, representing 32 major types, into 10 molecular-based "classes." Molecular patterns representing tissue or histologic dominant effects were first removed computationally, with the resulting classes representing emergent themes across tumor lineages. Results: Key differences involving mRNAs, miRNAs, proteins, and DNA methylation underscored the pan-cancer classes. One class expressing neuroendocrine and cancer-testis antigen markers represented ∼4% of cancers surveyed. Basal-like breast cancers segregated into an exclusive class, distinct from all other cancers. Immune checkpoint pathway markers and molecular signatures of immune infiltrates were most strongly manifested within a class representing ∼13% of cancers. Pathway-level differences involving hypoxia, NRF2-ARE, Wnt, and Notch were manifested in two additional classes enriched for mesenchymal markers and miR200 silencing. Conclusions: All pan-cancer molecular classes uncovered here, with the important exception of the basal-like breast cancer class, involve a wide range of cancer types and would facilitate understanding the molecular underpinnings of cancers beyond tissue-oriented domains. Numerous biological processes associated with cancer in the laboratory setting were found here to be coordinately manifested across large subsets of human cancers. The number of cancers manifesting features of neuroendocrine tumors may be much higher than previously thought, which disease is known to occur in many different tissues. Clin Cancer Res; 24(9); 2182-93. ©2018 AACR . ©2018 American Association for Cancer Research.