Applications of self-organizing neural networks in virtual screening and diversity selection.

PubMed

Selzer, Paul; Ertl, Peter

2006-01-01

Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

An opinion formation based binary optimization approach for feature selection

NASA Astrophysics Data System (ADS)

Hamedmoghadam, Homayoun; Jalili, Mahdi; Yu, Xinghuo

2018-02-01

This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.

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.

Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

PubMed Central

Careau, Vincent; Wolak, Matthew E.; Carter, Patrick A.; Garland, Theodore

2015-01-01

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix (G). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change. PMID:26582016

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.

Application of the artificial bee colony algorithm for solving the set covering problem.

PubMed

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

PubMed Central

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

Fingerprinting selection for agroenvironmental catchment studies: EDXRF analysis for solving complex artificial mixtures

NASA Astrophysics Data System (ADS)

Torres Astorga, Romina; Velasco, Hugo; Dercon, Gerd; Mabit, Lionel

2017-04-01

Soil erosion and associated sediment transportation and deposition processes are key environmental problems in Central Argentinian watersheds. Several land use practices - such as intensive grazing and crop cultivation - are considered likely to increase significantly land degradation and soil/sediment erosion processes. Characterized by highly erodible soils, the sub catchment Estancia Grande (12.3 km2) located 23 km north east of San Luis has been investigated by using sediment source fingerprinting techniques to identify critical hot spots of land degradation. The authors created 4 artificial mixtures using known quantities of the most representative sediment sources of the studied catchment. The first mixture was made using four rotation crop soil sources. The second and the third mixture were created using different proportions of 4 different soil sources including soils from a feedlot, a rotation crop, a walnut forest and a grazing soil. The last tested mixture contained the same sources as the third mixture but with the addition of a fifth soil source (i.e. a native bank soil). The Energy Dispersive X Ray Fluorescence (EDXRF) analytical technique has been used to reconstruct the source sediment proportion of the original mixtures. Besides using a traditional method of fingerprint selection such as Kruskal-Wallis H-test and Discriminant Function Analysis (DFA), the authors used the actual source proportions in the mixtures and selected from the subset of tracers that passed the statistical tests specific elemental tracers that were in agreement with the expected mixture contents. The selection process ended with testing in a mixing model all possible combinations of the reduced number of tracers obtained. Alkaline earth metals especially Strontium (Sr) and Barium (Ba) were identified as the most effective fingerprints and provided a reduced Mean Absolute Error (MAE) of approximately 2% when reconstructing the 4 artificial mixtures. This study demonstrates that the EDXRF fingerprinting approach performed very well in reconstructing our original mixtures especially in identifying and quantifying the contribution of the 4 rotation crop soil sources in the first mixture.

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.

Selection and collection of multi parameter physiological data for cardiac rhythm diagnostic algorithm development

NASA Astrophysics Data System (ADS)

Bostock, J.; Weller, P.; Cooklin, M.

2010-07-01

Automated diagnostic algorithms are used in implantable cardioverter-defibrillators (ICD's) to detect abnormal heart rhythms. Algorithms misdiagnose and improved specificity is needed to prevent inappropriate therapy. Knowledge engineering (KE) and artificial intelligence (AI) could improve this. A pilot study of KE was performed with artificial neural network (ANN) as AI system. A case note review analysed arrhythmic events stored in patients ICD memory. 13.2% patients received inappropriate therapy. The best ICD algorithm had sensitivity 1.00, specificity 0.69 (p<0.001 different to gold standard). A subset of data was used to train and test an ANN. A feed-forward, back-propagation network with 7 inputs, a 4 node hidden layer and 1 output had sensitivity 1.00, specificity 0.71 (p<0.001). A prospective study was performed using KE to list arrhythmias, factors and indicators for which measurable parameters were evaluated and results reviewed by a domain expert. Waveforms from electrodes in the heart and thoracic bio-impedance; temperature and motion data were collected from 65 patients during cardiac electrophysiological studies. 5 incomplete datasets were due to technical failures. We concluded that KE successfully guided selection of parameters and ANN produced a usable system and that complex data collection carries greater risk of technical failure, leading to data loss.

Artificial selection increased body weight but induced increase of runs of homozygosity in Hanwoo cattle

PubMed Central

Kim, Kwondo; Jung, Jaehoon; Caetano-Anollés, Kelsey; Sung, Samsun; Yoo, DongAhn; Choi, Bong-Hwan; Kim, Hyung-Chul; Jeong, Jin-Young; Cho, Yong-Min; Park, Eung-Woo; Choi, Tae-Jeong; Park, Byoungho; Lim, Dajeong

2018-01-01

Artificial selection has been demonstrated to have a rapid and significant effect on the phenotype and genome of an organism. However, most previous studies on artificial selection have focused solely on genomic sequences modified by artificial selection or genomic sequences associated with a specific trait. In this study, we generated whole genome sequencing data of 126 cattle under artificial selection, and 24,973,862 single nucleotide variants to investigate the relationship among artificial selection, genomic sequences and trait. Using runs of homozygosity detected by the variants, we showed increase of inbreeding for decades, and at the same time demonstrated a little influence of recent inbreeding on body weight. Also, we could identify ~0.2 Mb runs of homozygosity segment which may be created by recent artificial selection. This approach may aid in development of genetic markers directly influenced by artificial selection, and provide insight into the process of artificial selection. PMID:29561881

The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

NASA Astrophysics Data System (ADS)

Zaremotlagh, S.; Hezarkhani, A.

2017-04-01

Some evidences of rare earth elements (REE) concentrations are found in iron oxide-apatite (IOA) deposits which are located in Central Iranian microcontinent. There are many unsolved problems about the origin and metallogenesis of IOA deposits in this district. Although it is considered that felsic magmatism and mineralization were simultaneous in the district, interaction of multi-stage hydrothermal-magmatic processes within the Early Cambrian volcano-sedimentary sequence probably caused some epigenetic mineralizations. Secondary geological processes (e.g., multi-stage mineralization, alteration, and weathering) have affected on variations of major elements and possible redistribution of REE in IOA deposits. Hence, the geochemical behaviors and distribution patterns of REE are expected to be complicated in different zones of these deposits. The aim of this paper is recognizing LREE distribution patterns based on whole-rock chemical compositions and automatic discovery of their geochemical rules. For this purpose, the pattern recognition techniques including decision tree and neural network were applied on a high-dimensional geochemical dataset from Choghart IOA deposit. Because some data features were irrelevant or redundant in recognizing the distribution patterns of each LREE, a greedy attribute subset selection technique was employed to select the best subset of predictors used in classification tasks. The decision trees (CART algorithm) were pruned optimally to more accurately categorize independent test data than unpruned ones. The most effective classification rules were extracted from the pruned tree to describe the meaningful relationships between the predictors and different concentrations of LREE. A feed-forward artificial neural network was also applied to reliably predict the influence of various rock compositions on the spatial distribution patterns of LREE with a better performance than the decision tree induction. The findings of this study could be effectively used to visualize the LREE distribution patterns as geochemical maps.

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.

Evolution of the additive genetic variance-covariance matrix under continuous directional selection on a complex behavioural phenotype.

PubMed

Careau, Vincent; Wolak, Matthew E; Carter, Patrick A; Garland, Theodore

2015-11-22

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance-covariance matrix ( G: ). Yet knowledge of G: in a population experiencing new or altered selection is not sufficient to predict selection response because G: itself evolves in ways that are poorly understood. We experimentally evaluated changes in G: when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G: induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G: induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G: and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change. © 2015 The Author(s).

Levels and limits in artificial selection of communities.

PubMed

Blouin, Manuel; Karimi, Battle; Mathieu, Jérôme; Lerch, Thomas Z

2015-10-01

Artificial selection of individuals has been determinant in the elaboration of the Darwinian theory of natural selection. Nowadays, artificial selection of ecosystems has proven its efficiency and could contribute to a theory of natural selection at several organisation levels. Here, we were not interested in identifying mechanisms of adaptation to selection, but in establishing the proof of principle that a specific structure of interaction network emerges under ecosystem artificial selection. We also investigated the limits in ecosystem artificial selection to evaluate its potential in terms of managing ecosystem function. By artificially selecting microbial communities for low CO2 emissions over 21 generations (n = 7560), we found a very high heritability of community phenotype (52%). Artificial selection was responsible for simpler interaction networks with lower interaction richness. Phenotype variance and heritability both decreased across generations, suggesting that selection was more likely limited by sampling effects than by stochastic ecosystem dynamics. © 2015 John Wiley & Sons Ltd/CNRS.

Colonization of habitat islands in the deep sea: recruitment to glass sponge stalks

NASA Astrophysics Data System (ADS)

Beaulieu, Stace E.

2001-04-01

Biogenic structures in the deep sea often act as hard substratum 'islands' for the attachment of encrusting fauna. At an abyssal station in the NE Pacific, stalks of hexactinellid sponges in the genus Hyalonema are habitat islands for species-rich epifaunal communities. An experimental study was conducted to (1) determine the colonization rates of artificial Hyalonema stalks, (2) compare the species composition and diversity of recruits to newly available substrata to that of the natural communities, and (3) examine the vertical distribution of recruits. Four sets of six artificial sponge stalks, constructed of Hyalonema spicules, were deployed at 4100 m depth for 3- to 5-month periods. There was no difference in net colonization or immigration rate among the four deployments. Colonization rates were similar to those reported for other deep-sea, hard substratum recruitment experiments. The taxa that recruited to the artificial stalks were a subset of the taxa found in natural communities. However, several taxa important in structuring natural communities did not recruit to the artificial stalks. The two taxa with the highest invasion rates, a calcareous foraminiferan ( Cibicides lobatulus) and a serpulid polychaete ( Bathyvermilia sp.), also were the two taxa with greatest relative abundance in natural communities. Vertical distributions of Cibicides and an agglutinated foraminiferan ( Telammina sp.) were skewed towards the top of the artificial stalks, potentially because of active habitat selection. These results have several implications for natural Hyalonema stalk communities. Most importantly, species composition and abundance of individuals in the stalk communities appear to be maintained by frequent recruitment of a few common taxa and infrequent recruitment of many rare taxa. An argument is presented for temporal-mosaic maintenance of diversity in these deep-sea, hard substratum communities.

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.

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

NASA Astrophysics Data System (ADS)

Correa, R.; Chesta, M. A.; Morales, J. R.; Dinator, M. I.; Requena, I.; Vila, I.

2006-08-01

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

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.

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.

Gene selection for cancer classification with the help of bees.

PubMed

Moosa, Johra Muhammad; Shakur, Rameen; Kaykobad, Mohammad; Rahman, Mohammad Sohel

2016-08-10

Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses. This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings. The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior. The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.

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…

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.

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

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.

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.

Sample selection via angular distance in the space of the arguments of an artificial neural network

NASA Astrophysics Data System (ADS)

Fernández Jaramillo, J. M.; Mayerle, R.

2018-05-01

In the construction of an artificial neural network (ANN) a proper data splitting of the available samples plays a major role in the training process. This selection of subsets for training, testing and validation affects the generalization ability of the neural network. Also the number of samples has an impact in the time required for the design of the ANN and the training. This paper introduces an efficient and simple method for reducing the set of samples used for training a neural network. The method reduces the required time to calculate the network coefficients, while keeping the diversity and avoiding overtraining the ANN due the presence of similar samples. The proposed method is based on the calculation of the angle between two vectors, each one representing one input of the neural network. When the angle formed among samples is smaller than a defined threshold only one input is accepted for the training. The accepted inputs are scattered throughout the sample space. Tidal records are used to demonstrate the proposed method. The results of a cross-validation show that with few inputs the quality of the outputs is not accurate and depends on the selection of the first sample, but as the number of inputs increases the accuracy is improved and differences among the scenarios with a different starting sample have and important reduction. A comparison with the K-means clustering algorithm shows that for this application the proposed method with a smaller number of samples is producing a more accurate network.

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

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.

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.

Darwin, artificial selection, and poverty.

PubMed

Sanchez, Luis

2010-03-01

This paper argues that the processes of evolutionary selection are becoming increasingly artificial, a trend that goes against the belief in a purely natural selection process claimed by Darwin's natural selection theory. Artificial selection is mentioned by Darwin, but it was ignored by Social Darwinists, and it is all but absent in neo-Darwinian thinking. This omission results in an underestimation of probable impacts of artificial selection upon assumed evolutionary processes, and has implications for the ideological uses of Darwin's language, particularly in relation to poverty and other social inequalities. The influence of artificial selection on genotypic and phenotypic adaptations arguably represents a substantial shift in the presumed path of evolution, a shift laden with both biological and political implications.

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.

Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns

NASA Astrophysics Data System (ADS)

Manahov, Viktor; Hudson, Robert

2013-10-01

Many scholars express concerns that herding behaviour causes excess volatility, destabilises financial markets, and increases the likelihood of systemic risk. We use a special form of the Strongly Typed Genetic Programming (STGP) technique to evolve a stock market divided into two groups-a small subset of artificial agents called ‘Best Agents’ and a main cohort of agents named ‘All Agents’. The ‘Best Agents’ perform best in term of the trailing return of a wealth moving average. We then investigate whether herding behaviour can arise when agents trade Dow Jones, General Electric, and IBM financial instruments in four different artificial stock markets. This paper uses real historical quotes of the three financial instruments to analyse the behavioural foundations of stylised facts such as leptokurtosis, non-IIDness, and volatility clustering. We found evidence of more herding in a group of stocks than in individual stocks, but the magnitude of herding does not contribute to the mispricing of assets in the long run. Our findings suggest that the price formation process caused by the collective behaviour of the entire market exhibit less herding and is more efficient than the segmented market populated by a small subset of agents. Hence, greater genetic diversity leads to greater consistency with fundamental values and market efficiency.

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.

Identifying artificial selection signals in the chicken genome.

PubMed

Ma, Yunlong; Gu, Lantao; Yang, Liubin; Sun, Chenghao; Xie, Shengsong; Fang, Chengchi; Gong, Yangzhang; Li, Shijun

2018-01-01

Identifying the signals of artificial selection can contribute to further shaping economically important traits. Here, a chicken 600k SNP-array was employed to detect the signals of artificial selection using 331 individuals from 9 breeds, including Jingfen (JF), Jinghong (JH), Araucanas (AR), White Leghorn (WL), Pekin-Bantam (PB), Shamo (SH), Gallus-Gallus-Spadiceus (GA), Rheinlander (RH) and Vorwerkhuhn (VO). Per the population genetic structure, 9 breeds were combined into 5 breed-pools, and a 'two-step' strategy was used to reveal the signals of artificial selection. GA, which has little artificial selection, was defined as the reference population, and a total of 204, 155, 305 and 323 potential artificial selection signals were identified in AR_VO, PB, RH_WL and JH_JF, respectively. We also found signals derived from standing and de-novo genetic variations have contributed to adaptive evolution during artificial selection. Further enrichment analysis suggests that the genomic regions of artificial selection signals harbour genes, including THSR, PTHLH and PMCH, responsible for economic traits, such as fertility, growth and immunization. Overall, this study found a series of genes that contribute to the improvement of chicken breeds and revealed the genetic mechanisms of adaptive evolution, which can be used as fundamental information in future chicken functional genomics study.

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.

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.

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.

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.

On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data-Driven Models

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Maier, Holger R.; Wu, Wenyan; Dandy, Graeme C.; Gupta, Hoshin V.; Zhang, Tuqiao

2018-02-01

Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

PubMed Central

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-01

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. PMID:28124985

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

PubMed

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

Artificial Neural Network and application in calibration transfer of AOTF-based NIR spectrometer

NASA Astrophysics Data System (ADS)

Wang, Wenbo; Jiang, Chengzhi; Xu, Kexin; Wang, Bin

2002-09-01

Chemometrics is widely applied to develop models for quantitative prediction of unknown samples in Near-infrared (NIR) spectroscopy. However, calibrated models generally fail when new instruments are introduced or replacement of the instrument parts occurs. Therefore, calibration transfer becomes necessary to avoid the costly, time-consuming recalibration of models. Piecewise Direct Standardization (PDS) has been proven to be a reference method for standardization. In this paper, Artificial Neural Network (ANN) is employed as an alternative to transfer spectra between instruments. Two Acousto-optic Tunable Filter NIR spectrometers are employed in the experiment. Spectra of glucose solution are collected on the spectrometers through transflectance mode. A Back propagation Network with two layers is employed to simulate the function between instruments piecewisely. Standardization subset is selected by Kennard and Stone (K-S) algorithm in the first two score space of Principal Component Analysis (PCA) of spectra matrix. In current experiment, it is noted that obvious nonlinearity exists between instruments and attempts are made to correct such nonlinear effect. Prediction results before and after successful calibration transfer are compared. Successful transfer can be achieved by adapting window size and training parameters. Final results reveal that ANN is effective in correcting the nonlinear instrumental difference and a only 1.5~2 times larger prediction error is expected after successful transfer.

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.

Contrasting Fish Behavior in Artificial Seascapes with Implications for Resources Conservation

PubMed Central

Koeck, Barbara; Alós, Josep; Caro, Anthony; Neveu, Reda; Crec'hriou, Romain; Saragoni, Gilles; Lenfant, Philippe

2013-01-01

Artificial reefs are used by many fisheries managers as a tool to mitigate the impact of fisheries on coastal fish communities by providing new habitat for many exploited fish species. However, the comparison between the behavior of wild fish inhabiting either natural or artificial habitats has received less attention. Thus the spatio-temporal patterns of fish that establish their home range in one habitat or the other and their consequences of intra-population differentiation on life-history remain largely unexplored. We hypothesize that individuals with a preferred habitat (i.e. natural vs. artificial) can behave differently in terms of habitat use, with important consequences on population dynamics (e.g. life-history, mortality, and reproductive success). Therefore, using biotelemetry, 98 white seabream (Diplodus sargus) inhabiting either artificial or natural habitats were tagged and their behavior was monitored for up to eight months. Most white seabreams were highly resident either on natural or artificial reefs, with a preference for the shallow artificial reef subsets. Connectivity between artificial and natural reefs was limited for resident individuals due to great inter-habitat distances. The temporal behavioral patterns of white seabreams differed between artificial and natural reefs. Artificial-reef resident fish had a predominantly nocturnal diel pattern, whereas natural-reef resident fish showed a diurnal diel pattern. Differences in diel behavioral patterns of white seabream inhabiting artificial and natural reefs could be the expression of realized individual specialization resulting from differences in habitat configuration and resource availability between these two habitats. Artificial reefs have the potential to modify not only seascape connectivity but also the individual behavioral patterns of fishes. Future management plans of coastal areas and fisheries resources, including artificial reef implementation, should therefore consider the potential effect of habitat modification on fish behavior, which could have key implications on fish dynamics. PMID:23935978

Contrasting fish behavior in artificial seascapes with implications for resources conservation.

PubMed

Koeck, Barbara; Alós, Josep; Caro, Anthony; Neveu, Reda; Crec'hriou, Romain; Saragoni, Gilles; Lenfant, Philippe

2013-01-01

Artificial reefs are used by many fisheries managers as a tool to mitigate the impact of fisheries on coastal fish communities by providing new habitat for many exploited fish species. However, the comparison between the behavior of wild fish inhabiting either natural or artificial habitats has received less attention. Thus the spatio-temporal patterns of fish that establish their home range in one habitat or the other and their consequences of intra-population differentiation on life-history remain largely unexplored. We hypothesize that individuals with a preferred habitat (i.e. natural vs. artificial) can behave differently in terms of habitat use, with important consequences on population dynamics (e.g. life-history, mortality, and reproductive success). Therefore, using biotelemetry, 98 white seabream (Diplodus sargus) inhabiting either artificial or natural habitats were tagged and their behavior was monitored for up to eight months. Most white seabreams were highly resident either on natural or artificial reefs, with a preference for the shallow artificial reef subsets. Connectivity between artificial and natural reefs was limited for resident individuals due to great inter-habitat distances. The temporal behavioral patterns of white seabreams differed between artificial and natural reefs. Artificial-reef resident fish had a predominantly nocturnal diel pattern, whereas natural-reef resident fish showed a diurnal diel pattern. Differences in diel behavioral patterns of white seabream inhabiting artificial and natural reefs could be the expression of realized individual specialization resulting from differences in habitat configuration and resource availability between these two habitats. Artificial reefs have the potential to modify not only seascape connectivity but also the individual behavioral patterns of fishes. Future management plans of coastal areas and fisheries resources, including artificial reef implementation, should therefore consider the potential effect of habitat modification on fish behavior, which could have key implications on fish dynamics.

Angles-only, ground-based, initial orbit determination

NASA Astrophysics Data System (ADS)

Taff, L. G.; Randall, P. M. S.; Stansfield, S. A.

1984-05-01

Over the past few years, passive, ground-based, angles-only initial orbit determination has had a thorough analytical, numerical, experimental, and creative re-examination. This report presents the numerical culmination of this effort and contains specific recommendations for which of several techniques one should use on the different subsets of high altitude artificial satellites and minor planets.

Selection of a Representative Subset of Global Climate Models that Captures the Profile of Regional Changes for Integrated Climate Impacts Assessment

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Mcdermid, Sonali P.

2017-01-01

We present the Representative Temperature and Precipitation (T&P) GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to select a practical subset of global climate models (GCMs) for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry) are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts.

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.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

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.

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

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.

A novel approach for dimension reduction of microarray.

PubMed

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artificial “ping-pong” cascade of PIWI-interacting RNA in silkworm cells

PubMed Central

Shoji, Keisuke; Suzuki, Yutaka; Sugano, Sumio; Shimada, Toru; Katsuma, Susumu

2017-01-01

PIWI-interacting RNAs (piRNAs) play essential roles in the defense system against selfish elements in animal germline cells by cooperating with PIWI proteins. A subset of piRNAs is predicted to be generated via the “ping-pong” cascade, which is mainly controlled by two different PIWI proteins. Here we established a cell-based artificial piRNA production system using a silkworm ovarian cultured cell line that is believed to possess a complete piRNA pathway. In addition, we took advantage of a unique silkworm sex-determining one-to-one ping-pong piRNA pair, which enabled us to precisely monitor the behavior of individual artificial piRNAs. With this novel strategy, we successfully generated artificial piRNAs against endogenous protein-coding genes via the expected back-and-forth traveling mechanism. Furthermore, we detected “primary” piRNAs from the upstream region of the artificial “ping-pong” site in the endogenous gene. This artificial piRNA production system experimentally confirms the existence of the “ping-pong” cascade of piRNAs. Also, this system will enable us to identify the factors involved in both, or each, of the “ping” and “pong” cascades and the sequence features that are required for efficient piRNA production. PMID:27777367

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

Artificial-epitope mapping for CK-MB assay.

PubMed

Tai, Dar-Fu; Ho, Yi-Fang; Wu, Cheng-Hsin; Lin, Tzu-Chieh; Lu, Kuo-Hao; Lin, Kun-Shian

2011-06-07

A quantitative method using artificial antibody to detect creatine kinases was developed. Linear epitope sequences were selected based on an artificial-epitope mapping strategy. Nine different MIPs corresponding to the selected peptides were then fabricated on QCM chips. The subtle conformational changes were also recognized by these chips.

Citizen Science Provides Valuable Data for Monitoring Global Night Sky Luminance

PubMed Central

Kyba, Christopher C. M.; Wagner, Janna M.; Kuechly, Helga U.; Walker, Constance E.; Elvidge, Christopher D.; Falchi, Fabio; Ruhtz, Thomas; Fischer, Jürgen; Hölker, Franz

2013-01-01

The skyglow produced by artificial lights at night is one of the most dramatic anthropogenic modifications of Earth's biosphere. The GLOBE at Night citizen science project allows individual observers to quantify skyglow using star maps showing different levels of light pollution. We show that aggregated GLOBE at Night data depend strongly on artificial skyglow, and could be used to track lighting changes worldwide. Naked eye time series can be expected to be very stable, due to the slow pace of human eye evolution. The standard deviation of an individual GLOBE at Night observation is found to be 1.2 stellar magnitudes. Zenith skyglow estimates from the “First World Atlas of Artificial Night Sky Brightness” are tested using a subset of the GLOBE at Night data. Although we find the World Atlas overestimates sky brightness in the very center of large cities, its predictions for Milky Way visibility are accurate. PMID:23677222

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.

Evolution under domestication: ongoing artificial selection and divergence of wild and managed Stenocereus pruinosus (Cactaceae) populations in the Tehuacán Valley, Mexico

PubMed Central

Parra, Fabiola; Casas, Alejandro; Peñaloza-Ramírez, Juan Manuel; Cortés-Palomec, Aurea C.; Rocha-Ramírez, Víctor; González-Rodríguez, Antonio

2010-01-01

Background and Aims The Tehuacán Valley in Mexico is a principal area of plant domestication in Mesoamerica. There, artificial selection is currently practised on nearly 120 native plant species with coexisting wild, silvicultural and cultivated populations, providing an excellent setting for studying ongoing mechanisms of evolution under domestication. One of these species is the columnar cactus Stenocereus pruinosus, in which we studied how artificial selection is operating through traditional management and whether it has determined morphological and genetic divergence between wild and managed populations. Methods Semi-structured interviews were conducted with 83 households of three villages to investigate motives and mechanisms of artificial selection. Management effects were studied by comparing variation patterns of 14 morphological characters and population genetics (four microsatellite loci) of 264 plants from nine wild, silvicultural and cultivated populations. Key Results Variation in fruit characters was recognized by most people, and was the principal target of artificial selection directed to favour larger and sweeter fruits with thinner or thicker peel, fewer spines and pulp colours others than red. Artificial selection operates in agroforestry systems favouring abundance (through not felling plants and planting branches) of the preferred phenotypes, and acts more intensely in household gardens. Significant morphological divergence between wild and managed populations was observed in fruit characters and plant vigour. On average, genetic diversity in silvicultural populations (HE = 0·743) was higher than in wild (HE = 0·726) and cultivated (HE = 0·700) populations. Most of the genetic variation (90·58 %) occurred within populations. High gene flow (NmFST > 2) was identified among almost all populations studied, but was slightly limited by mountains among wild populations, and by artificial selection among wild and managed populations. Conclusions Traditional management of S. pruinosus involves artificial selection, which, despite the high levels of gene flow, has promoted morphological divergence and moderate genetic structure between wild and managed populations, while conserving genetic diversity. PMID:20729372

Evolution under domestication: ongoing artificial selection and divergence of wild and managed Stenocereus pruinosus (Cactaceae) populations in the Tehuacan Valley, Mexico.

PubMed

Parra, Fabiola; Casas, Alejandro; Peñaloza-Ramírez, Juan Manuel; Cortés-Palomec, Aurea C; Rocha-Ramírez, Víctor; González-Rodríguez, Antonio

2010-09-01

The Tehuacán Valley in Mexico is a principal area of plant domestication in Mesoamerica. There, artificial selection is currently practised on nearly 120 native plant species with coexisting wild, silvicultural and cultivated populations, providing an excellent setting for studying ongoing mechanisms of evolution under domestication. One of these species is the columnar cactus Stenocereus pruinosus, in which we studied how artificial selection is operating through traditional management and whether it has determined morphological and genetic divergence between wild and managed populations. Semi-structured interviews were conducted with 83 households of three villages to investigate motives and mechanisms of artificial selection. Management effects were studied by comparing variation patterns of 14 morphological characters and population genetics (four microsatellite loci) of 264 plants from nine wild, silvicultural and cultivated populations. Variation in fruit characters was recognized by most people, and was the principal target of artificial selection directed to favour larger and sweeter fruits with thinner or thicker peel, fewer spines and pulp colours other than red. Artificial selection operates in agroforestry systems favouring abundance (through not felling plants and planting branches) of the preferred phenotypes, and acts more intensely in household gardens. Significant morphological divergence between wild and managed populations was observed in fruit characters and plant vigour. On average, genetic diversity in silvicultural populations (H(E) = 0.743) was higher than in wild (H(E) = 0.726) and cultivated (H(E) = 0.700) populations. Most of the genetic variation (90.58 %) occurred within populations. High gene flow (Nm(FST) > 2) was identified among almost all populations studied, but was slightly limited by mountains among wild populations, and by artificial selection among wild and managed populations. Traditional management of S. pruinosus involves artificial selection, which, despite the high levels of gene flow, has promoted morphological divergence and moderate genetic structure between wild and managed populations, while conserving genetic diversity.

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.

HOMPRA Europe - A gridded precipitation data set from European homogenized time series

NASA Astrophysics Data System (ADS)

Rustemeier, Elke; Kapala, Alice; Meyer-Christoffer, Anja; Finger, Peter; Schneider, Udo; Venema, Victor; Ziese, Markus; Simmer, Clemens; Becker, Andreas

2017-04-01

Reliable monitoring data are essential for robust analyses of climate variability and, in particular, long-term trends. In this regard, a gridded, homogenized data set of monthly precipitation totals - HOMPRA Europe (HOMogenized PRecipitation Analysis of European in-situ data)- is presented. The data base consists of 5373 homogenized monthly time series, a carefully selected subset held by the Global Precipitation Climatology Centre (GPCC). The chosen series cover the period 1951-2005 and contain less than 10% missing values. Due to the large number of data, an automatic algorithm had to be developed for the homogenization of these precipitation series. In principal, the algorithm is based on three steps: * Selection of overlapping station networks in the same precipitation regime, based on rank correlation and Ward's method of minimal variance. Since the underlying time series should be as homogeneous as possible, the station selection is carried out by deterministic first derivation in order to reduce artificial influences. * The natural variability and trends were temporally removed by means of highly correlated neighboring time series to detect artificial break-points in the annual totals. This ensures that only artificial changes can be detected. The method is based on the algorithm of Caussinus and Mestre (2004). * In the last step, the detected breaks are corrected monthly by means of a multiple linear regression (Mestre, 2003). Due to the automation of the homogenization, the validation of the algorithm is essential. Therefore, the method was tested on artificial data sets. Additionally the sensitivity of the method was tested by varying the neighborhood series. If available in digitized form, the station history was also used to search for systematic errors in the jump detection. Finally, the actual HOMPRA Europe product is produced by interpolation of the homogenized series onto a 1° grid using one of the interpolation schems operationally at GPCC (Becker et al., 2013 and Schamm et al., 2014). Caussinus, H., und O. Mestre, 2004: Detection and correction of artificial shifts in climate series, Journal of the Royal, Statistical Society. Series C (Applied Statistics), 53(3), 405-425. Mestre, O., 2003: Correcting climate series using ANOVA technique, Proceedings of the fourth seminar Willmott, C.; Rowe, C. & Philpot, W., 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring The American Carthographer, 12, 5-16 Becker, A.; Finger, P.; Meyer-Christoffer, A.; Rudolf, B.; Schamm, K.; Schneider, U. & Ziese, M., 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present Earth System Science Data, 5, 71-99 Schamm, K.; Ziese, M.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Schneider, U.; Schröder, M. & Stender, P., 2014: Global gridded precipitation over land: a description of the new GPCC First Guess Daily product, Earth System Science Data, 6, 49-60

Using different classification models in wheat grading utilizing visual features

NASA Astrophysics Data System (ADS)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

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.

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

Initial development of a computer-aided diagnosis tool for solitary pulmonary nodules

NASA Astrophysics Data System (ADS)

Catarious, David M., Jr.; Baydush, Alan H.; Floyd, Carey E., Jr.

2001-07-01

This paper describes the development of a computer-aided diagnosis (CAD) tool for solitary pulmonary nodules. This CAD tool is built upon physically meaningful features that were selected because of their relevance to shape and texture. These features included a modified version of the Hotelling statistic (HS), a channelized HS, three measures of fractal properties, two measures of spicularity, and three manually measured shape features. These features were measured from a difficult database consisting of 237 regions of interest (ROIs) extracted from digitized chest radiographs. The center of each 256x256 pixel ROI contained a suspicious lesion which was sent to follow-up by a radiologist and whose nature was later clinically determined. Linear discriminant analysis (LDA) was used to search the feature space via sequential forward search using percentage correct as the performance metric. An optimized feature subset, selected for the highest accuracy, was then fed into a three layer artificial neural network (ANN). The ANN's performance was assessed by receiver operating characteristic (ROC) analysis. A leave-one-out testing/training methodology was employed for the ROC analysis. The performance of this system is competitive with that of three radiologists on the same database.

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk.

PubMed

Cecchinato, A; De Marchi, M; Gallo, L; Bittante, G; Carnier, P

2009-10-01

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a(30), mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Prediction models for RCT and a(30) based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a(30), respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a(30) were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a(30) ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.

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

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.

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.

Therapeutic effects of eustachian tube surfactant in barotitis media in guinea pigs.

PubMed

Feng, Li-Ning; Chen, Wen-Xian; Cong, Rui; Gou, Lin

2003-07-01

Previous research has shown that the eustachian tube (ET) in animals and humans is lined with a substance that lowers surface tension and thus facilitates the opening of the eustachian tube and aeration of the middle ear. The aims of the present study were to observe the role of eustachian tube surfactant (ETS) on the opening of the ET and to explore the therapeutic effect of natural and artificial ETS on barotitis media (BOM). BOM was successfully established in 50 guinea pigs by simulated ascent in an altitude chamber. Subsets of the affected ears were treated by flushing with natural ETS, artificial ETS, artificial phospholipid, or saline. The effects were evaluated by measuring eustachian tube pressure opening level (POL). Other animals with BOM were treated with artificial ETS on one side and saline in the other, after which the clinical signs were observed. The POL of the saline group remained unchanged. Natural ETS decreased the POL from 11.98 to 6.11 kPa (p < 0.01); artificial ETS reduced the POL from 11.91 to 6.67 kPa (p < 0.01); there was no significant difference between the two treatments. Artificial phospholipid was less effective, decreasing POL from 11.86 to 8.61 kPa (p < 0.05). Clinical observations showed that after 1 wk of treatment with artificial ETS, the congestion in the tympanic membrane was alleviated, the hearing threshold improved, and the effusion in tympanic cavity diminished. Artificial ETS was as effective as natural ETS in facilitating the opening of eustachian tube and had definite therapeutic effects on BOM in this model.

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.

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.

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection.

PubMed

Zawbaa, Hossam M; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

2016-01-01

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres—Focus on Feature Selection

PubMed Central

Zawbaa, Hossam M.; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

2016-01-01

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven. PMID:27315205

Non-caloric artificial sweeteners and the microbiome: findings and challenges

PubMed Central

Suez, Jotham; Korem, Tal; Zilberman-Schapira, Gili; Segal, Eran; Elinav, Eran

2015-01-01

Non-caloric artificial sweeteners (NAS) are common food supplements consumed by millions worldwide as means of combating weight gain and diabetes, by retaining sweet taste without increasing caloric intake. While they are considered safe, there is increasing controversy regarding their potential ability to promote metabolic derangements in some humans. We recently demonstrated that NAS consumption could induce glucose intolerance in mice and distinct human subsets, by functionally altering the gut microbiome. In this commentary, we discuss these findings in the context of previous and recent works demonstrating the effects of NAS on host health and the microbiome, and the challenges and open questions that need to be addressed in understanding the effects of NAS consumption on human health. PMID:25831243

Non-caloric artificial sweeteners and the microbiome: findings and challenges.

PubMed

Suez, Jotham; Korem, Tal; Zilberman-Schapira, Gili; Segal, Eran; Elinav, Eran

2015-01-01

Non-caloric artificial sweeteners (NAS) are common food supplements consumed by millions worldwide as means of combating weight gain and diabetes, by retaining sweet taste without increasing caloric intake. While they are considered safe, there is increasing controversy regarding their potential ability to promote metabolic derangements in some humans. We recently demonstrated that NAS consumption could induce glucose intolerance in mice and distinct human subsets, by functionally altering the gut microbiome. In this commentary, we discuss these findings in the context of previous and recent works demonstrating the effects of NAS on host health and the microbiome, and the challenges and open questions that need to be addressed in understanding the effects of NAS consumption on human health.

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.

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.

The total artificial heart.

PubMed

Cook, Jason A; Shah, Keyur B; Quader, Mohammed A; Cooke, Richard H; Kasirajan, Vigneshwar; Rao, Kris K; Smallfield, Melissa C; Tchoukina, Inna; Tang, Daniel G

2015-12-01

The total artificial heart (TAH) is a form of mechanical circulatory support in which the patient's native ventricles and valves are explanted and replaced by a pneumatically powered artificial heart. Currently, the TAH is approved for use in end-stage biventricular heart failure as a bridge to heart transplantation. However, with an increasing global burden of cardiovascular disease and congestive heart failure, the number of patients with end-stage heart failure awaiting heart transplantation now far exceeds the number of available hearts. As a result, the use of mechanical circulatory support, including the TAH and left ventricular assist device (LVAD), is growing exponentially. The LVAD is already widely used as destination therapy, and destination therapy for the TAH is under investigation. While most patients requiring mechanical circulatory support are effectively treated with LVADs, there is a subset of patients with concurrent right ventricular failure or major structural barriers to LVAD placement in whom TAH may be more appropriate. The history, indications, surgical implantation, post device management, outcomes, complications, and future direction of the TAH are discussed in this review.

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.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

PubMed

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

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.

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.

Choice: 36 band feature selection software with applications to multispectral pattern recognition

NASA Technical Reports Server (NTRS)

Jones, W. C.

1973-01-01

Feature selection software was developed at the Earth Resources Laboratory that is capable of inputting up to 36 channels and selecting channel subsets according to several criteria based on divergence. One of the criterion used is compatible with the table look-up classifier requirements. The software indicates which channel subset best separates (based on average divergence) each class from all other classes. The software employs an exhaustive search technique, and computer time is not prohibitive. A typical task to select the best 4 of 22 channels for 12 classes takes 9 minutes on a Univac 1108 computer.

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

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

PubMed

Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

PubMed Central

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272

Selective Attention and Control of Action: Comparative Psychology of an Artificial, Evolved Agent and People

ERIC Educational Resources Information Center

Ward, Robert; Ward, Ronnie

2008-01-01

This study examined the selective attention abilities of a simple, artificial, evolved agent and considered implications of the agent's performance for theories of selective attention and action. The agent processed two targets in continuous time, catching one and then the other. This task required many cognitive operations, including prioritizing…

Defining an essence of structure determining residue contacts in proteins.

PubMed

Sathyapriya, R; Duarte, Jose M; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

2009-12-01

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this "structural essence" has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts-such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed "cone-peeling" that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 A Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This "structural essence" opens new avenues in the fields of structure prediction, empirical potentials and docking.

Defining an Essence of Structure Determining Residue Contacts in Proteins

PubMed Central

Sathyapriya, R.; Duarte, Jose M.; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

2009-01-01

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this “structural essence” has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts—such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed “cone-peeling” that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 Å Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This “structural essence” opens new avenues in the fields of structure prediction, empirical potentials and docking. PMID:19997489

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.

Why do bacteria divide?

PubMed Central

Norris, Vic

2015-01-01

The problem of not only how but also why cells divide can be tackled using recent ideas. One idea from the origins of life – Life as independent of its constituents – is that a living entity like a cell is a particular pattern of connectivity between its constituents. This means that if the growing cell were just to get bigger the average connectivity between its constituents per unit mass – its cellular connectivity – would decrease and the cell would lose its identity. The solution is division which restores connectivity. The corollary is that the cell senses decreasing cellular connectivity and uses this information to trigger division. A second idea from phenotypic diversity – Life on the Scales of Equilibria – is that a bacterium must find strategies that allow it to both survive and grow. This means that it has learnt to reconcile the opposing constraints that these strategies impose. The solution is that the cell cycle generates daughter cells with different phenotypes based on sufficiently complex equilibrium (E) and non-equilibrium (NE) cellular compounds and structures appropriate for survival and growth, respectively, alias ‘hyperstructures.’ The corollary is that the cell senses both the quantity of E material and the intensity of use of NE material and then uses this information to trigger the cell cycle. A third idea from artificial intelligence – Competitive Coherence – is that a cell selects the active subset of elements that actively determine its phenotype from a much larger set of available elements. This means that the selection of an active subset of a specific size and composition must be done so as to generate both a coherent cell state, in which the cell’s contents work together harmoniously, and a coherent sequence of cell states, each coherent with respect to itself and to an unpredictable environment. The solution is the use of a range of mechanisms ranging from hyperstructure dynamics to the cell cycle itself. PMID:25932025

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

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.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

PubMed Central

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

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.

Artificial pheromone for path selection by a foraging swarm of robots.

PubMed

Campo, Alexandre; Gutiérrez, Alvaro; Nouyan, Shervin; Pinciroli, Carlo; Longchamp, Valentin; Garnier, Simon; Dorigo, Marco

2010-11-01

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.

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.

Credentialism in Our Ignorant Society.

ERIC Educational Resources Information Center

Marien, Michael

All societies have procedures for selecting who will occupy important positions. The use of credentials characterizes our system of social selection, and our worship of them has created the following problems: an artificial demand for education, artificial restraints to learning, the overlooking of obsolescence, generational inversion (wherein the…

Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

PubMed Central

Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

2010-01-01

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

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

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 ...

Coral Colonisation of an Artificial Reef in a Turbid Nearshore Environment, Dampier Harbour, Western Australia

PubMed Central

Blakeway, David; Byers, Michael; Stoddart, James; Rossendell, Jason

2013-01-01

A 0.6 hectare artificial reef of local rock and recycled concrete sleepers was constructed in December 2006 at Parker Point in the industrial port of Dampier, western Australia, with the aim of providing an environmental offset for a nearshore coral community lost to land reclamation. Corals successfully colonised the artificial reef, despite the relatively harsh environmental conditions at the site (annual water temperature range 18-32°C, intermittent high turbidity, frequent cyclones, frequent nearby ship movements). Coral settlement to the artificial reef was examined by terracotta tile deployments, and later stages of coral community development were examined by in-situ visual surveys within fixed 25 x 25 cm quadrats on the rock and concrete substrates. Mean coral density on the tiles varied from 113 ± 17 SE to 909 ± 85 SE per m2 over five deployments, whereas mean coral density in the quadrats was only 6.0 ± 1.0 SE per m2 at eight months post construction, increasing to 24.0 ± 2.1 SE per m2 at 62 months post construction. Coral taxa colonising the artificial reef were a subset of those on the surrounding natural reef, but occurred in different proportions— Pseudosiderastreatayami , Mycediumelephantotus and Leptastrea purpurea being disproportionately abundant on the artificial reef. Coral cover increased rapidly in the later stages of the study, reaching 2.3 ± 0.7 SE % at 62 months post construction. This study indicates that simple materials of opportunity can provide a suitable substrate for coral recruitment in Dampier Harbour, and that natural colonisation at the study site remains sufficient to initiate a coral community on artificial substrate despite ongoing natural and anthropogenic perturbations. PMID:24040405

Coral colonisation of an artificial reef in a turbid nearshore environment, Dampier Harbour, western Australia.

PubMed

Blakeway, David; Byers, Michael; Stoddart, James; Rossendell, Jason

2013-01-01

A 0.6 hectare artificial reef of local rock and recycled concrete sleepers was constructed in December 2006 at Parker Point in the industrial port of Dampier, western Australia, with the aim of providing an environmental offset for a nearshore coral community lost to land reclamation. Corals successfully colonised the artificial reef, despite the relatively harsh environmental conditions at the site (annual water temperature range 18-32°C, intermittent high turbidity, frequent cyclones, frequent nearby ship movements). Coral settlement to the artificial reef was examined by terracotta tile deployments, and later stages of coral community development were examined by in-situ visual surveys within fixed 25 x 25 cm quadrats on the rock and concrete substrates. Mean coral density on the tiles varied from 113 ± 17 SE to 909 ± 85 SE per m(2) over five deployments, whereas mean coral density in the quadrats was only 6.0 ± 1.0 SE per m(2) at eight months post construction, increasing to 24.0 ± 2.1 SE per m(2) at 62 months post construction. Coral taxa colonising the artificial reef were a subset of those on the surrounding natural reef, but occurred in different proportions--Pseudosiderastrea tayami, Mycedium elephantotus and Leptastrea purpurea being disproportionately abundant on the artificial reef. Coral cover increased rapidly in the later stages of the study, reaching 2.3 ± 0.7 SE % at 62 months post construction. This study indicates that simple materials of opportunity can provide a suitable substrate for coral recruitment in Dampier Harbour, and that natural colonisation at the study site remains sufficient to initiate a coral community on artificial substrate despite ongoing natural and anthropogenic perturbations.

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

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 Hospital of the Future. Megatrends, Driving Forces, Barriers to Implementation, Overarching Perspectives, Major Trends into the Future, Implications for TATRC And Specific Recommendations for Action

DTIC Science & Technology

2008-10-01

Healthcare Systems Will Be Those That Work With Data/Info In New Ways • Artificial Intelligence Will Come to the Fore o Effectively Acquire...Education • Artificial Intelligence Will Assist in o History and Physical Examination o Imaging Selection via algorithms o Test Selection via algorithms...medical language into a simulation model based upon artificial intelligence , and • the content verification and validation of the cognitive

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

Artificial selection on ant female caste ratio uncovers a link between female-biased sex ratios and infection by Wolbachia endosymbionts.

PubMed

Pontieri, L; Schmidt, A M; Singh, R; Pedersen, J S; Linksvayer, T A

2017-02-01

Social insect sex and caste ratios are well-studied targets of evolutionary conflicts, but the heritable factors affecting these traits remain unknown. To elucidate these factors, we carried out a short-term artificial selection study on female caste ratio in the ant Monomorium pharaonis. Across three generations of bidirectional selection, we observed no response for caste ratio, but sex ratios rapidly became more female-biased in the two replicate high selection lines and less female-biased in the two replicate low selection lines. We hypothesized that this rapid divergence for sex ratio was caused by changes in the frequency of infection by the heritable bacterial endosymbiont Wolbachia, because the initial breeding stock varied for Wolbachia infection, and Wolbachia is known to cause female-biased sex ratios in other insects. Consistent with this hypothesis, the proportions of Wolbachia-infected colonies in the selection lines changed rapidly, mirroring the sex ratio changes. Moreover, the estimated effect of Wolbachia on sex ratio (~13% female bias) was similar in colonies before and during artificial selection, indicating that this Wolbachia effect is likely independent of the effects of artificial selection on other heritable factors. Our study provides evidence for the first case of endosymbiont sex ratio manipulation in a social insect. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

The total artificial heart

PubMed Central

Cook, Jason A.; Shah, Keyur B.; Quader, Mohammed A.; Cooke, Richard H.; Kasirajan, Vigneshwar; Rao, Kris K.; Smallfield, Melissa C.; Tchoukina, Inna

2015-01-01

The total artificial heart (TAH) is a form of mechanical circulatory support in which the patient’s native ventricles and valves are explanted and replaced by a pneumatically powered artificial heart. Currently, the TAH is approved for use in end-stage biventricular heart failure as a bridge to heart transplantation. However, with an increasing global burden of cardiovascular disease and congestive heart failure, the number of patients with end-stage heart failure awaiting heart transplantation now far exceeds the number of available hearts. As a result, the use of mechanical circulatory support, including the TAH and left ventricular assist device (LVAD), is growing exponentially. The LVAD is already widely used as destination therapy, and destination therapy for the TAH is under investigation. While most patients requiring mechanical circulatory support are effectively treated with LVADs, there is a subset of patients with concurrent right ventricular failure or major structural barriers to LVAD placement in whom TAH may be more appropriate. The history, indications, surgical implantation, post device management, outcomes, complications, and future direction of the TAH are discussed in this review. PMID:26793338

In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

PubMed Central

Lu, Peng; Chen, Jianxin; Zhao, Huihui; Gao, Yibo; Luo, Liangtao; Zuo, Xiaohan; Shi, Qi; Yang, Yiping; Yi, Jianqiang; Wang, Wei

2012-01-01

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM. PMID:22567030

Tracking footprints of artificial selection in the dog genome.

PubMed

Akey, Joshua M; Ruhe, Alison L; Akey, Dayna T; Wong, Aaron K; Connelly, Caitlin F; Madeoy, Jennifer; Nicholas, Thomas J; Neff, Mark W

2010-01-19

The size, shape, and behavior of the modern domesticated dog has been sculpted by artificial selection for at least 14,000 years. The genetic substrates of selective breeding, however, remain largely unknown. Here, we describe a genome-wide scan for selection in 275 dogs from 10 phenotypically diverse breeds that were genotyped for over 21,000 autosomal SNPs. We identified 155 genomic regions that possess strong signatures of recent selection and contain candidate genes for phenotypes that vary most conspicuously among breeds, including size, coat color and texture, behavior, skeletal morphology, and physiology. In addition, we demonstrate a significant association between HAS2 and skin wrinkling in the Shar-Pei, and provide evidence that regulatory evolution has played a prominent role in the phenotypic diversification of modern dog breeds. Our results provide a first-generation map of selection in the dog, illustrate how such maps can rapidly inform the genetic basis of canine phenotypic variation, and provide a framework for delineating the mechanistic basis of how artificial selection promotes rapid and pronounced phenotypic evolution.

Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels.

PubMed

Mueller, Amy V; Hemond, Harold F

2013-12-15

A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non-linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures. Conductivity measurements and the concept of charge balance are incorporated into the ANN structure, resulting in (1) removal of estimation bias typically seen with use of ISEs in mixtures of unknown composition and (2) improvement of signal estimation by an order of magnitude or more for both major and minor constituents relative to use of ISEs as stand-alone sensors and error reduction by 30-50% relative to use of standard ANN models. This method is suggested as an alternative to parameterization of traditional models (e.g., Nikolsky-Eisenman), for which parameters are strongly dependent on both analyte concentration and temperature, and to standard ANN models which have no mechanism for incorporation of system knowledge. Network architecture and weighting are presented for the base case where the dot product can be used to relate ion concentrations to both conductivity and charge balance as well as for an extension to log-normalized data where the model can no longer be represented in this manner. While parameterization in this case study is analyte-dependent, the architecture is generalizable, allowing application of this method to other environmental problems for which mathematical constraints can be explicitly stated. © 2013 Elsevier B.V. All rights reserved.

Introduction to the Special Issue on Innovative Applications of Artificial Intelligence 2014

DOE PAGES

Stracuzzi, David J.; Gunning, David

2015-09-28

This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence held in Quebec City, Canada. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

Introduction to the Special Issue on Innovative Applications of Artificial Intelligence 2014

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

Stracuzzi, David J.; Gunning, David

This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence held in Quebec City, Canada. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

Nucleocytoplasmic Transport: A Paradigm for Molecular Logistics in Artificial Systems.

PubMed

Vujica, Suncica; Zelmer, Christina; Panatala, Radhakrishnan; Lim, Roderick Y H

2016-01-01

Artificial organelles, molecular factories and nanoreactors are membrane-bound systems envisaged to exhibit cell-like functionality. These constitute liposomes, polymersomes or hybrid lipo-polymersomes that display different membrane-spanning channels and/or enclose molecular modules. To achieve more complex functionality, an artificial organelle should ideally sustain a continuous influx of essential macromolecular modules (i.e. cargoes) and metabolites against an outflow of reaction products. This would benefit from the incorporation of selective nanopores as well as specific trafficking factors that facilitate cargo selectivity, translocation efficiency, and directionality. Towards this goal, we describe how proteinaceous cargoes are transported between the nucleus and cytoplasm by nuclear pore complexes and the biological trafficking machinery in living cells (i.e. nucleocytoplasmic transport). On this basis, we discuss how biomimetic control may be implemented to selectively import, compartmentalize and accumulate diverse macromolecular modules against concentration gradients in artificial organelles.

Artificial Intelligence in Cardiology.

PubMed

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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.

An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling

NASA Astrophysics Data System (ADS)

Moghadamfalahi, Mohammad; Akcakaya, Murat; Nezamfar, Hooman; Sourati, Jamshid; Erdogmus, Deniz

2017-10-01

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.

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

Efficient production of artificially designed gelatins with a Bacillus brevis system.

PubMed

Kajino, T; Takahashi, H; Hirai, M; Yamada, Y

2000-01-01

Artificially designed gelatins comprising tandemly repeated 30-amino-acid peptide units derived from human alphaI collagen were successfully produced with a Bacillus brevis system. The DNA encoding the peptide unit was synthesized by taking into consideration the codon usage of the host cells, but no clones having a tandemly repeated gene were obtained through the above-mentioned strategy. Minirepeat genes could be selected in vivo from a mixture of every possible sequence encoding an artificial gelatin by randomly ligating the mixed sequence unit and transforming it into Escherichia coli. Larger repeat genes constructed by connecting minirepeat genes obtained by in vivo selection were also stable in the expression host cells. Gelatins derived from the eight-unit and six-unit repeat genes were extracellularly produced at the level of 0.5 g/liter and easily purified by ammonium sulfate fractionation and anion-exchange chromatography. The purified artificial gelatins had the predicted N-terminal sequences and amino acid compositions and a solgel property similar to that of the native gelatin. These results suggest that the selection of a repeat unit sequence stable in an expression host is a shortcut for the efficient production of repetitive proteins and that it can conveniently be achieved by the in vivo selection method. This study revealed the possible industrial application of artificially designed repetitive proteins.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Domestication and fitness in the wild: A multivariate view.

PubMed

Tufto, Jarle

2017-09-01

Domesticated species continually escaping and interbreeding with wild relatives impose a migration load on wild populations. As domesticated stocks become increasingly different as a result of artificial and natural selection in captivity, fitness of escapees in the wild is expected to decline, reducing the effective rate of migration into wild populations. Recent theory suggest that this may alleviate and eventually eliminate the resulting migration load. I develop a multivariate model of trait and wild fitness evolution resulting from the joint effects of artificial and natural selection in the captive environment. Initially, the evolutionary trajectory is dominated by the effects of artificial selection causing a fast initial decline in fitness of escapees in the wild. In later phases, through the counteracting effects of correlational multivariate natural selection in captivity, the mean phenotype is pushed in directions of weak stabilizing selection, allowing a sustained response in the trait subject to artificial selection. Provided that there is some alignment between the adaptive landscapes in the wild and in captivity, these phases are associated with slower rates of decline in wild fitness of the domesticated stock, suggesting that detrimental effects on wild populations are likely to remain a concern in the foreseeable future. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Artificially intelligent recognition of Arabic speaker using voice print-based local features

NASA Astrophysics Data System (ADS)

Mahmood, Awais; Alsulaiman, Mansour; Muhammad, Ghulam; Akram, Sheeraz

2016-11-01

Local features for any pattern recognition system are based on the information extracted locally. In this paper, a local feature extraction technique was developed. This feature was extracted in the time-frequency plain by taking the moving average on the diagonal directions of the time-frequency plane. This feature captured the time-frequency events producing a unique pattern for each speaker that can be viewed as a voice print of the speaker. Hence, we referred to this technique as voice print-based local feature. The proposed feature was compared to other features including mel-frequency cepstral coefficient (MFCC) for speaker recognition using two different databases. One of the databases used in the comparison is a subset of an LDC database that consisted of two short sentences uttered by 182 speakers. The proposed feature attained 98.35% recognition rate compared to 96.7% for MFCC using the LDC subset.

Study on digital teeth selection and virtual teeth arrangement for complete denture.

PubMed

Yu, Xiaoling; Cheng, Xiaosheng; Dai, Ning; Chen, Hu; Yu, Changjiang; Sun, Yuchun

2018-03-01

In dentistry, the complete denture is a conventional treatment for edentulous patients. The computer-aided design and computer-aided manufacturing (CAD/CAM) has been applied on the digital complete denture which is developed rapidly. Tooth selection and arrangement is one of the most important parts in digital complete denture. In this paper, we propose a new method of personalized teeth arrangement. This paper presents a method of arranging teeth virtually for a complete denture. First, scan and extract the feature points of the 3D triangular mesh data of artificial teeth (PLY format), then establish a tooth selection system. Second, scan and mark the anatomic characteristics of the maxillary and mandibular cast surfaces, such as facial midline, the curve of the arches. With the enter information, the study calculates the common arrangement lines of artificial teeth. Third, select the preferred artificial teeth and automatically arrange them virtually in the correct position by using our own software. After that, design the gingival part of the dentures on the basic of the arranged teeth on the screen and then fabricated it by using Computerized Numerical Control (CNC) technology, Rapid Prototyping (RP) technology or 3D printer technology. Finally, select artificial teeth were embedded in wax rims. This system can choose artificial teeth reasonably and the teeth placement can meet the dentist's request to a certain extent, whereas all the operations are based on the medical principles. The study performed here involves computer sciences, medicine, and dentistry, a teeth selection system was proposed and virtual teeth arrangement was described. This study has the capacity of helping operators to select teeth, which improved the accuracy of tooth arrangement, and customized complete denture. Copyright © 2017 Elsevier B.V. All rights reserved.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

PubMed Central

2013-01-01

Background Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. Methods We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. Results The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO. PMID:23617960

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

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

Molecular footprints of domestication and improvement in soybean revealed by whole genome re-sequencing

PubMed Central

2013-01-01

Background Artificial selection played an important role in the origin of modern Glycine max cultivars from the wild soybean Glycine soja. To elucidate the consequences of artificial selection accompanying the domestication and modern improvement of soybean, 25 new and 30 published whole-genome re-sequencing accessions, which represent wild, domesticated landrace, and Chinese elite soybean populations were analyzed. Results A total of 5,102,244 single nucleotide polymorphisms (SNPs) and 707,969 insertion/deletions were identified. Among the SNPs detected, 25.5% were not described previously. We found that artificial selection during domestication led to more pronounced reduction in the genetic diversity of soybean than the switch from landraces to elite cultivars. Only a small proportion (2.99%) of the whole genomic regions appear to be affected by artificial selection for preferred agricultural traits. The selection regions were not distributed randomly or uniformly throughout the genome. Instead, clusters of selection hotspots in certain genomic regions were observed. Moreover, a set of candidate genes (4.38% of the total annotated genes) significantly affected by selection underlying soybean domestication and genetic improvement were identified. Conclusions Given the uniqueness of the soybean germplasm sequenced, this study drew a clear picture of human-mediated evolution of the soybean genomes. The genomic resources and information provided by this study would also facilitate the discovery of genes/loci underlying agronomically important traits. PMID:23984715

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.

A Study for the Feature Selection to Identify GIEMSA-Stained Human Chromosomes Based on Artificial Neural Network

DTIC Science & Technology

2001-10-25

neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial

Recent artificial selection in U.S. Jersey cattle impacts autozygosity levels of specific genomic regions

USDA-ARS?s Scientific Manuscript database

Genome signatures of artificial selection in U.S. Jersey cattle were identified by examining changes in haplotype homozygosity for a resource population of animals born between 1962 and 2005. Genetic merit of this population changed dramatically during this period for a number of traits, especially ...

Genome changes due to forty years of artificial selection associated with divergent dairy production and reproduction

USDA-ARS?s Scientific Manuscript database

Artificial selection in dairy cattle since 1964 has achieved steady increase in milk production that was accompanied by unintended declines in fertility. Direct comparison of 45,878 SNPs between a group of Holstein cattle unselected since 1964 and the contemporary Holsteins was conducted to identify...

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

Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

NASA Astrophysics Data System (ADS)

Salamatova, T.; Zhukov, V.

2017-02-01

The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

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

Stabilizing selection on sperm number revealed by artificial selection and experimental evolution.

PubMed

Cattelan, Silvia; Di Nisio, Andrea; Pilastro, Andrea

2018-03-01

Sperm competition is taxonomically widespread in animals and is usually associated with large sperm production, being the number of sperm in the competing pool the prime predictor of fertilization success. Despite the strong postcopulatory selection acting directionally on sperm production, its genetic variance is often very high. This can be explained by trade-offs between sperm production and traits associated with mate acquisition or survival, that may contribute to generate an overall stabilizing selection. To investigate this hypothesis, we first artificially selected male guppies (Poecilia reticulata) for high and low sperm production for three generations, while simultaneously removing sexual selection. Then, we interrupted artificial selection and restored sexual selection. Sperm production responded to divergent selection in one generation, and when we restored sexual selection, both high and low lines converged back to the mean sperm production of the original population within two generations, indicating that sperm number is subject to strong stabilizing total sexual selection (i.e., selection acting simultaneously on all traits associated with reproductive success). We discuss the possible mechanisms responsible for the maintenance of high genetic variability in sperm production despite strong selection acting on it. © 2018 The Author(s). Evolution © 2018 The Society for the Study of Evolution.

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.

Direct and correlated responses to artificial selection on male mating frequency in the stalk-eyed fly Cyrtodiopsis dalmanni.

PubMed

Rogers, D W; Baker, R H; Chapman, T; Denniff, M; Pomiankowski, A; Fowler, K

2005-05-01

Traditionally it was thought that fitness-related traits such as male mating frequency, with a history of strong directional selection, should have little additive genetic variance and thus respond asymmetrically to bidirectional artificial selection. However, recent findings and theory suggest that a balance between selection for increased male mating frequency and opposing selection pressures on physiologically linked traits will cause male mating frequency to have high additive genetic variation and hence respond symmetrically to selection. We tested these hypotheses in the stalk-eyed fly, Cyrtodiopsis dalmanni, in which males hold harems comprising many females and so have the opportunity to mate at extremely high frequencies. We subjected male stalk-eyed flies to artificial selection for increased ('high') and decreased ('low') mating frequency in the presence of ecologically realistic, high numbers of females. High line males mated significantly more often than control or low line males. The direct response to selection was approximately symmetric in the high and low lines, revealing high additive genetic variation for, and no significant genetic constraints on, increased male mating frequency in C. dalmanni. In order to investigate trade-offs that might constrain male mating frequency under natural conditions we examined correlated responses to artificial selection. We measured accessory gland length, testis length and eyespan after 7 and 14 generations of selection. High line males had significantly larger accessory glands than low line males. No consistent correlated responses to selection were found in testis length or eyespan. Our results suggest that costs associated with the production and maintenance of large accessory glands, although yet to be identified, are likely to be a major constraint on mating frequency in natural populations of C. dalmanni.

An ArcGIS decision support tool for artificial reefs site selection (ArcGIS ARSS)

NASA Astrophysics Data System (ADS)

Stylianou, Stavros; Zodiatis, George

2017-04-01

Although the use and benefits of artificial reefs, both socio-economic and environmental, have been recognized with research and national development programmes worldwide their development is rarely subjected to a rigorous site selection process and the majority of the projects use the traditional (non-GIS) approach, based on trial and error mode. Recent studies have shown that the use of Geographic Information Systems, unlike to traditional methods, for the identification of suitable areas for artificial reefs siting seems to offer a number of distinct advantages minimizing possible errors, time and cost. A decision support tool (DSS) has been developed based on the existing knowledge, the multi-criteria decision analysis techniques and the GIS approach used in previous studies in order to help the stakeholders to identify the optimal locations for artificial reefs deployment on the basis of the physical, biological, oceanographic and socio-economic features of the sites. The tool provides to the users the ability to produce a final report with the results and suitability maps. The ArcGIS ARSS support tool runs within the existing ArcMap 10.2.x environment and for the development the VB .NET high level programming language has been used along with ArcObjects 10.2.x. Two local-scale case studies were conducted in order to test the application of the tool focusing on artificial reef siting. The results obtained from the case studies have shown that the tool can be successfully integrated within the site selection process in order to select objectively the optimal site for artificial reefs deployment.

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.

Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment.

PubMed

Nirouei, Mahyar; Ghasemi, Ghasem; Abdolmaleki, Parviz; Tavakoli, Abdolreza; Shariati, Shahab

2012-06-01

The antiviral drugs that inhibit human immunodeficiency virus (HIV) entry to the target cells are already in different phases of clinical trials. They prevent viral entry and have a highly specific mechanism of action with a low toxicity profile. Few QSAR studies have been performed on this group of inhibitors. This study was performed to develop a quantitative structure-activity relationship (QSAR) model of the biological activity of indole glyoxamide derivatives as inhibitors of the interaction between HIV glycoprotein gp120 and host cell CD4 receptors. Forty different indole glyoxamide derivatives were selected as a sample set and geometrically optimized using Gaussian 98W. Different combinations of multiple linear regression (MLR), genetic algorithms (GA) and artificial neural networks (ANN) were then utilized to construct the QSAR models. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear log (1/EC50) prediction. The results that were obtained using GA-ANN were compared with MLR-MLR and MLR-ANN models. A high predictive ability was observed for the MLR, MLR-ANN and GA-ANN models, with root mean sum square errors (RMSE) of 0.99, 0.91 and 0.67, respectively (N = 40). In summary, machine learning methods were highly effective in designing QSAR models when compared to statistical method.

Computer vision-based method for classification of wheat grains using artificial neural network.

PubMed

Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

2017-06-01

A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures

PubMed Central

González-Calabuig, Andreu; Cetó, Xavier

2018-01-01

This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L−1, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948. PMID:29342848

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…

Ion transport through lipid bilayers by synthetic ionophores: modulation of activity and selectivity.

PubMed

De Riccardis, Francesco; Izzo, Irene; Montesarchio, Daniela; Tecilla, Paolo

2013-12-17

The ion-coupled processes that occur in the plasma membrane regulate the cell machineries in all the living organisms. The details of the chemical events that allow ion transport in biological systems remain elusive. However, investigations of the structure and function of natural and artificial transporters has led to increasing insights about the conductance mechanisms. Since the publication of the first successful artificial system by Tabushi and co-workers in 1982, synthetic chemists have designed and constructed a variety of chemically diverse and effective low molecular weight ionophores. Despite their relative structural simplicity, ionophores must satisfy several requirements. They must partition in the membrane, interact specifically with ions, shield them from the hydrocarbon core of the phospholipid bilayer, and transport ions from one side of the membrane to the other. All these attributes require amphipathic molecules in which the polar donor set used for ion recognition (usually oxygens for cations and hydrogen bond donors for anions) is arranged on a lipophilic organic scaffold. Playing with these two structural motifs, donor atoms and scaffolds, researchers have constructed a variety of different ionophores, and we describe a subset of interesting examples in this Account. Despite the ample structural diversity, structure/activity relationships studies reveal common features. Even when they include different hydrophilic moieties (oxyethylene chains, free hydroxyl, etc.) and scaffolds (steroid derivatives, neutral or polar macrocycles, etc.), amphipathic molecules, that cannot span the entire phospholipid bilayer, generate defects in the contact zone between the ionophore and the lipids and increase the permeability in the bulk membrane. Therefore, topologically complex structures that span the entire membrane are needed to elicit channel-like and ion selective behaviors. In particular the alternate-calix[4]arene macrocycle proved to be a versatile platform to obtain 3D-structures that can form unimolecular channels in membranes. In these systems, the selection of proper donor groups allows us to control the ion selectivity of the process. We can switch from cation to anion transport by substituting protonated amines for the oxygen donors. Large and stable tubular structures with nanometric sized transmembrane nanopores that provide ample internal space represent a different approach for the preparation of synthetic ion channels. We used the metal-mediated self-assembly of porphyrin ligands with Re(I) corners as a new method for producing to robust channel-like structures. Such structures can survive in the complex membrane environment and show interesting ionophoric behavior. In addition to the development of new design principles, the selective modification of the biological membrane permeability could lead to important developments in medicine and technology.

Problems of selecting donors for artificial insemination

PubMed Central

Schoysman, R

1975-01-01

This paper is concerned with only one of the problems encountered in selecting donors for artificial insemination, that of choosing suitable donors. In Belgium medical students have generally been the donors of semen but Dr Schoysman examines the other choices of potential donors and outlines certain criteria for selecting them: these criteria are more explicit than those outlined by Professor Kerr and Miss Rogers on page 32. He also touches on the question of payment to donors. PMID:1165573

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

A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pinctada fucata.

PubMed

Zou, Keshu; Zhang, Dianchang; Guo, Huayang; Zhu, Caiyan; Li, Min; Jiang, Shigui

2014-05-25

Pearl oyster Pinctada fucata is widely cultured to produce seawater pearl in South China, and the quality of pearl is significantly affected by its shell color. Thus the Pearl Oyster Selective Breeding Program (POSBP) was carried out for the shell color and growth traits. The black (B), gold (G), red (R) and white (W) shell strains with fast growth trait were achieved after five successive generation selection. In this study, AFLP technique was used to scan genome of four strains with different shell colors to identify the candidate markers under artificial selection. Eight AFLP primer combinations were screened and yielded 688 loci, 676 (98.26%) of which were polymorphic. In black, gold, red and white strains, the percentage of polymorphic loci was 90.41%, 87.79%, 93.60% and 93.31%, respectively, Nei's gene diversity was 0.3225, 0.2829, 0.3221 and 0.3292, Shannon's information index was 0.4801, 0.4271, 0.4825 and 0.4923, and the value of FST was 0.1805. These results suggested that the four different shell color strains had high genetic diversity and great genetic differentiation among strains, which had been subjected to the continuous selective pressures during the artificial selective breeding. Furthermore, six outlier loci were considered as the candidate markers under artificial selection for shell color. This study provides a molecular evidence for the inheritance of shell color of P. fucata. Copyright © 2014 Elsevier B.V. All rights reserved.

A proto-architecture for innate directionally selective visual maps.

PubMed

Adams, Samantha V; Harris, Chris M

2014-01-01

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.

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.

Home artificial nutrition in advanced cancer patients.

PubMed

Ruggeri, Enrico; Agostini, Federica; Fettucciari, Luana; Giannantonio, Marilena; Pironi, Loris; Pannuti, Franco

2013-01-01

Malnutrition is over 50% in advanced cancer patients and is related to a decreased survival. Cachexia is the first reason for death in 4-23% of cases. The aim of the study was to estimate the appropriateness of the criteria to select patients for home artificial nutrition and its effectiveness to avoid death from cachexia and to improve quality of life in patients with advanced cancer assisted at home by the National Tumor Association (ANT) Foundation. The criteria for patient selection are: inadequate caloric intake ± malnutrition; life expectancy ≥6 weeks; suitable psycho-physical conditions; informed consent. The measured parameters were sex, age, tumor site, food intake, nutritional status, Karnofsky performance status, indication for home artificial nutrition, type of home artificial nutrition (enteral or parenteral), and survival after starting home artificial nutrition. The ANT Foundation assisted 29,348 patients in Bologna and its province from July 1990 to July 2012. Home artificial nutrition had been submitted to 618 patients (2.1%): enteral to 285/618 (46.1%) and parenteral to 333/618 (53.9%). Access routes for home artificial nutrition were: 39% nasogastric tube, 26% percutaneous endoscopic gastrostomy, 33% digiunostomy, and 2% gastrostomy. The central venous catheters used for home artificial nutrition were: 61% non-tunneled, 13 peripherally inserted, 8% partially tunneled, and 18% totally implanted. By July 2012, all the patients had died. Duration of life ≥6 weeks was 78% (484/618). Karnofsky performance status was related to survival ( P <0.0001): one month after starting home artificial nutrition, it decreased in 73 patients (12%), was unchanged in 414 (67%), and increased in 131 (21%). The low incidence of home artificial nutrition over all the patients assisted by the ANT Foundation and the achievement to avoid death from cachexia in 78% prove the efficacy of the criteria of patient selection in order to prevent its excessive and indiscriminate use. It was effective in maintaining and improving the performance status in 88% of patients. Karnofsky performance status is a reliable prognostic index to start home artificial nutrition.

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

Lex, Alexander; Partl, Christian; Kalkofen, Denis; Streit, Marc; Gratzl, Samuel; Wassermann, Anne Mai; Schmalstieg, Dieter; Pfister, Hanspeter

2014-01-01

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Fig. 1Entourage showing the Glioma pathway in detail and contextual information of multiple related pathways. PMID:24051820

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.

Creating and Evaluating Artificial Domiciles for Bumble Bees

ERIC Educational Resources Information Center

Golick, Douglas A.; Ellis, Marion D.; Beecham, Brady

2006-01-01

Bumble bees are valuable pollinators of native and cultivated flora. Despite our knowledge of bumble bee nest site selection, most efforts to attract bumble bees to artificial domiciles have been met with limited success. Creating and evaluating artificial domiciles provides students an opportunity to investigate a real problem. In this lesson,…

Advanced Taste Sensors Based on Artificial Lipids with Global Selectivity to Basic Taste Qualities and High Correlation to Sensory Scores

PubMed Central

Kobayashi, Yoshikazu; Habara, Masaaki; Ikezazki, Hidekazu; Chen, Ronggang; Naito, Yoshinobu; Toko, Kiyoshi

2010-01-01

Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets. PMID:22319306

An Assessment of Artificial Compressibility and Pressure Projection Methods for Incompressible Flow Simulations

NASA Technical Reports Server (NTRS)

Kwak, Dochan; Kiris, C.; Smith, Charles A. (Technical Monitor)

1998-01-01

Performance of the two commonly used numerical procedures, one based on artificial compressibility method and the other pressure projection method, are compared. These formulations are selected primarily because they are designed for three-dimensional applications. The computational procedures are compared by obtaining steady state solutions of a wake vortex and unsteady solutions of a curved duct flow. For steady computations, artificial compressibility was very efficient in terms of computing time and robustness. For an unsteady flow which requires small physical time step, pressure projection method was found to be computationally more efficient than an artificial compressibility method. This comparison is intended to give some basis for selecting a method or a flow solution code for large three-dimensional applications where computing resources become a critical issue.

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

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...

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification.

PubMed

Ballabio, Davide; Consonni, Viviana; Mauri, Andrea; Todeschini, Roberto

2010-01-11

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks' Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.

Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy

NASA Astrophysics Data System (ADS)

Somers, Ben; Asner, Gregory P.

2013-10-01

The use of imaging spectroscopy for florisic mapping of forests is complicated by the spectral similarity among coexisting species. Here we evaluated an alternative spectral unmixing strategy combining a time series of EO-1 Hyperion images and an automated feature selection strategy in MESMA. Instead of using the same spectral subset to unmix each image pixel, our modified approach allowed the spectral subsets to vary on a per pixel basis such that each pixel is evaluated using a spectral subset tuned towards maximal separability of its specific endmember class combination or species mixture. The potential of the new approach for floristic mapping of tree species in Hawaiian rainforests was quantitatively demonstrated using both simulated and actual hyperspectral image time-series. With a Cohen's Kappa coefficient of 0.65, our approach provided a more accurate tree species map compared to MESMA (Kappa = 0.54). In addition, by the selection of spectral subsets our approach was about 90% faster than MESMA. The flexible or adaptive use of band sets in spectral unmixing as such provides an interesting avenue to address spectral similarities in complex vegetation canopies.

Northeast Artificial Intelligence Consortium Annual Report - 1988. Volume 4. Distributed AI for Communications Network Management

DTIC Science & Technology

1989-10-01

apiots to rerlliii their los1 ’ at act ions iii Ilie rouist ’lirt iii of thle plain. Th’lis fast piece of iiiforiat liolu is plrovidied tlioiigli Ow 1use of...maximum compatible sets and delete subsets otherwise for every plan fragment pf, for g,. tile first goal in goals, if p.f- does not exceed resource... deletes an non-default assumption. 4.5.3.2 Data Structures The MATMS is a frame-based system in which there are five basic types of objects: beliefs

The neighbourhood polynomial of some families of dendrimers

NASA Astrophysics Data System (ADS)

Nazri Husin, Mohamad; Hasni, Roslan

2018-04-01

The neighbourhood polynomial N(G,x) is generating function for the number of faces of each cardinality in the neighbourhood complex of a graph and it is defined as (G,x)={\\sum }U\\in N(G){x}|U|, where N(G) is neighbourhood complex of a graph, whose vertices of the graph and faces are subsets of vertices that have a common neighbour. A dendrimers is an artificially manufactured or synthesized molecule built up from branched units called monomers. In this paper, we compute this polynomial for some families of dendrimer.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

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

Power Analysis of Artificial Selection Experiments Using Efficient Whole Genome Simulation of Quantitative Traits

PubMed Central

Kessner, Darren; Novembre, John

2015-01-01

Evolve and resequence studies combine artificial selection experiments with massively parallel sequencing technology to study the genetic basis for complex traits. In these experiments, individuals are selected for extreme values of a trait, causing alleles at quantitative trait loci (QTL) to increase or decrease in frequency in the experimental population. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework that explicitly models whole genomes of individuals, quantitative traits, and selection based on individual trait values. We find that explicitly modeling QTL provides qualitatively different insights than considering independent loci with constant selection coefficients. Specifically, we observe how interference between QTL under selection affects the trajectories and lengthens the fixation times of selected alleles. We also show that a substantial portion of the genetic variance of the trait (50–100%) can be explained by detected QTL in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, we show that power depends crucially on the opportunity for recombination during the experiment. Finally, we show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates. PMID:25672748

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

Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.

PubMed

Feng, Lei; Peng, Fuduan; Li, Shanfei; Jiang, Li; Sun, Hui; Ji, Anquan; Zeng, Changqing; Li, Caixia; Liu, Fan

2018-03-23

Estimating individual age from biomarkers may provide key information facilitating forensic investigations. Recent progress has shown DNA methylation at age-associated CpG sites as the most informative biomarkers for estimating the individual age of an unknown donor. Optimal feature selection plays a critical role in determining the performance of the final prediction model. In this study we investigate methylation levels at 153 age-associated CpG sites from 21 previously reported genomic regions using the EpiTYPER system for their predictive power on individual age in 390 Han Chinese males ranging from 15 to 75 years of age. We conducted a systematic feature selection using a stepwise backward multiple linear regression analysis as well as an exhaustive searching algorithm. Both approaches identified the same subset of 9 CpG sites, which in linear combination provided the optimal model fitting with mean absolute deviation (MAD) of 2.89 years of age and explainable variance (R 2 ) of 0.92. The final model was validated in two independent Han Chinese male samples (validation set 1, N = 65, MAD = 2.49, R 2  = 0.95, and validation set 2, N = 62, MAD = 3.36, R 2  = 0.89). Other competing models such as support vector machine and artificial neural network did not outperform the linear model to any noticeable degree. The validation set 1 was additionally analyzed using Pyrosequencing technology for cross-platform validation and was termed as validation set 3. Directly applying our model, in which the methylation levels were detected by the EpiTYPER system, to the data from pyrosequencing technology showed, however, less accurate results in terms of MAD (validation set 3, N = 65 Han Chinese males, MAD = 4.20, R 2  = 0.93), suggesting the presence of a batch effect between different data generation platforms. This batch effect could be partially overcome by a z-score transformation (MAD = 2.76, R 2  = 0.93). Overall, our systematic feature selection identified 9 CpG sites as the optimal subset for forensic age estimation and the prediction model consisting of these 9 markers demonstrated high potential in forensic practice. An age estimator implementing our prediction model allowing missing markers is freely available at http://liufan.big.ac.cn/AgePrediction. Copyright © 2018 Elsevier B.V. All rights reserved.

Natural selection stops the evolution of male attractiveness

PubMed Central

Hine, Emma; McGuigan, Katrina; Blows, Mark W.

2011-01-01

Sexual selection in natural populations acts on highly heritable traits and tends to be relatively strong, implicating sexual selection as a causal agent in many phenotypic radiations. Sexual selection appears to be ineffectual in promoting phenotypic divergence among contemporary natural populations, however, and there is little evidence from artificial selection experiments that sexual fitness can evolve. Here, we demonstrate that a multivariate male trait preferred by Drosophila serrata females can respond to selection and results in the maintenance of male mating success. The response to selection was associated with a gene of major effect increasing in frequency from 12 to 35% in seven generations. No further response to selection, or increase in frequency of the major gene, was observed between generations 7 and 11, indicating an evolutionary limit had been reached. Genetic analyses excluded both depletion of genetic variation and overdominance as causes of the evolutionary limit. Relaxing artificial selection resulted in the loss of 52% of the selection response after a further five generations, demonstrating that the response under artificial sexual selection was opposed by antagonistic natural selection. We conclude that male D. serrata sexually selected traits, and attractiveness to D. serrata females conferred by these traits, were held at an evolutionary limit by the lack of genetic variation that would allow an increase in sexual fitness while simultaneously maintaining nonsexual fitness. Our results suggest that sexual selection is unlikely to cause divergence among natural populations without a concomitant change in natural selection, a conclusion consistent with observational evidence from natural populations. PMID:21321197

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

Near-infrared image-guided laser ablation of artificial caries lesions.

PubMed

Tao, You-Chen; Fan, Kenneth; Fried, Daniel

2007-01-01

Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. The objective of this study was to test the hypothesis that two-dimensional NIR images of demineralized tooth surfaces can be used to guide CO(2) laser ablation for the selective removal of artificial caries lesions. Highly patterned artificial lesions were produced by submerging 5 × 5 mm(2) bovine enamel samples in demineralized solution for a 9-day period while sound areas were protected with acid resistant varnish. NIR imaging and polarization sensitive optical coherence tomography (PS-OCT) were used to acquire depth-resolved images at a wavelength of 1310-nm. An imaging processing module was developed to analyze the NIR images and to generate optical maps. The optical maps were used to control a CO(2) laser for the selective removal of the lesions at a uniform depth. This experiment showed that the patterned artificial lesions were removed selectively using the optical maps with minimal damage to sound enamel areas. Post-ablation NIR and PS-OCT imaging confirmed that demineralized areas were removed while sound enamel was conserved. This study successfully demonstrated that near-IR imaging can be integrated with a CO(2) laser ablation system for the selective removal of dental caries.

Near-infrared image-guided laser ablation of artificial caries lesions

PubMed Central

Tao, You-Chen; Fan, Kenneth; Fried, Daniel

2012-01-01

Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. The objective of this study was to test the hypothesis that two–dimensional NIR images of demineralized tooth surfaces can be used to guide CO2 laser ablation for the selective removal of artificial caries lesions. Highly patterned artificial lesions were produced by submerging 5 × 5 mm2 bovine enamel samples in demineralized solution for a 9-day period while sound areas were protected with acid resistant varnish. NIR imaging and polarization sensitive optical coherence tomography (PS-OCT) were used to acquire depth-resolved images at a wavelength of 1310-nm. An imaging processing module was developed to analyze the NIR images and to generate optical maps. The optical maps were used to control a CO2 laser for the selective removal of the lesions at a uniform depth. This experiment showed that the patterned artificial lesions were removed selectively using the optical maps with minimal damage to sound enamel areas. Post-ablation NIR and PS-OCT imaging confirmed that demineralized areas were removed while sound enamel was conserved. This study successfully demonstrated that near-IR imaging can be integrated with a CO2 laser ablation system for the selective removal of dental caries. PMID:22866210

Near-infrared image-guided laser ablation of artificial caries lesions

NASA Astrophysics Data System (ADS)

Tao, You-Chen; Fan, Kenneth; Fried, Daniel

2007-02-01

Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. The objective of this study was to test the hypothesis that two-dimensional NIR images of demineralized tooth surfaces can be used to guide CO II laser ablation for the selective removal of artificial caries lesions. Highly patterned artificial lesions were produced by submerging 5 x 5 mm2 bovine enamel samples in demineralized solution for a 9-day period while sound areas were protected with acid resistant varnish. NIR imaging and polarization sensitive optical coherence tomography (PS-OCT) were used to acquire depth-resolved images at a wavelength of 1310-nm. An imaging processing module was developed to analyze the NIR images and to generate optical maps. The optical maps were used to control a CO II laser for the selective removal of the lesions at a uniform depth. This experiment showed that the patterned artificial lesions were removed selectively using the optical maps with minimal damage to sound enamel areas. Post-ablation NIR and PS-OCT imaging confirmed that demineralized areas were removed while sound enamel was conserved. This study successfully demonstrated that near-IR imaging can be integrated with a CO II laser ablation system for the selective removal of dental caries.

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.

Efficient Simulation Budget Allocation for Selecting an Optimal Subset

NASA Technical Reports Server (NTRS)

Chen, Chun-Hung; He, Donghai; Fu, Michael; Lee, Loo Hay

2008-01-01

We consider a class of the subset selection problem in ranking and selection. The objective is to identify the top m out of k designs based on simulated output. Traditional procedures are conservative and inefficient. Using the optimal computing budget allocation framework, we formulate the problem as that of maximizing the probability of correc tly selecting all of the top-m designs subject to a constraint on the total number of samples available. For an approximation of this corre ct selection probability, we derive an asymptotically optimal allocat ion and propose an easy-to-implement heuristic sequential allocation procedure. Numerical experiments indicate that the resulting allocatio ns are superior to other methods in the literature that we tested, and the relative efficiency increases for larger problems. In addition, preliminary numerical results indicate that the proposed new procedur e has the potential to enhance computational efficiency for simulation optimization.

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.

Testosterone-Induced Expression of Male Colour Morphs in Females of the Polymorphic Tawny Dragon Lizard, Ctenophorus decresii.

PubMed

Rankin, Katrina; Stuart-Fox, Devi

2015-01-01

Many colour polymorphisms are present only in one sex, usually males, but proximate mechanisms controlling the expression of sex-limited colour polymorphisms have received little attention. Here, we test the hypothesis that artificial elevation of testosterone in females of the colour polymorphic tawny dragon lizard, Ctenophorus decresii, can induce them to express the same colour morphs, in similar frequencies, to those found in males. Male C. decresii, express four discrete throat colour morphs (orange, yellow, grey and an orange central patch surrounded by yellow). We used silastic implants to experimentally elevate testosterone levels in mature females to induce colour expression. Testosterone elevation resulted in a substantial increase in the proportion and intensity of orange but not yellow colouration, which was present in a subset of females prior to treatment. Consequently, females exhibited the same set of colour morphs as males, and we confirmed that these morphs are objectively classifiable, by using digital image analyses and spectral reflectance measurements, and occur in similar frequencies as in males. These results indicate that the influence of testosterone differs for different colours, suggesting that their expression may be governed by different proximate hormonal mechanisms. Thus, caution must be exercised when using artificial testosterone manipulation to induce female expression of sex-limited colour polymorphisms. Nevertheless, the ability to express sex-limited colours (in this case orange) to reveal the same, objectively classifiable morphs in similar frequencies to males suggests autosomal rather than sex-linked inheritance, and can facilitate further research on the genetic basis of colour polymorphism, including estimating heritability and selection on colour morphs from pedigree data.

Testosterone-Induced Expression of Male Colour Morphs in Females of the Polymorphic Tawny Dragon Lizard, Ctenophorus decresii

PubMed Central

Rankin, Katrina; Stuart-Fox, Devi

2015-01-01

Many colour polymorphisms are present only in one sex, usually males, but proximate mechanisms controlling the expression of sex-limited colour polymorphisms have received little attention. Here, we test the hypothesis that artificial elevation of testosterone in females of the colour polymorphic tawny dragon lizard, Ctenophorus decresii, can induce them to express the same colour morphs, in similar frequencies, to those found in males. Male C. decresii, express four discrete throat colour morphs (orange, yellow, grey and an orange central patch surrounded by yellow). We used silastic implants to experimentally elevate testosterone levels in mature females to induce colour expression. Testosterone elevation resulted in a substantial increase in the proportion and intensity of orange but not yellow colouration, which was present in a subset of females prior to treatment. Consequently, females exhibited the same set of colour morphs as males, and we confirmed that these morphs are objectively classifiable, by using digital image analyses and spectral reflectance measurements, and occur in similar frequencies as in males. These results indicate that the influence of testosterone differs for different colours, suggesting that their expression may be governed by different proximate hormonal mechanisms. Thus, caution must be exercised when using artificial testosterone manipulation to induce female expression of sex-limited colour polymorphisms. Nevertheless, the ability to express sex-limited colours (in this case orange) to reveal the same, objectively classifiable morphs in similar frequencies to males suggests autosomal rather than sex-linked inheritance, and can facilitate further research on the genetic basis of colour polymorphism, including estimating heritability and selection on colour morphs from pedigree data. PMID:26485705

Experiments to Determine Whether Recursive Partitioning (CART) or an Artificial Neural Network Overcomes Theoretical Limitations of Cox Proportional Hazards Regression

NASA Technical Reports Server (NTRS)

Kattan, Michael W.; Hess, Kenneth R.; Kattan, Michael W.

1998-01-01

New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

PubMed

Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu

2015-04-01

Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

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

Cytogenetic map of common bean (Phaseolus vulgaris L.)

PubMed Central

Fonsêca, Artur; Ferreira, Joana; dos Santos, Tiago Ribeiro Barros; Mosiolek, Magdalena; Bellucci, Elisa; Kami, James; Gepts, Paul; Geffroy, Valérie; Schweizer, Dieter; dos Santos, Karla G. B.

2010-01-01

A cytogenetic map of common bean was built by in situ hybridization of 35 bacterial artificial chromosomes (BACs) selected with markers mapping to eight linkage groups, plus two plasmids for 5S and 45S ribosomal DNA and one bacteriophage. Together with three previously mapped chromosomes (chromosomes 3, 4, and 7), 43 anchoring points between the genetic map and the cytogenetic map of the species are now available. Furthermore, a subset of four BAC clones was proposed to identify the 11 chromosome pairs of the standard cultivar BAT93. Three of these BACs labelled more than a single chromosome pair, indicating the presence of repetitive DNA in their inserts. A repetitive distribution pattern was observed for most of the BACs; for 38% of them, highly repetitive pericentromeric or subtelomeric signals were observed. These distribution patterns corresponded to pericentromeric and subtelomeric heterochromatin blocks observed with other staining methods. Altogether, the results indicate that around half of the common bean genome is heterochromatic and that genes and repetitive sequences are intermingled in the euchromatin and heterochromatin of the species. Electronic supplementary material The online version of this article (doi:10.1007/s10577-010-9129-8) contains supplementary material, which is available to authorized users. PMID:20449646

Polymeric membrane materials for artificial organs.

PubMed

Kawakami, Hiroyoshi

2008-01-01

Many polymeric materials have already been used in the field of artificial organs. However, the materials used in artificial organs are not necessarily created with the best material selectivity and materials design; therefore, the development of synthesized polymeric membrane materials for artificial organs based on well-defined designs is required. The approaches to the development of biocompatible polymeric materials fall into three categories: (1) control of physicochemical characteristics on material surfaces, (2) modification of material surfaces using biomolecules, and (3) construction of biomimetic membrane surfaces. This review will describe current issues regarding polymeric membrane materials for use in artificial organs.

Selection signature in domesticated animals.

PubMed

Pan, Zhang-yuan; He, Xiao-yun; Wang, Xiang-yu; Guo, Xiao-fei; Cao, Xiao-han; Hu, Wen-ping; Di, Ran; Liu, Qiu-yue; Chu, Ming-xing

2016-12-20

Domesticated animals play an important role in the life of humanity. All these domesticated animals undergo same process, first domesticated from wild animals, then after long time natural and artificial selection, formed various breeds that adapted to the local environment and human needs. In this process, domestication, natural and artificial selection will leave the selection signal in the genome. The research on these selection signals can find functional genes directly, is one of the most important strategies in screening functional genes. The current studies of selection signal have been performed in pigs, chickens, cattle, sheep, goats, dogs and other domestic animals, and found a great deal of functional genes. This paper provided an overview of the types and the detected methods of selection signal, and outlined researches of selection signal in domestic animals, and discussed the key issues in selection signal analysis and its prospects.

Opportunity for natural selection among five population groups of Manipur, North East India.

PubMed

Asghar, M; Meitei, S Y; Luxmi, Y; Achoubi, N; Meitei, K S; Murry, B; Sachdeva, M P; Saraswathy, K N

2014-01-01

Opportunity for natural selection among five population groups of Manipur in comparison with other North East Indian population has been studied. Crow's index as well as Johnston and Kensinger's index for natural selection were calculated based on differential fertility and mortality. The mortality component was found to be lower compared to fertility component in all the populations which may attribute to comparatively improved and easily accessible health care facilities. However, different selection pressures, artificial and natural, seem to be influencing the selection intensity through induced abortion and spontaneous abortion among the two non-tribal migrant groups: Bamon and Muslims, respectively. This study highlights the probable interaction of artificial and natural selection in determining the evolutionary fate of any population group.

Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection.

PubMed

Edward, Dominic A; Fricke, Claudia; Chapman, Tracey

2010-08-27

Artificial selection and experimental evolution document natural selection under controlled conditions. Collectively, these techniques are continuing to provide fresh and important insights into the genetic basis of evolutionary change, and are now being employed to investigate mating behaviour. Here, we focus on how selection techniques can reveal the genetic basis of post-mating adaptations to sexual selection and sexual conflict. Alteration of the operational sex ratio of adult Drosophila over just a few tens of generations can lead to altered ejaculate allocation patterns and the evolution of resistance in females to the costly effects of elevated mating rates. We provide new data to show how male responses to the presence of rivals can evolve. For several traits, the way in which males responded to rivals was opposite in lines selected for male-biased, as opposed to female-biased, adult sex ratio. This shows that the manipulation of the relative intensity of intra- and inter-sexual selection can lead to replicable and repeatable effects on mating systems, and reveals the potential for significant contemporary evolutionary change. Such studies, with important safeguards, have potential utility for understanding sexual selection and sexual conflict across many taxa. We discuss how artificial selection studies combined with genomics will continue to deepen our knowledge of the evolutionary principles first laid down by Darwin 150 years ago.

Recombination walking: genetic selection of clones from pooled libraries of yeast artificial chromosomes by homologous recombination.

PubMed Central

Miller, A M; Savinelli, E A; Couture, S M; Hannigan, G M; Han, Z; Selden, R F; Treco, D A

1993-01-01

Recombination walking is based on the genetic selection of specific human clones from a yeast artificial chromosome (YAC) library by homologous recombination. The desired clone is selected from a pooled (unordered) YAC library, eliminating labor-intensive steps typically used in organizing and maintaining ordered YAC libraries. Recombination walking represents an efficient approach to library screening and is well suited for chromosome-walking approaches to the isolation of genes associated with common diseases. Images Fig. 1 Fig. 2 Fig. 3 Fig. 4 PMID:8367472

From wild animals to domestic pets, an evolutionary view of domestication.

PubMed

Driscoll, Carlos A; Macdonald, David W; O'Brien, Stephen J

2009-06-16

Artificial selection is the selection of advantageous natural variation for human ends and is the mechanism by which most domestic species evolved. Most domesticates have their origin in one of a few historic centers of domestication as farm animals. Two notable exceptions are cats and dogs. Wolf domestication was initiated late in the Mesolithic when humans were nomadic hunter-gatherers. Those wolves less afraid of humans scavenged nomadic hunting camps and over time developed utility, initially as guards warning of approaching animals or other nomadic bands and soon thereafter as hunters, an attribute tuned by artificial selection. The first domestic cats had limited utility and initiated their domestication among the earliest agricultural Neolithic settlements in the Near East. Wildcat domestication occurred through a self-selective process in which behavioral reproductive isolation evolved as a correlated character of assortative mating coupled to habitat choice for urban environments. Eurasian wildcats initiated domestication and their evolution to companion animals was initially a process of natural, rather than artificial, selection over time driven during their sympatry with forbear wildcats.

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.

Data splitting for artificial neural networks using SOM-based stratified sampling.

PubMed

May, R J; Maier, H R; Dandy, G C

2010-03-01

Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance; however, the choice of sampling methodology is rarely given due consideration by ANN modellers. Increased confidence in the sampling is of paramount importance, since the hold-out sampling is generally performed only once during ANN development. This paper considers the variability in the quality of subsets that are obtained using different data splitting approaches. A novel approach to stratified sampling, based on Neyman sampling of the self-organizing map (SOM), is developed, with several guidelines identified for setting the SOM size and sample allocation in order to minimize the bias and variance in the datasets. Using an example ANN function approximation task, the SOM-based approach is evaluated in comparison to random sampling, DUPLEX, systematic stratified sampling, and trial-and-error sampling to minimize the statistical differences between data sets. Of these approaches, DUPLEX is found to provide benchmark performance with good model performance, with no variability. The results show that the SOM-based approach also reliably generates high-quality samples and can therefore be used with greater confidence than other approaches, especially in the case of non-uniform datasets, with the benefit of scalability to perform data splitting on large datasets. Copyright 2009 Elsevier Ltd. All rights reserved.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

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.

Fitness variation in response to artificial selection for reduced cell area, cell number and wing area in natural populations of Drosophila melanogaster.

PubMed

Trotta, Vincenzo; Calboli, Federico C F; Ziosi, Marcello; Cavicchi, Sandro

2007-08-16

Genetically based body size differences are naturally occurring in populations of Drosophila melanogaster, with bigger flies in the cold. Despite the cosmopolitan nature of body size clines in more than one Drosophila species, the actual selective mechanisms controlling the genetic basis of body size variation are not fully understood. In particular, it is not clear what the selective value of cell size and cell area variation exactly is. In the present work we determined variation in viability, developmental time and larval competitive ability in response to crowding at two temperatures after artificial selection for reduced cell area, cell number and wing area in four different natural populations of D. melanogaster. No correlated effect of selection on viability or developmental time was observed among all selected populations. An increase in competitive ability in one thermal environment (18 degrees C) under high larval crowding was observed as a correlated response to artificial selection for cell size. Viability and developmental time are not affected by selection for the cellular component of body size, suggesting that these traits only depend on the contingent genetic makeup of a population. The higher larval competitive ability shown by populations selected for reduced cell area seems to confirm the hypothesis that cell area mediated changes have a relationship with fitness, and might be the preferential way to change body size under specific circumstances.

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.

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.

Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries

NASA Astrophysics Data System (ADS)

Le Bris, A.; Chehata, N.; Briottet, X.; Paparoditis, N.

2016-06-01

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000-2400 nm) to material classification was also shown.

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

Photochemistry in Organized Media.

ERIC Educational Resources Information Center

Fendler, Janos H.

1983-01-01

Describes common artificially produced organized media such as colloids, surfactants, and polymers and their usefulness in studying complex biochemical processes. Discusses selected recent photophysical and photochemical exploitations of these systems, including artificial photosynthesis, in situ generation of colloidal gold and platinum,…

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.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Estimating the spatial distribution of artificial groundwater recharge using multiple tracers.

PubMed

Moeck, Christian; Radny, Dirk; Auckenthaler, Adrian; Berg, Michael; Hollender, Juliane; Schirmer, Mario

2017-10-01

Stable isotopes of water, organic micropollutants and hydrochemistry data are powerful tools for identifying different water types in areas where knowledge of the spatial distribution of different groundwater is critical for water resource management. An important question is how the assessments change if only one or a subset of these tracers is used. In this study, we estimate spatial artificial infiltration along an infiltration system with stage-discharge relationships and classify different water types based on the mentioned hydrochemistry data for a drinking water production area in Switzerland. Managed aquifer recharge via surface water that feeds into the aquifer creates a hydraulic barrier between contaminated groundwater and drinking water wells. We systematically compare the information from the aforementioned tracers and illustrate differences in distribution and mixing ratios. Despite uncertainties in the mixing ratios, we found that the overall spatial distribution of artificial infiltration is very similar for all the tracers. The highest infiltration occurred in the eastern part of the infiltration system, whereas infiltration in the western part was the lowest. More balanced infiltration within the infiltration system could cause the elevated groundwater mound to be distributed more evenly, preventing the natural inflow of contaminated groundwater. Dedicated to Professor Peter Fritz on the occasion of his 80th birthday.

Social network- and community-level influences on contraceptive use: evidence from rural Poland.

PubMed

Colleran, Heidi; Mace, Ruth

2015-05-22

The diffusion of 'modern' contraceptives-as a proxy for the spread of low-fertility norms-has long interested researchers wishing to understand global fertility decline. A fundamental question is how local cultural norms and other people's behaviour influence the probability of contraceptive use, independent of women's socioeconomic and life-history characteristics. However, few studies have combined data at individual, social network and community levels to simultaneously capture multiple levels of influence. Fewer still have tested if the same predictors matter for different contraceptive types. Here, we use new data from 22 high-fertility communities in Poland to compare predictors of the use of (i) any contraceptives-a proxy for the decision to control fertility-with those of (ii) 'artificial' contraceptives-a subset of more culturally taboo methods. We find that the contraceptive behaviour of friends and family is more influential than are women's own characteristics and that community level characteristics additionally influence contraceptive use. Highly educated neighbours accelerate women's contraceptive use overall, but not their artificial method use. Highly religious neighbours slow women's artificial method use, but not their contraceptive use overall. Our results highlight different dimensions of sociocultural influence on contraceptive diffusion and suggest that these may be more influential than are individual characteristics. A comparative multilevel framework is needed to understand these dynamics.

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.

Predicting astronaut radiation doses from major solar particle events using artificial intelligence

NASA Astrophysics Data System (ADS)

Tehrani, Nazila H.

1998-06-01

Space radiation is an important issue for manned space flight. For long missions outside of the Earth's magnetosphere, there are two major sources of exposure. Large Solar Particle Events (SPEs) consisting of numerous energetic protons and other heavy ions emitted by the Sun, and the Galactic Cosmic Rays (GCRs) that constitute an isotropic radiation field of low flux and high energy. In deep-space missions both SPEs and GCRs can be hazardous to the space crew. SPEs can provide an acute dose, which is a large dose over a short period of time. The acute doses from a large SPE that could be received by an astronaut with shielding as thick as a spacesuit maybe as large as 500 cGy. GCRs will not provide acute doses, but may increase the lifetime risk of cancer from prolonged exposures in a range of 40-50 cSv/yr. In this research, we are using artificial intelligence to model the dose-time profiles during a major solar particle event. Artificial neural networks are reliable approximators for nonlinear functions. In this study we design a dynamic network. This network has the ability to update its dose predictions as new input dose data is received while the event is occurring. To accomplish this temporal behavior of the system we use an innovative Sliding Time-Delay Neural Network (STDNN). By using a STDNN one can predict doses received from large SPEs while the event is happening. The parametric fits and actual calculated doses for the skin, eye and bone marrow are used. The parametric data set obtained by fitting the Weibull functional forms to the calculated dose points has been divided into two subsets. The STDNN has been trained using some of these parametric events. The other subset of parametric data and the actual doses are used for testing with the resulting weights and biases of the first set. This is done to show that the network can generalize. Results of this testing indicate that the STDNN is capable of predicting doses from events that it has not seen before.

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...

Proceedings of the Organization of 1990 Meeting of International Neural Network Society Jointed with IEEE Held in Washington, DC on January 15 - 19, 1990. Volume 2. Applications Track.

DTIC Science & Technology

1990-11-30

Simonotto Universita’ di Genova Learning from Natural Selection in an Artificial Environment ...................................................... 1...11-92 Ethem Alpaydin Swiss Federal Institute of Technology Framework for Distributed Artificial Neural System Simulation...11-129 David Y. Fong Lockheed Missiles and Space Co. and Christopher Tocci Raytheon Co. Simulation of Artificial Neural

Limitation of Inverse Probability-of-Censoring Weights in Estimating Survival in the Presence of Strong Selection Bias

PubMed Central

Howe, Chanelle J.; Cole, Stephen R.; Chmiel, Joan S.; Muñoz, Alvaro

2011-01-01

In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984–2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed. PMID:21289029

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

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...

Artificial Intelligence: A Selected Bibliography.

ERIC Educational Resources Information Center

Smith, Linda C., Comp.

1984-01-01

This 19-item annotated bibliography introducing the literature of artificial intelligence (AI) is arranged by type of material--handbook, books (general interest, textbooks, collected readings), journals and newsletters, and conferences and workshops. The availability of technical reports from AI laboratories at universities and private companies…

Automatic Exposure Control Device for Digital Mammography

DTIC Science & Technology

2001-08-01

developing innovative approaches for controlling DM exposures. These approaches entail using the digital detector and an artificial neural network to...of interest that determine the exposure parameters for the fully exposed image; and (2) to use an artificial neural network to select exposure

Automatic Exposure Control Device for Digital Mammography

DTIC Science & Technology

2004-08-01

developing innovative approaches for controlling DM exposures. These approaches entail using the digital detector and an artificial neural network to...of interest that determine the exposure parameters for the fully exposed image; and (2) to use an artificial neural network to select exposure

Effect of Artificial Selection on Runs of Homozygosity in U.S. Holstein Cattle

PubMed Central

Kim, Eui-Soo; Cole, John B.; Huson, Heather; Wiggans, George R.; Van Tassell, Curtis P.; Crooker, Brian A.; Liu, George; Da, Yang; Sonstegard, Tad S.

2013-01-01

The intensive selection programs for milk made possible by mass artificial insemination increased the similarity among the genomes of North American (NA) Holsteins tremendously since the 1960s. This migration of elite alleles has caused certain regions of the genome to have runs of homozygosity (ROH) occasionally spanning millions of continuous base pairs at a specific locus. In this study, genome signatures of artificial selection in NA Holsteins born between 1953 and 2008 were identified by comparing changes in ROH between three distinct groups under different selective pressure for milk production. The ROH regions were also used to estimate the inbreeding coefficients. The comparisons of genomic autozygosity between groups selected or unselected since 1964 for milk production revealed significant differences with respect to overall ROH frequency and distribution. These results indicate selection has increased overall autozygosity across the genome, whereas the autozygosity in an unselected line has not changed significantly across most of the chromosomes. In addition, ROH distribution was more variable across the genomes of selected animals in comparison to a more even ROH distribution for unselected animals. Further analysis of genome-wide autozygosity changes and the association between traits and haplotypes identified more than 40 genomic regions under selection on several chromosomes (Chr) including Chr 2, 7, 16 and 20. Many of these selection signatures corresponded to quantitative trait loci for milk, fat, and protein yield previously found in contemporary Holsteins. PMID:24348915

Round and large: morphological and genetic consequences of artificial selection on the gourd tree Crescentia cujete by the Maya of the Yucatan Peninsula, Mexico

PubMed Central

Aguirre-Dugua, Xitlali; Eguiarte, Luis E.; González-Rodríguez, Antonio; Casas, Alejandro

2012-01-01

Background and Aims Artificial selection, the main driving force of domestication, depends on human perception of intraspecific variation and operates through management practices that drive morphological and genetic divergences with respect to wild populations. This study analysed the recognition of varieties of Crescentia cujete by Maya people in relation to preferred plant characters and documents ongoing processes of artificial selection influencing differential chloroplast DNA haplotype distribution in sympatric wild and home-garden populations. Methods Fifty-three home gardens in seven villages (93 trees) and two putative wild populations (43 trees) were sampled. Through semi-structured interviews we documented the nomenclature of varieties, their distinctive characters, provenance, frequency and management. Phenotypic divergence of fruits was assessed with morphometric analyses. Genetic analyses were performed through five cpDNA microsatellites. Key Results The Maya recognize two generic (wild/domesticated) and two specific domesticated (white/green) varieties of Crescentia cujete. In home gardens, most trees (68 %) were from domesticated varieties while some wild individuals (32 %) were tolerated. Cultivation involves mainly vegetative propagation (76 %). Domesticated fruits were significantly rounder, larger and with thicker pericarp than wild fruits. Haplotype A was dominant in home gardens (76 %) but absent in wild populations. Haplotypes B–F were found common in the wild but at low frequency (24 %) in home gardens. Conclusions The gourd tree is managed through clonal and sexual propagules, fruit form and size being the main targets of artificial selection. Domesticated varieties belong to a lineage preserved by vegetative propagation but propagation by seeds and tolerance of spontaneous trees favour gene flow from wild populations. Five mutational steps between haplotypes A and D suggest that domesticated germplasm has been introduced to the region. The close relationship between Maya nomenclature and artificial selection has maintained the morphological and haplotypic identity (probably for centuries) of domesticated Crescentia despite gene flow from wild populations. PMID:22499854

Round and large: morphological and genetic consequences of artificial selection on the gourd tree Crescentia cujete by the Maya of the Yucatan Peninsula, Mexico.

PubMed

Aguirre-Dugua, Xitlali; Eguiarte, Luis E; González-Rodríguez, Antonio; Casas, Alejandro

2012-06-01

Artificial selection, the main driving force of domestication, depends on human perception of intraspecific variation and operates through management practices that drive morphological and genetic divergences with respect to wild populations. This study analysed the recognition of varieties of Crescentia cujete by Maya people in relation to preferred plant characters and documents ongoing processes of artificial selection influencing differential chloroplast DNA haplotype distribution in sympatric wild and home-garden populations. Fifty-three home gardens in seven villages (93 trees) and two putative wild populations (43 trees) were sampled. Through semi-structured interviews we documented the nomenclature of varieties, their distinctive characters, provenance, frequency and management. Phenotypic divergence of fruits was assessed with morphometric analyses. Genetic analyses were performed through five cpDNA microsatellites. The Maya recognize two generic (wild/domesticated) and two specific domesticated (white/green) varieties of Crescentia cujete. In home gardens, most trees (68 %) were from domesticated varieties while some wild individuals (32 %) were tolerated. Cultivation involves mainly vegetative propagation (76 %). Domesticated fruits were significantly rounder, larger and with thicker pericarp than wild fruits. Haplotype A was dominant in home gardens (76 %) but absent in wild populations. Haplotypes B-F were found common in the wild but at low frequency (24 %) in home gardens. The gourd tree is managed through clonal and sexual propagules, fruit form and size being the main targets of artificial selection. Domesticated varieties belong to a lineage preserved by vegetative propagation but propagation by seeds and tolerance of spontaneous trees favour gene flow from wild populations. Five mutational steps between haplotypes A and D suggest that domesticated germplasm has been introduced to the region. The close relationship between Maya nomenclature and artificial selection has maintained the morphological and haplotypic identity (probably for centuries) of domesticated Crescentia despite gene flow from wild populations.

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.

Artificial neural network models: A decision support tool for enhancing seedling selection in sugarcane

USDA-ARS?s Scientific Manuscript database

Currently, sugarcane selection begins at the seedling stage with visual selection for cane yield and other yield-related traits. Although subjective and inefficient, visual selection remains the primary method for selection. Visual selection is inefficient because of the confounding effect of genoty...

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.

Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.

PubMed

Carpenter, Kristy A; Huang, Xudong

2018-06-07

Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more heavily toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics of for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Self Improving Methods for Materials and Process Design

DTIC Science & Technology

1998-08-31

using inductive coupling techniques. The first phase of the work focuses on developing an artificial neural network learning for function approximation...developing an artificial neural network learning algorithm for time-series prediction. The third phase of the work focuses on model selection. We have

Membranes with artificial free-volume for biofuel production

PubMed Central

Petzetakis, Nikos; Doherty, Cara M.; Thornton, Aaron W.; Chen, X. Chelsea; Cotanda, Pepa; Hill, Anita J.; Balsara, Nitash P.

2015-01-01

Free-volume of polymers governs transport of penetrants through polymeric films. Control over free-volume is thus important for the development of better membranes for a wide variety of applications such as gas separations, pharmaceutical purifications and energy storage. To date, methodologies used to create materials with different amounts of free-volume are based primarily on chemical synthesis of new polymers. Here we report a simple methodology for generating free-volume based on the self-assembly of polyethylene-b-polydimethylsiloxane-b-polyethylene triblock copolymers. We have used this method to fabricate a series of membranes with identical compositions but with different amounts of free-volume. We use the term artificial free-volume to refer to the additional free-volume created by self-assembly. The effect of artificial free-volume on selective transport through the membranes was tested using butanol/water and ethanol/water mixtures due to their importance in biofuel production. We found that the introduction of artificial free-volume improves both alcohol permeability and selectivity. PMID:26104672

Membranes with artificial free-volume for biofuel production

NASA Astrophysics Data System (ADS)

Petzetakis, Nikos; Doherty, Cara M.; Thornton, Aaron W.; Chen, X. Chelsea; Cotanda, Pepa; Hill, Anita J.; Balsara, Nitash P.

2015-06-01

Free-volume of polymers governs transport of penetrants through polymeric films. Control over free-volume is thus important for the development of better membranes for a wide variety of applications such as gas separations, pharmaceutical purifications and energy storage. To date, methodologies used to create materials with different amounts of free-volume are based primarily on chemical synthesis of new polymers. Here we report a simple methodology for generating free-volume based on the self-assembly of polyethylene-b-polydimethylsiloxane-b-polyethylene triblock copolymers. We have used this method to fabricate a series of membranes with identical compositions but with different amounts of free-volume. We use the term artificial free-volume to refer to the additional free-volume created by self-assembly. The effect of artificial free-volume on selective transport through the membranes was tested using butanol/water and ethanol/water mixtures due to their importance in biofuel production. We found that the introduction of artificial free-volume improves both alcohol permeability and selectivity.

Back propagation artificial neural network for community Alzheimer's disease screening in China.

PubMed

Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

2013-01-25

Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.

Back propagation artificial neural network for community Alzheimer's disease screening in China★

PubMed Central

Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

2013-01-01

Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598

Membranes with artificial free-volume for biofuel production

DOE PAGES

Petzetakis, Nikos; Doherty, Cara M.; Thornton, Aaron W.; ...

2015-06-24

Free-volume of polymers governs transport of penetrants through polymeric films. Control over free-volume is thus important for the development of better membranes for a wide variety of applications such as gas separations, pharmaceutical purifications and energy storage. To date, methodologies used to create materials with different amounts of free-volume are based primarily on chemical synthesis of new polymers. Here we report a simple methodology for generating free-volume based on the self-assembly of polyethylene-b-polydimethylsiloxane-b-polyethylene triblock copolymers. Here, we have used this method to fabricate a series of membranes with identical compositions but with different amounts of free-volume. We use the termmore » artificial free-volume to refer to the additional free-volume created by self-assembly. The effect of artificial free-volume on selective transport through the membranes was tested using butanol/water and ethanol/water mixtures due to their importance in biofuel production. Moreover, we found that the introduction of artificial free-volume improves both alcohol permeability and selectivity.« less

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.

Caseoperoxidase, mixed β-casein-SDS-hemin-imidazole complex: a nano artificial enzyme.

PubMed

Moosavi-Movahedi, Zainab; Gharibi, Hussein; Hadi-Alijanvand, Hamid; Akbarzadeh, Mohammad; Esmaili, Mansoore; Atri, Maliheh S; Sefidbakht, Yahya; Bohlooli, Mousa; Nazari, Khodadad; Javadian, Soheila; Hong, Jun; Saboury, Ali A; Sheibani, Nader; Moosavi-Movahedi, Ali A

2015-01-01

A novel peroxidase-like artificial enzyme, named "caseoperoxidase", was biomimetically designed using a nano artificial amino acid apo-protein hydrophobic pocket. This four-component nano artificial enzyme containing heme-imidazole-β-casein-SDS exhibited high activity growth and k(cat) performance toward the native horseradish peroxidase demonstrated by the steady state kinetics using UV-vis spectrophotometry. The hydrophobicity and secondary structure of the caseoperoxidase were studied by ANS fluorescence and circular dichroism spectroscopy. Camel β-casein (Cβ-casein) was selected as an appropriate apo-protein for the heme active site because of its innate flexibility and exalted hydrophobicity. This selection was confirmed by homology modeling method. Heme docking into the newly obtained Cβ-casein structure indicated one heme was mainly incorporated with Cβ-casein. The presence of a main electrostatic site for the active site in the Cβ-casein was also confirmed by experimental methods through Wyman binding potential and isothermal titration calorimetry. The existence of Cβ-casein protein in this biocatalyst lowered the suicide inactivation and provided a suitable protective role for the heme active-site. Additional experiments confirmed the retention of caseoperoxidase structure and function as an artificial enzyme.

Multigenerational response to artificial selection for biased clutch sex ratios in Tigriopus californicus populations.

PubMed

Alexander, H J; Richardson, J M L; Anholt, B R

2014-09-01

Polygenic sex determination (PSD) is relatively rare and theoretically evolutionary unstable, yet has been reported across a range of taxa. Evidence for multilocus PSD is provided by (i) large between-family variance in sex ratio, (ii) paternal and maternal effects on family sex ratio and (iii) response to selection for family sex ratio. This study tests the polygenic hypothesis of sex determination in the harpacticoid copepod Tigriopus californicus using the criterion of response to selection. We report the first multigenerational quantitative evidence that clutch sex ratio responds to artificial selection in both directions (selection for male- and female-biased families) and in multiple populations of T. californicus. In the five of six lines that showed a response to selection, realized heritability estimated by multigenerational analysis ranged from 0.24 to 0.58. Divergence of clutch sex ratio between selection lines is rapid, with response to selection detectable within the first four generations of selection. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

PubMed

Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

2017-08-01

Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Niche construction, sources of selection and trait coevolution.

PubMed

Laland, Kevin; Odling-Smee, John; Endler, John

2017-10-06

Organisms modify and choose components of their local environments. This 'niche construction' can alter ecological processes, modify natural selection and contribute to inheritance through ecological legacies. Here, we propose that niche construction initiates and modifies the selection directly affecting the constructor, and on other species, in an orderly, directed and sustained manner. By dependably generating specific environmental states, niche construction co-directs adaptive evolution by imposing a consistent statistical bias on selection. We illustrate how niche construction can generate this evolutionary bias by comparing it with artificial selection. We suggest that it occupies the middle ground between artificial and natural selection. We show how the perspective leads to testable predictions related to: (i) reduced variance in measures of responses to natural selection in the wild; (ii) multiple trait coevolution, including the evolution of sequences of traits and patterns of parallel evolution; and (iii) a positive association between niche construction and biodiversity. More generally, we submit that evolutionary biology would benefit from greater attention to the diverse properties of all sources of selection.

Linked genetic variants on chromosome 10 control ear morphology and body mass among dog breeds.

PubMed

Webster, Matthew T; Kamgari, Nona; Perloski, Michele; Hoeppner, Marc P; Axelsson, Erik; Hedhammar, Åke; Pielberg, Gerli; Lindblad-Toh, Kerstin

2015-06-23

The domestic dog is a rich resource for mapping the genetic components of phenotypic variation due to its unique population history involving strong artificial selection. Genome-wide association studies have revealed a number of chromosomal regions where genetic variation associates with morphological characters that typify dog breeds. A region on chromosome 10 is among those with the highest levels of genetic differentiation between dog breeds and is associated with body mass and ear morphology, a common motif of animal domestication. We characterised variation in this region to uncover haplotype structure and identify candidate functional variants. We first identified SNPs that strongly associate with body mass and ear type by comparing sequence variation in a 3 Mb region between 19 breeds with a variety of phenotypes. We next genotyped a subset of 123 candidate SNPs in 288 samples from 46 breeds to identify the variants most highly associated with phenotype and infer haplotype structure. A cluster of SNPs that associate strongly with the drop ear phenotype is located within a narrow interval downstream of the gene MSRB3, which is involved in human hearing. These SNPs are in strong genetic linkage with another set of variants that correlate with body mass within the gene HMGA2, which affects human height. In addition we find evidence that this region has been under selection during dog domestication, and identify a cluster of SNPs within MSRB3 that are highly differentiated between dogs and wolves. We characterise genetically linked variants that potentially influence ear type and body mass in dog breeds, both key traits that have been modified by selective breeding that may also be important for domestication. The finding that variants on long haplotypes have effects on more than one trait suggests that genetic linkage can be an important determinant of the phenotypic response to selection in domestic animals.

Importance of sperm morphology during sperm transport and fertilization in mammals.

PubMed

García-Vázquez, Francisco A; Gadea, Joaquín; Matás, Carmen; Holt, William V

2016-01-01

After natural or artificial insemination, the spermatozoon starts a journey from the site of deposition to the place of fertilization. However, only a small subset of the spermatozoa deposited achieves their goal: to reach and fertilize the egg. Factors involved in controlling sperm transport and fertilization include the female reproductive tract environment, cell-cell interactions, gene expression, and phenotypic sperm traits. Some of the significant determinants of fertilization are known (i.e., motility or DNA status), but many sperm traits are still indecipherable. One example is the influence of sperm dimensions and shape upon transport within the female genital tract towards the oocyte. Biophysical associations between sperm size and motility may influence the progression of spermatozoa through the female reproductive tract, but uncertainties remain concerning how sperm morphology influences the fertilization process, and whether only the sperm dimensions per se are involved. Moreover, such explanations do not allow the possibility that the female tract is capable of distinguishing fertile spermatozoa on the basis of their morphology, as seems to be the case with biochemical, molecular, and genetic properties. This review focuses on the influence of sperm size and shape in evolution and their putative role in sperm transport and selection within the uterus and the ability to fertilize the oocyte.

Importance of sperm morphology during sperm transport and fertilization in mammals

PubMed Central

García-Vázquez, Francisco A; Gadea, Joaquín; Matás, Carmen; Holt, William V

2016-01-01

After natural or artificial insemination, the spermatozoon starts a journey from the site of deposition to the place of fertilization. However, only a small subset of the spermatozoa deposited achieves their goal: to reach and fertilize the egg. Factors involved in controlling sperm transport and fertilization include the female reproductive tract environment, cell-cell interactions, gene expression, and phenotypic sperm traits. Some of the significant determinants of fertilization are known (i.e., motility or DNA status), but many sperm traits are still indecipherable. One example is the influence of sperm dimensions and shape upon transport within the female genital tract towards the oocyte. Biophysical associations between sperm size and motility may influence the progression of spermatozoa through the female reproductive tract, but uncertainties remain concerning how sperm morphology influences the fertilization process, and whether only the sperm dimensions per se are involved. Moreover, such explanations do not allow the possibility that the female tract is capable of distinguishing fertile spermatozoa on the basis of their morphology, as seems to be the case with biochemical, molecular, and genetic properties. This review focuses on the influence of sperm size and shape in evolution and their putative role in sperm transport and selection within the uterus and the ability to fertilize the oocyte. PMID:27624988

Pregnancy rates after artificial insemination with cooled stallion spermatozoa either with or without single layer centrifugation.

PubMed

Morrell, J M; Richter, J; Martinsson, G; Stuhtmann, G; Hoogewijs, M; Roels, K; Dalin, A-M

2014-11-01

A successful outcome after artificial insemination with cooled semen is dependent on many factors, the sperm quality of the ejaculate being one. Previous studies have shown that spermatozoa with good motility, normal morphology, and good chromatin integrity can be selected by means of colloid centrifugation, particularly single layer centrifugation (SLC) using species-specific colloids. The purpose of the present study was to conduct an insemination trial with spermatozoa from "normal" ejaculates, i.e., from stallions with no known fertility problem, to determine whether the improvements in sperm quality seen in SLC-selected sperm samples compared with uncentrifuged controls in laboratory tests are reflected in an increased pregnancy rate after artificial insemination. In a multicentre study, SLC-selected sperm samples and uncentrifuged controls from eight stallions were inseminated into approximately 10 mares per treatment per stallion. Ultrasound examination was carried out approximately 16 days after insemination to detect an embryonic vesicle. The pregnancy rates per cycle were 45% for controls and 69% for SLC-selected sperm samples, which is statistically significant (P < 0.0018). Thus, the improvement in sperm quality reported previously for SLC-selected sperm samples is associated with an increase in pregnancy rate, even for ejaculates from stallions with no known fertility problem. Copyright © 2014 Elsevier Inc. All rights reserved.

Artificial selection on male genitalia length alters female brain size.

PubMed

Buechel, Séverine D; Booksmythe, Isobel; Kotrschal, Alexander; Jennions, Michael D; Kolm, Niclas

2016-11-30

Male harassment is a classic example of how sexual conflict over mating leads to sex-specific behavioural adaptations. Females often suffer significant costs from males attempting forced copulations, and the sexes can be in an arms race over male coercion. Yet, despite recent recognition that divergent sex-specific interests in reproduction can affect brain evolution, sexual conflict has not been addressed in this context. Here, we investigate whether artificial selection on a correlate of male success at coercion, genital length, affects brain anatomy in males and females. We analysed the brains of eastern mosquitofish (Gambusia holbrooki), which had been artificially selected for long or short gonopodium, thereby mimicking selection arising from differing levels of male harassment. By analogy to how prey species often have relatively larger brains than their predators, we found that female, but not male, brain size was greater following selection for a longer gonopodium. Brain subregion volumes remained unchanged. These results suggest that there is a positive genetic correlation between male gonopodium length and female brain size, which is possibly linked to increased female cognitive ability to avoid male coercion. We propose that sexual conflict is an important factor in the evolution of brain anatomy and cognitive ability. © 2016 The Author(s).

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

Correlative feature analysis on FFDM

PubMed Central

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-01-01

Identifying the corresponding images of a lesion in different views is an essential step in improving the diagnostic ability of both radiologists and computer-aided diagnosis (CAD) systems. Because of the nonrigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this pilot study, we present a computerized framework that differentiates between corresponding images of the same lesion in different views and noncorresponding images, i.e., images of different lesions. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, is applied to extract mass lesions from the surrounding parenchyma. Then various lesion features are automatically extracted from each of the two views of each lesion to quantify the characteristics of density, size, texture and the neighborhood of the lesion, as well as its distance to the nipple. A two-step scheme is employed to estimate the probability that the two lesion images from different mammographic views are of the same physical lesion. In the first step, a correspondence metric for each pairwise feature is estimated by a Bayesian artificial neural network (BANN). Then, these pairwise correspondence metrics are combined using another BANN to yield an overall probability of correspondence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing corresponding pairs from noncorresponding pairs. Using a FFDM database with 123 corresponding image pairs and 82 noncorresponding pairs, the distance feature yielded an area under the ROC curve (AUC) of 0.81±0.02 with leave-one-out (by physical lesion) evaluation, and the feature metric subset, which included distance, gradient texture, and ROI-based correlation, yielded an AUC of 0.87±0.02. The improvement by using multiple feature metrics was statistically significant compared to single feature performance. PMID:19175108

Designing Artificial Enzymes by Intuition and Computation

PubMed Central

Nanda, Vikas; Koder, Ronald L.

2012-01-01

The rational design of artificial enzymes either by applying physio-chemical intuition of protein structure and function or with the aid of computation methods is a promising area of research with the potential to tremendously impact medicine, industrial chemistry and energy production. Designed proteins also provide a powerful platform for dissecting enzyme mechanisms of natural systems. Artificial enzymes have come a long way, from simple α-helical peptide catalysts to proteins that facilitate multi-step chemical reactions designed by state-of-the-art computational methods. Looking forward, we examine strategies employed by natural enzymes which could be used to improve the speed and selectivity of artificial catalysts. PMID:21124375

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.

Tuning of CO2 Reduction Selectivity on Metal Electrocatalysts.

PubMed

Wang, Yuhang; Liu, Junlang; Wang, Yifei; Al-Enizi, Abdullah M; Zheng, Gengfeng

2017-11-01

Climate change, caused by heavy CO 2 emissions, is driving new demands to alleviate the rising concentration of atmospheric CO 2 levels. Enlightened by the photosynthesis of green plants, photo(electro)chemical catalysis of CO 2 reduction, also known as artificial photosynthesis, is emerged as a promising candidate to address these demands and is widely investigated during the past decade. Among various artificial photosynthetic systems, solar-driven electrochemical CO 2 reduction is widely recognized to possess high efficiencies and potentials for practical application. The efficient and selective electroreduction of CO 2 is the key to the overall solar-to-chemical efficiency of artificial photosynthesis. Recent studies show that various metallic materials possess the capability to play as electrocatalysts for CO 2 reduction. In order to achieve high selectivity for CO 2 reduction products, various efforts are made including studies on electrolytes, crystal facets, oxide-derived catalysts, electronic and geometric structures, nanostructures, and mesoscale phenomena. In this Review, these methods for tuning the selectivity of CO 2 electrochemical reduction of metallic catalysts are summarized. The challenges and perspectives in this field are also discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Artificial Life in Quantum Technologies

NASA Astrophysics Data System (ADS)

Alvarez-Rodriguez, Unai; Sanz, Mikel; Lamata, Lucas; Solano, Enrique

2016-02-01

We develop a quantum information protocol that models the biological behaviours of individuals living in a natural selection scenario. The artificially engineered evolution of the quantum living units shows the fundamental features of life in a common environment, such as self-replication, mutation, interaction of individuals, and death. We propose how to mimic these bio-inspired features in a quantum-mechanical formalism, which allows for an experimental implementation achievable with current quantum platforms. This study paves the way for the realization of artificial life and embodied evolution with quantum technologies.

Space applications of artificial intelligence; 1990 Goddard Conference, Greenbelt, MD, May 1, 2, 1990, Selected Papers

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Break-even cost of cloning in genetic improvement of dairy cattle.

PubMed

Dematawewa, C M; Berger, P J

1998-04-01

Twelve different models for alternative progeny-testing schemes based on genetic and economic gains were compared. The first 10 alternatives were considered to be optimally operating progeny-testing schemes. Alternatives 1 to 5 considered the following combinations of technologies: 1) artificial insemination, 2) artificial insemination with sexed semen, 3) artificial insemination with embryo transfer, 4) artificial insemination and embryo transfer with few bulls as sires, and 5) artificial insemination, embryo transfer, and sexed semen with few bulls, respectively. Alternatives 6 to 12 considered cloning from dams. Alternatives 11 and 12 considered a regular progeny-testing scheme that had selection gains (intensity x accuracy x genetic standard deviation) of 890, 300, 600, and 89 kg, respectively, for the four paths. The sums of the generation intervals of the four paths were 19 yr for the first 8 alternatives and 19.5, 22, 29, and 29.5 yr for alternatives 9 to 12, respectively. Rates of genetic gain in milk yield for alternatives 1 to 5 were 257, 281, 316, 327, and 340 kg/yr, respectively. The rate of gain for other alternatives increased as number of clones increased. The use of three records per clone increased both accuracy and generation interval of a path. Cloning was highly beneficial for progeny-testing schemes with lower intensity and accuracy of selection. The discounted economic gain (break-even cost) per clone was the highest ($84) at current selection levels using sexed semen and three records on clones of the dam. The total cost associated with cloning has to be below $84 for cloning to be an economically viable option.

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.

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

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.

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.

Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes

DTIC Science & Technology

2011-06-01

implementing, and evaluating many feature selection algorithms. Mucciardi and Gose compared seven different techniques for choosing subsets of pattern...122 THIS PAGE INTENTIONALLY LEFT BLANK 123 LIST OF REFERENCES [1] A. Mucciardi and E. Gose , “A comparison of seven techniques for

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.

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.

The great chemical residue detection debate: dog versus machine

NASA Astrophysics Data System (ADS)

Tripp, Alan C.; Walker, James C.

2003-09-01

Many engineering groups desire to construct instrumentation to replace dog-handler teams in identifying and localizing chemical mixtures. This goal requires performance specifications for an "artificial dog-handler team". Progress toward generating such specifications from laboratory tests of dog-handler teams has been made recently at the Sensory Research Institute, and the method employed is amenable to the measurement of tasks representative of the decision-making that must go on when such teams solve problems in actual (and therefore informationally messy) situations. As progressively more quantitative data are obtained on progressively more complex odor tasks, the boundary conditions of dog-handler performance will be understood in great detail. From experiments leading to this knowledge, one ca develop, as we do in this paper, a taxonomy of test conditions that contain various subsets of the variables encountered in "real world settings". These tests provide the basis for the rigorous testing that will provide an improved basis for deciding when biological sensing approaches (e.g. dog-handler teams) are best and when "artificial noses" are most valuable.

Vesicle encapsulation of a nonbiological photochemical system capable of reducing NAD(+) to NADH.

PubMed

Summers, David P; Rodoni, David

2015-10-06

One of the fundamental structures of a cell is the membrane. Self-assembling lipid bilayer vesicles can form the membrane of an artificial cell and could also have plausibly assembled prebiotically for the origin of life. Such cell-like structures, that encapsulate some basic subset of the functions of living cells, are important for research to infer the minimum chemistry necessary for a cell, to help understand the origin of life, and to allow the production of useful species in microscopic containers. We show that the encapsulation of TiO2 particles has the potential to provide the basis for an energy transduction system inside vesicles which can be used to drive subsequent chemistry. TiO2 encapsulated in vesicles can be used to produce biochemical species such as NADH. The NADH is formed from NAD(+) reduction and is produced in a form that is able to drive further enzymatic chemistry. This allows us to link a mineral-based, nonbiological photosystem to biochemical reactions. This is a fundamental step toward being able to use this mineral photosystem in a protocell/artificial cell.

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.

PubMed

Janet, Jon Paul; Chan, Lydia; Kulik, Heather J

2018-03-01

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhaïl

2013-04-01

The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal drift (ANNEX). Moreover, the exact number of output neurons and the selection of the corresponding variables were based on the subsets created during the exploratory phase. Concerning hidden layers, no restriction were made and multiple architectures were tested. For each MLP model, the quality of the modeling procedure was assessed by variograms: if the variogram of the residuals demonstrates pure nugget effect and if the level of the nugget exactly corresponds to the nugget value of the theoretical variogram of the corresponding variable, all the structured information has been correctly extracted without overfitting. Finally, it is worth mentioning that simple MLP models are not always able to remove all the spatial correlation structure from the data. In that case, Neural Network Residual Kriging (NNRK) can be carried out and risk assessment can be conducted with Neural Network Residual Simulations (NNRS). Finally, the results of the ANNEX models were compared to those of ordinary (co)kriging and (co)kriging with an external drift. It was shown that the ANNEX models performed better than traditional geostatistical algorithms when the relationship between the variable of interest and the auxiliary predictor was not linear. References Kanevski, M. and Maignan, M. (2004). Analysis and Modelling of Spatial Environmental Data. Lausanne: EPFL Press.

Property-based design: optimization and characterization of polyvinyl alcohol (PVA) hydrogel and PVA-matrix composite for artificial cornea.

PubMed

Jiang, Hong; Zuo, Yi; Zhang, Li; Li, Jidong; Zhang, Aiming; Li, Yubao; Yang, Xiaochao

2014-03-01

Each approach for artificial cornea design is toward the same goal: to develop a material that best mimics the important properties of natural cornea. Accordingly, the selection and optimization of corneal substitute should be based on their physicochemical properties. In this study, three types of polyvinyl alcohol (PVA) hydrogels with different polymerization degree (PVA1799, PVA2499 and PVA2699) were prepared by freeze-thawing techniques. After characterization in terms of transparency, water content, water contact angle, mechanical property, root-mean-square roughness and protein adsorption behavior, the optimized PVA2499 hydrogel with similar properties of natural cornea was selected as a matrix material for artificial cornea. Based on this, a biomimetic artificial cornea was fabricated with core-and-skirt structure: a transparent PVA hydrogel core, surrounding by a ringed PVA-matrix composite skirt that composed of graphite, Fe-doped nano hydroxyapatite (n-Fe-HA) and PVA hydrogel. Different ratio of graphite/n-Fe-HA can tune the skirt color from dark brown to light brown, which well simulates the iris color of Oriental eyes. Moreover, morphologic and mechanical examination showed that an integrated core-and-skirt artificial cornea was formed from an interpenetrating polymer network, no phase separation appeared on the interface between the core and the skirt.

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...

A Retrospective and Prospective View of Information Retrieval and Artificial Intelligence in the 21st Century.

ERIC Educational Resources Information Center

Garfield, Eugene

2001-01-01

Traces the development of information retrieval/services and suggests that the creation of large digital libraries seems inevitable. Examines possibilities for increasing electronic access and the role of artificial intelligence. Highlights include: searching full text; sending full texts; selective dissemination of information (SDI) profiling and…

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

On the tesseral-harmonics resonance problem in artificial-satellite theory

NASA Technical Reports Server (NTRS)

Romanowicz, B. A.

1975-01-01

The longitude-dependent part of the geopotential usually gives rise only to short-period effects in the motion of an artificial satellite. However, when the motion of the satellite is commensurable with that of the earth, the path of the satellite repeats itself relative to the earth and perturbations build up at each passage of the satellite in the same spot, so that there can be important long-period effects. In order to take these effects into account in deriving a theoretical solution to the equations of motion of an artificial satellite, it is necessary to select terms in the longitude-dependent part of the geopotential that will contribute significantly to the perturbations. Attempts made to obtain a selection that is valid in a general case, regardless of the initial eccentricity of the orbit and of the order of the resonance, are reported. The solution to the equations of motion of an artificial satellite, in a geopotential thus determined, is then derived by using Hori's method by Lie series, which, by its properties regarding canonical invariance, has proved advantageous in the classical theory.

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.

Learning performance and brain structure of artificially-reared honey bees fed with different quantities of food

PubMed Central

Spaethe, Johannes; Steffan-Dewenter, Ingolf; Härtel, Stephan

2017-01-01

Background Artificial rearing of honey bee larvae is an established method which enables to fully standardize the rearing environment and to manipulate the supplied diet to the brood. However, there are no studies which compare learning performance or neuroanatomic differences of artificially-reared (in-lab) bees in comparison with their in-hive reared counterparts. Methods Here we tested how different quantities of food during larval development affect body size, brain morphology and learning ability of adult honey bees. We used in-lab rearing to be able to manipulate the total quantity of food consumed during larval development. After hatching, a subset of the bees was taken for which we made 3D reconstructions of the brains using confocal laser-scanning microscopy. Learning ability and memory formation of the remaining bees was tested in a differential olfactory conditioning experiment. Finally, we evaluated how bees reared with different quantities of artificial diet compared to in-hive reared bees. Results Thorax and head size of in-lab reared honey bees, when fed the standard diet of 160 µl or less, were slightly smaller than hive bees. The brain structure analyses showed that artificially reared bees had smaller mushroom body (MB) lateral calyces than their in-hive counterparts, independently of the quantity of food they received. However, they showed the same total brain size and the same associative learning ability as in-hive reared bees. In terms of mid-term memory, but not early long-term memory, they performed even better than the in-hive control. Discussion We have demonstrated that bees that are reared artificially (according to the Aupinel protocol) and kept in lab-conditions perform the same or even better than their in-hive sisters in an olfactory conditioning experiment even though their lateral calyces were consistently smaller at emergence. The applied combination of experimental manipulation during the larval phase plus subsequent behavioral and neuro-anatomic analyses is a powerful tool for basic and applied honey bee research. PMID:29085743

A genome-wide scan for signatures of differential artificial selection in ten cattle breeds.

PubMed

Rothammer, Sophie; Seichter, Doris; Förster, Martin; Medugorac, Ivica

2013-12-21

Since the times of domestication, cattle have been continually shaped by the influence of humans. Relatively recent history, including breed formation and the still enduring enormous improvement of economically important traits, is expected to have left distinctive footprints of selection within the genome. The purpose of this study was to map genome-wide selection signatures in ten cattle breeds and thus improve the understanding of the genome response to strong artificial selection and support the identification of the underlying genetic variants of favoured phenotypes. We analysed 47,651 single nucleotide polymorphisms (SNP) using Cross Population Extended Haplotype Homozygosity (XP-EHH). We set the significance thresholds using the maximum XP-EHH values of two essentially artificially unselected breeds and found up to 229 selection signatures per breed. Through a confirmation process we verified selection for three distinct phenotypes typical for one breed (polledness in Galloway, double muscling in Blanc-Bleu Belge and red coat colour in Red Holstein cattle). Moreover, we detected six genes strongly associated with known QTL for beef or dairy traits (TG, ABCG2, DGAT1, GH1, GHR and the Casein Cluster) within selection signatures of at least one breed. A literature search for genes lying in outstanding signatures revealed further promising candidate genes. However, in concordance with previous genome-wide studies, we also detected a substantial number of signatures without any yet known gene content. These results show the power of XP-EHH analyses in cattle to discover promising candidate genes and raise the hope of identifying phenotypically important variants in the near future. The finding of plausible functional candidates in some short signatures supports this hope. For instance, MAP2K6 is the only annotated gene of two signatures detected in Galloway and Gelbvieh cattle and is already known to be associated with carcass weight, back fat thickness and marbling score in Korean beef cattle. Based on the confirmation process and literature search we deduce that XP-EHH is able to uncover numerous artificial selection targets in subpopulations of domesticated animals.

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…

Achieving high permeability and enhanced selectivity for Angstrom-scale separations using artificial water channel membranes.

PubMed

Shen, Yue-Xiao; Song, Woochul C; Barden, D Ryan; Ren, Tingwei; Lang, Chao; Feroz, Hasin; Henderson, Codey B; Saboe, Patrick O; Tsai, Daniel; Yan, Hengjing; Butler, Peter J; Bazan, Guillermo C; Phillip, William A; Hickey, Robert J; Cremer, Paul S; Vashisth, Harish; Kumar, Manish

2018-06-12

Synthetic polymer membranes, critical to diverse energy-efficient separations, are subject to permeability-selectivity trade-offs that decrease their overall efficacy. These trade-offs are due to structural variations (e.g., broad pore size distributions) in both nonporous membranes used for Angstrom-scale separations and porous membranes used for nano to micron-scale separations. Biological membranes utilize well-defined Angstrom-scale pores to provide exceptional transport properties and can be used as inspiration to overcome this trade-off. Here, we present a comprehensive demonstration of such a bioinspired approach based on pillar[5]arene artificial water channels, resulting in artificial water channel-based block copolymer membranes. These membranes have a sharp selectivity profile with a molecular weight cutoff of ~ 500 Da, a size range challenging to achieve with current membranes, while achieving a large improvement in permeability (~65 L m -2  h -1  bar -1  compared with 4-7 L m -2  h -1  bar -1 ) over similarly rated commercial membranes.

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

Hybridization, natural selection, and evolution of reproductive isolation: a 25-years survey of an artificial sympatric area between two mosquito sibling species of the Aedes mariae complex.

PubMed

Urbanelli, Sandra; Porretta, Daniele; Mastrantonio, Valentina; Bellini, Romeo; Pieraccini, Giuseppe; Romoli, Riccardo; Crasta, Graziano; Nascetti, Giuseppe

2014-10-01

Natural selection can act against maladaptive hybridization between co-occurring divergent populations leading to evolution of reproductive isolation among them. A critical unanswered question about this process that provides a basis for the theory of speciation by reinforcement, is whether natural selection can cause hybridization rates to evolve to zero. Here, we investigated this issue in two sibling mosquitoes species, Aedes mariae and Aedes zammitii, that show postmating reproductive isolation (F1 males sterile) and partial premating isolation (different height of mating swarms) that could be reinforced by natural selection against hybridization. In 1986, we created an artificial sympatric area between the two species and sampled about 20,000 individuals over the following 25 years. Between 1986 and 2011, the composition of mating swarms and the hybridization rate between the two species were investigated across time in the sympatric area. Our results showed that A. mariae and A. zammitii have not completed reproductive isolation since their first contact in the artificial sympatric area. We have discussed the relative role of factors such as time of contact, gene flow, strength of natural selection, and biological mechanisms causing prezygotic isolation to explain the observed results. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

The relationship between runs of homozygosity and inbreeding in Jersey cattle under selection

USDA-ARS?s Scientific Manuscript database

Inbreeding is often an inevitable outcome of strong directional artificial selection but it reduces fitness in a population with increased frequency of recessive deleterious alleles. Runs of homozygosity (ROH) representing genomic autozygosity that occur from mating between selected and genomically ...

Use of Gene Expression Programming in regionalization of flow duration curve

NASA Astrophysics Data System (ADS)

Hashmi, Muhammad Z.; Shamseldin, Asaad Y.

2014-06-01

In this paper, a recently introduced artificial intelligence technique known as Gene Expression Programming (GEP) has been employed to perform symbolic regression for developing a parametric scheme of flow duration curve (FDC) regionalization, to relate selected FDC characteristics to catchment characteristics. Stream flow records of selected catchments located in the Auckland Region of New Zealand were used. FDCs of the selected catchments were normalised by dividing the ordinates by their median value. Input for the symbolic regression analysis using GEP was (a) selected characteristics of normalised FDCs; and (b) 26 catchment characteristics related to climate, morphology, soil properties and land cover properties obtained using the observed data and GIS analysis. Our study showed that application of this artificial intelligence technique expedites the selection of a set of the most relevant independent variables out of a large set, because these are automatically selected through the GEP process. Values of the FDC characteristics obtained from the developed relationships have high correlations with the observed values.

Stallion spermatozoa selected by single layer centrifugation are capable of fertilization after storage for up to 96 h at 6°C prior to artificial insemination

PubMed Central

2012-01-01

Background One of the challenges faced by equine breeders is ensuring delivery of good quality semen doses for artificial insemination when the mare is due to ovulate. Single Layer Centrifugation (SLC) has been shown to select morphologically normal spermatozoa with intact chromatin and good progressive motility from the rest of the ejaculate, and to prolong the life of these selected spermatozoa in vitro. The objective of the present study was a proof of concept, to determine whether fertilizing ability was retained in SLC-selected spermatozoa during prolonged storage. Findings Sixteen mares were inseminated with SLC-selected sperm doses that had been cooled and stored at 6°C for 48 h, 72 h or 96 h. Embryos were identified in 11 mares by ultrasound examination 16–18 days after presumed ovulation. Conclusion SLC-selected stallion spermatozoa stored for up to 96 h are capable of fertilization. PMID:22788670

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.

Exploring expressivity and emotion with artificial voice and speech technologies.

PubMed

Pauletto, Sandra; Balentine, Bruce; Pidcock, Chris; Jones, Kevin; Bottaci, Leonardo; Aretoulaki, Maria; Wells, Jez; Mundy, Darren P; Balentine, James

2013-10-01

Emotion in audio-voice signals, as synthesized by text-to-speech (TTS) technologies, was investigated to formulate a theory of expression for user interface design. Emotional parameters were specified with markup tags, and the resulting audio was further modulated with post-processing techniques. Software was then developed to link a selected TTS synthesizer with an automatic speech recognition (ASR) engine, producing a chatbot that could speak and listen. Using these two artificial voice subsystems, investigators explored both artistic and psychological implications of artificial speech emotion. Goals of the investigation were interdisciplinary, with interest in musical composition, augmentative and alternative communication (AAC), commercial voice announcement applications, human-computer interaction (HCI), and artificial intelligence (AI). The work-in-progress points towards an emerging interdisciplinary ontology for artificial voices. As one study output, HCI tools are proposed for future collaboration.

Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

PubMed

Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

2012-03-01

To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Artificial Life in Quantum Technologies

PubMed Central

Alvarez-Rodriguez, Unai; Sanz, Mikel; Lamata, Lucas; Solano, Enrique

2016-01-01

We develop a quantum information protocol that models the biological behaviours of individuals living in a natural selection scenario. The artificially engineered evolution of the quantum living units shows the fundamental features of life in a common environment, such as self-replication, mutation, interaction of individuals, and death. We propose how to mimic these bio-inspired features in a quantum-mechanical formalism, which allows for an experimental implementation achievable with current quantum platforms. This study paves the way for the realization of artificial life and embodied evolution with quantum technologies. PMID:26853918

Artificial light at night confounds broad-scale habitat use by migrating birds

USGS Publications Warehouse

McLaren, James D.; Buler, Jeffrey J.; Schreckengost, Tim; Smolinsky, Jaclyn A.; Boone, Matthew; van Loon, E. Emiel; Dawson, Deanna K.; Walters, Eric L.

2018-01-01

With many of the world's migratory bird populations in alarming decline, broad-scale assessments of responses to migratory hazards may prove crucial to successful conservation efforts. Most birds migrate at night through increasingly light-polluted skies. Bright light sources can attract airborne migrants and lead to collisions with structures, but might also influence selection of migratory stopover habitat and thereby acquisition of food resources. We demonstrate, using multi-year weather radar measurements of nocturnal migrants across the northeastern U.S., that autumnal migrant stopover density increased at regional scales with proximity to the brightest areas, but decreased within a few kilometers of brightly-lit sources. This finding implies broad-scale attraction to artificial light while airborne, impeding selection for extensive forest habitat. Given that high-quality stopover habitat is critical to successful migration, and hindrances during migration can decrease fitness, artificial lights present a potentially heightened conservation concern for migratory bird populations.

The return of the Inseminator: Eutelegenesis in past and contemporary reproductive ethics.

PubMed

McMillan, John

2007-06-01

Eugenicists in the 1930s and 1940s emphasised our moral responsibilities to future generations and the importance of positively selecting traits that would benefit humanity. In 1935 Herbert Brewer recommended 'Eutelegenesis' (artificial insemination with sperm from specially selected males) so that that future generations are not only protected from hereditary disease but also become more intelligent and fraternal than us. The development of these techniques for human use and animal husbandry was the catalyst for the cross fertilization of moral ideas and the development of a critical procreative morality. While eugenicists argued for a new critical morality, religious critics argued against artificial insemination because of its potential to damage important moral institutions. The tension between critical and conservative procreative morality is a feature of the contemporary debates about reproductive technologies. This and some of the other aspects of the early and contemporary debates about artificial insemination and reproductive technologies are discussed in this article.

Selection for a nondiapausing strain of artificially reared red oak borers

Treesearch

Jimmy R. Galford

1984-01-01

The incidence of nondiapause in artificially reared red oak borers increased from 4 to 61 percent in five generations. Fecundity dropped by more than 50 percent, but fertility was unaffected. Sixty percent of the nondiapausing larvae formed prepupa by the 12th week of development in the F1 and in the F4 generations.

A Collaborative Bovine Artificial Insemination Short Course for Students Attending a Caribbean Veterinary School

ERIC Educational Resources Information Center

Dalton, Joseph C.; Robinson, James Q.; DeJarnette, J. M.

2013-01-01

Artificial insemination (AI) of cattle is a critical career skill for veterinarians interested in food animal practice. Consequently, Ross University School of Veterinary Medicine Student Chapter of the American Association of Bovine Practitioners, Select Sires, and University of Idaho Extension have partnered to offer an intensive 2-day course to…

Persistence of Caliciviruses in Artificially Contaminated Oysters during Depuration▿

PubMed Central

Ueki, You; Shoji, Mika; Suto, Atsushi; Tanabe, Toru; Okimura, Yoko; Kikuchi, Yoshihiko; Saito, Noriyuki; Sano, Daisuke; Omura, Tatsuo

2007-01-01

The fate of calicivirus in oysters in a 10-day depuration was assessed. The norovirus gene was persistently detected from artificially contaminated oysters during the depuration, whereas feline calicivirus in oysters was promptly eliminated. The prolonged observation of norovirus in oysters implies the existence of a selective retention mechanism for norovirus within oysters. PMID:17630304

Examining the Impact of "Quiet" Vehicles on the Performance of Orientation and Mobility Tasks by Pedestrians Who Are Blind

ERIC Educational Resources Information Center

Emerson, Robert Wall; Kim, Dae Shik; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2011-01-01

Participants who are blind discriminated vehicle paths and made crossing decisions for hybrid vehicles with and without artificial sounds added. Several artificial sounds matched the performance of tasks observed with vehicles with internal combustion engines. These data, with previous vehicle-detection results, indicate that selecting artificial…

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Evaluation of artificial selection in Standard Poodles using whole-genome sequencing.

PubMed

Friedenberg, Steven G; Meurs, Kathryn M; Mackay, Trudy F C

2016-12-01

Identifying regions of artificial selection within dog breeds may provide insights into genetic variation that underlies breed-specific traits or diseases-particularly if these traits or disease predispositions are fixed within a breed. In this study, we searched for runs of homozygosity (ROH) and calculated the d i statistic (which is based upon F ST ) to identify regions of artificial selection in Standard Poodles using high-coverage, whole-genome sequencing data of 15 Standard Poodles and 49 dogs across seven other breeds. We identified consensus ROH regions ≥1 Mb in length and common to at least ten Standard Poodles covering 0.6 % of the genome, and d i regions that most distinguish Standard Poodles from other breeds covering 3.7 % of the genome. Within these regions, we identified enriched gene pathways related to olfaction, digestion, and taste, as well as pathways related to adrenal hormone biosynthesis, T cell function, and protein ubiquitination that could contribute to the pathogenesis of some Poodle-prevalent autoimmune diseases. We also validated variants related to hair coat and skull morphology that have previously been identified as being under selective pressure in Poodles, and flagged additional polymorphisms in genes such as ITGA2B, CBX4, and TNXB that may represent strong candidates for other common Poodle disorders.

Artificial selection for structural color on butterfly wings and comparison with natural evolution

PubMed Central

Wasik, Bethany R.; Liew, Seng Fatt; Lilien, David A.; Dinwiddie, April J.; Noh, Heeso; Cao, Hui; Monteiro, Antónia

2014-01-01

Brilliant animal colors often are produced from light interacting with intricate nano-morphologies present in biological materials such as butterfly wing scales. Surveys across widely divergent butterfly species have identified multiple mechanisms of structural color production; however, little is known about how these colors evolved. Here, we examine how closely related species and populations of Bicyclus butterflies have evolved violet structural color from brown-pigmented ancestors with UV structural color. We used artificial selection on a laboratory model butterfly, B. anynana, to evolve violet scales from UV brown scales and compared the mechanism of violet color production with that of two other Bicyclus species, Bicyclus sambulos and Bicyclus medontias, which have evolved violet/blue scales independently via natural selection. The UV reflectance peak of B. anynana brown scales shifted to violet over six generations of artificial selection (i.e., in less than 1 y) as the result of an increase in the thickness of the lower lamina in ground scales. Similar scale structures and the same mechanism for producing violet/blue structural colors were found in the other Bicyclus species. This work shows that populations harbor large amounts of standing genetic variation that can lead to rapid evolution of scales’ structural color via slight modifications to the scales’ physical dimensions. PMID:25092295

Artificial selection for structural color on butterfly wings and comparison with natural evolution.

PubMed

Wasik, Bethany R; Liew, Seng Fatt; Lilien, David A; Dinwiddie, April J; Noh, Heeso; Cao, Hui; Monteiro, Antónia

2014-08-19

Brilliant animal colors often are produced from light interacting with intricate nano-morphologies present in biological materials such as butterfly wing scales. Surveys across widely divergent butterfly species have identified multiple mechanisms of structural color production; however, little is known about how these colors evolved. Here, we examine how closely related species and populations of Bicyclus butterflies have evolved violet structural color from brown-pigmented ancestors with UV structural color. We used artificial selection on a laboratory model butterfly, B. anynana, to evolve violet scales from UV brown scales and compared the mechanism of violet color production with that of two other Bicyclus species, Bicyclus sambulos and Bicyclus medontias, which have evolved violet/blue scales independently via natural selection. The UV reflectance peak of B. anynana brown scales shifted to violet over six generations of artificial selection (i.e., in less than 1 y) as the result of an increase in the thickness of the lower lamina in ground scales. Similar scale structures and the same mechanism for producing violet/blue structural colors were found in the other Bicyclus species. This work shows that populations harbor large amounts of standing genetic variation that can lead to rapid evolution of scales' structural color via slight modifications to the scales' physical dimensions.

Fidelity of northern pine snakes (Pituophis m. melanoleucus) to natural and artificial hibernation sites in the New Jersey Pine Barrens.

PubMed

Zappalorti, Robert T; Burger, Joanna; Burkett, David W; Schneider, David W; McCort, Matthew P; Golden, David M

2014-01-01

Environmental managers require information on whether human-made hibernacula are used by rare snakes before constructing large numbers of them as mitigation measures. Fidelity of northern pine snakes (Pituophis m. melanoleucus) was examined in a 6-year study in the New Jersey Pine Barrens to determine whether they used natural and artificial hibernacula equally. Pine snakes used both artificial (human-made) and natural (snake-adapted) hibernacula. Most natural hibernacula were in abandoned burrows of large mammals. Occupancy rates were similar between natural and artificial hibernacula. Only 6 of 27 radio-tracked snakes did not shift hibernacula between years, whereas 78% shifted sites at least once, and fidelity from one year to the next was 42%. For snakes that switched hibernacula (n = 21), one switched among artificial hibernacula, 14 (65%) switched among natural hibernacula, and 6 (29%) switched from artificial to natural hibernacula. Data indicate that most pine snakes switch among hibernacula, mainly selecting natural hibernacula, suggesting that artificial dens are used, but protecting natural hibernacula should be a higher conservation priority.

Artificial Loading of ASC Specks with Cytosolic Antigens

PubMed Central

Sahillioğlu, Ali Can; Özören, Nesrin

2015-01-01

Inflammasome complexes form upon interaction of Nod Like Receptor (NLR) proteins with pathogen associated molecular patterns (PAPMS) inside the cytosol. Stimulation of a subset of inflammasome receptors including NLRP3, NLRC4 and AIM2 triggers formation of the micrometer-sized spherical supramolecular complex called the ASC speck. The ASC speck is thought to be the platform of inflammasome activity, but the reason why a supramolecular complex is preferred against oligomeric platforms remains elusive. We observed that a set of cytosolic proteins, including the model antigen ovalbumin, tend to co-aggregate on the ASC speck. We suggest that co-aggregation of antigenic proteins on the ASC speck during intracellular infection might be instrumental in antigen presentation. PMID:26258904

[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.

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

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.

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.

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.

Artificial selection for food colour preferences.

PubMed

Cole, Gemma L; Endler, John A

2015-04-07

Colour is an important factor in food detection and acquisition by animals using visually based foraging. Colour can be used to identify the suitability of a food source or improve the efficiency of food detection, and can even be linked to mate choice. Food colour preferences are known to exist, but whether these preferences are heritable and how these preferences evolve is unknown. Using the freshwater fish Poecilia reticulata, we artificially selected for chase behaviour towards two different-coloured moving stimuli: red and blue spots. A response to selection was only seen for chase behaviours towards the red, with realized heritabilities ranging from 0.25 to 0.30. Despite intense selection, no significant chase response was recorded for the blue-selected lines. This lack of response may be due to the motion-detection mechanism in the guppy visual system and may have novel implications for the evolvability of responses to colour-related signals. The behavioural response to several colours after five generations of selection suggests that the colour opponency system of the fish may regulate the response to selection. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Artificial selection for food colour preferences

PubMed Central

Cole, Gemma L.; Endler, John A.

2015-01-01

Colour is an important factor in food detection and acquisition by animals using visually based foraging. Colour can be used to identify the suitability of a food source or improve the efficiency of food detection, and can even be linked to mate choice. Food colour preferences are known to exist, but whether these preferences are heritable and how these preferences evolve is unknown. Using the freshwater fish Poecilia reticulata, we artificially selected for chase behaviour towards two different-coloured moving stimuli: red and blue spots. A response to selection was only seen for chase behaviours towards the red, with realized heritabilities ranging from 0.25 to 0.30. Despite intense selection, no significant chase response was recorded for the blue-selected lines. This lack of response may be due to the motion-detection mechanism in the guppy visual system and may have novel implications for the evolvability of responses to colour-related signals. The behavioural response to several colours after five generations of selection suggests that the colour opponency system of the fish may regulate the response to selection. PMID:25740894

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.

Geological and technological assessment of artificial reef sites, Louisiana outer continental shelf

USGS Publications Warehouse

Pope, D.L.; Moslow, T.F.; Wagner, J.B.

1993-01-01

This paper describes the general procedures used to select sites for obsolete oil and gas platforms as artificial reefs on the Louisiana outer continental shelf (OCS). The methods employed incorporate six basic steps designed to resolve multiple-use conflicts that might otherwise arise with daily industry and commercial fishery operations, and to identify and assess both geological and technological constraints that could affect placement of the structures. These steps include: (1) exclusion mapping; (2) establishment of artificial reef planning areas; (3) database compilation; (4) assessment and interpretation of database; (5) mapping of geological and man-made features within each proposed reef site; and (6) site selection. Nautical charts, bathymetric maps, and offshore oil and gas maps were used for exclusion mapping, and to select nine regional planning areas. Pipeline maps were acquired from federal agencies and private industry to determine their general locations within each planning area, and to establish exclusion fairways along each pipeline route. Approximately 1600 line kilometers of high-resolution geophysical data collected by federal agencies and private industry was acquired for the nine planning areas. These data were interpreted to determine the nature and extent of near-surface geologic features that could affect placement of the structures. Seismic reflection patterns were also characterized to evaluate near-bottom sedimentation processes in the vicinity of each reef site. Geotechnical borings were used to determine the lithological and physical properties of the sediment, and for correlation with the geophysical data. Since 1987, five sites containing 10 obsolete production platforms have been selected on the Louisiana OCS using these procedures. Industry participants have realized a total savings of approximately US $1 500 000 in salvaging costs by converting these structures into artificial reefs. ?? 1993.

A photocatalyst-enzyme coupled artificial photosynthesis system for solar energy in production of formic acid from CO2.

PubMed

Yadav, Rajesh K; Baeg, Jin-Ook; Oh, Gyu Hwan; Park, No-Joong; Kong, Ki-jeong; Kim, Jinheung; Hwang, Dong Won; Biswas, Soumya K

2012-07-18

The photocatalyst-enzyme coupled system for artificial photosynthesis process is one of the most promising methods of solar energy conversion for the synthesis of organic chemicals or fuel. Here we report the synthesis of a novel graphene-based visible light active photocatalyst which covalently bonded the chromophore, such as multianthraquinone substituted porphyrin with the chemically converted graphene as a photocatalyst of the artificial photosynthesis system for an efficient photosynthetic production of formic acid from CO(2). The results not only show a benchmark example of the graphene-based material used as a photocatalyst in general artificial photosynthesis but also the benchmark example of the selective production system of solar chemicals/solar fuel directly from CO(2).

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).

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

NASA Astrophysics Data System (ADS)

Shamsoddini, A.; Aboodi, M. R.; Karami, J.

2017-09-01

Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase.

PubMed

Wang, Bin; Cancilla, John C; Torrecilla, Jose S; Haick, Hossam

2014-02-12

The use of molecularly modified Si nanowire field effect transistors (SiNW FETs) for selective detection in the liquid phase has been successfully demonstrated. In contrast, selective detection of chemical species in the gas phase has been rather limited. In this paper, we show that the application of artificial intelligence on deliberately controlled SiNW FET device parameters can provide high selectivity toward specific volatile organic compounds (VOCs). The obtained selectivity allows identifying VOCs in both single-component and multicomponent environments as well as estimating the constituent VOC concentrations. The effect of the structural properties (functional group and/or chain length) of the molecular modifications on the accuracy of VOC detection is presented and discussed. The reported results have the potential to serve as a launching pad for the use of SiNW FET sensors in real-world counteracting conditions and/or applications.

Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

ERIC Educational Resources Information Center

Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

2001-01-01

Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

Artificial intelligence applications in the intensive care unit.

PubMed

Hanson, C W; Marshall, B E

2001-02-01

To review the history and current applications of artificial intelligence in the intensive care unit. The MEDLINE database, bibliographies of selected articles, and current texts on the subject. The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis. All literature relevant to the topic was reviewed. Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control. The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.

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

Detection of artificially ripened mango using spectrometric analysis

NASA Astrophysics Data System (ADS)

Mithun, B. S.; Mondal, Milton; Vishwakarma, Harsh; Shinde, Sujit; Kimbahune, Sanjay

2017-05-01

Hyperspectral sensing has been proven to be useful to determine the quality of food in general. It has also been used to distinguish naturally and artificially ripened mangoes by analyzing the spectral signature. However the focus has been on improving the accuracy of classification after performing dimensionality reduction, optimum feature selection and using suitable learning algorithm on the complete visible and NIR spectrum range data, namely 350nm to 1050nm. In this paper we focus on, (i) the use of low wavelength resolution and low cost multispectral sensor to reliably identify artificially ripened mango by selectively using the spectral information so that classification accuracy is not hampered at the cost of low resolution spectral data and (ii) use of visible spectrum i.e. 390nm to 700 nm data to accurately discriminate artificially ripened mangoes. Our results show that on a low resolution spectral data, the use of logistic regression produces an accuracy of 98.83% and outperforms other methods like classification tree, random forest significantly. And this is achieved by analyzing only 36 spectral reflectance data points instead of the complete 216 data points available in visual and NIR range. Another interesting experimental observation is that we are able to achieve more than 98% classification accuracy by selecting only 15 irradiance values in the visible spectrum. Even the number of data needs to be collected using hyper-spectral or multi-spectral sensor can be reduced by a factor of 24 for classification with high degree of confidence

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.

Fitness consequences of artificial selection on relative male genital size

PubMed Central

Booksmythe, Isobel; Head, Megan L.; Keogh, J. Scott; Jennions, Michael D.

2016-01-01

Male genitalia often show remarkable differences among related species in size, shape and complexity. Across poeciliid fishes, the elongated fin (gonopodium) that males use to inseminate females ranges from 18 to 53% of body length. Relative genital size therefore varies greatly among species. In contrast, there is often tight within-species allometric scaling, which suggests strong selection against genital–body size combinations that deviate from a species' natural line of allometry. We tested this constraint by artificially selecting on the allometric intercept, creating lines of males with relatively longer or shorter gonopodia than occur naturally for a given body size in mosquitofish, Gambusia holbrooki. We show that relative genital length is heritable and diverged 7.6–8.9% between our up-selected and down-selected lines, with correlated changes in body shape. However, deviation from the natural line of allometry does not affect male success in assays of attractiveness, swimming performance and, crucially, reproductive success (paternity). PMID:27188478

The effect of artificial selection on phenotypic plasticity in maize.

PubMed

Gage, Joseph L; Jarquin, Diego; Romay, Cinta; Lorenz, Aaron; Buckler, Edward S; Kaeppler, Shawn; Alkhalifah, Naser; Bohn, Martin; Campbell, Darwin A; Edwards, Jode; Ertl, David; Flint-Garcia, Sherry; Gardiner, Jack; Good, Byron; Hirsch, Candice N; Holland, Jim; Hooker, David C; Knoll, Joseph; Kolkman, Judith; Kruger, Greg; Lauter, Nick; Lawrence-Dill, Carolyn J; Lee, Elizabeth; Lynch, Jonathan; Murray, Seth C; Nelson, Rebecca; Petzoldt, Jane; Rocheford, Torbert; Schnable, James; Schnable, Patrick S; Scully, Brian; Smith, Margaret; Springer, Nathan M; Srinivasan, Srikant; Walton, Renee; Weldekidan, Teclemariam; Wisser, Randall J; Xu, Wenwei; Yu, Jianming; de Leon, Natalia

2017-11-07

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0-5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

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.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Artificial limb representation in amputees

PubMed Central

van den Heiligenberg, Fiona M Z; Orlov, Tanya; Macdonald, Scott N; Duff, Eugene P; Henderson Slater, David; Beckmann, Christian F; Johansen-Berg, Heidi; Culham, Jody C; Makin, Tamar R

2018-01-01

Abstract The human brain contains multiple hand-selective areas, in both the sensorimotor and visual systems. Could our brain repurpose neural resources, originally developed for supporting hand function, to represent and control artificial limbs? We studied individuals with congenital or acquired hand-loss (hereafter one-handers) using functional MRI. We show that the more one-handers use an artificial limb (prosthesis) in their everyday life, the stronger visual hand-selective areas in the lateral occipitotemporal cortex respond to prosthesis images. This was found even when one-handers were presented with images of active prostheses that share the functionality of the hand but not necessarily its visual features (e.g. a ‘hook’ prosthesis). Further, we show that daily prosthesis usage determines large-scale inter-network communication across hand-selective areas. This was demonstrated by increased resting state functional connectivity between visual and sensorimotor hand-selective areas, proportional to the intensiveness of everyday prosthesis usage. Further analysis revealed a 3-fold coupling between prosthesis activity, visuomotor connectivity and usage, suggesting a possible role for the motor system in shaping use-dependent representation in visual hand-selective areas, and/or vice versa. Moreover, able-bodied control participants who routinely observe prosthesis usage (albeit less intensively than the prosthesis users) showed significantly weaker associations between degree of prosthesis observation and visual cortex activity or connectivity. Together, our findings suggest that altered daily motor behaviour facilitates prosthesis-related visual processing and shapes communication across hand-selective areas. This neurophysiological substrate for prosthesis embodiment may inspire rehabilitation approaches to improve usage of existing substitutionary devices and aid implementation of future assistive and augmentative technologies. PMID:29534154

Artificial limb representation in amputees.

PubMed

van den Heiligenberg, Fiona M Z; Orlov, Tanya; Macdonald, Scott N; Duff, Eugene P; Henderson Slater, David; Beckmann, Christian F; Johansen-Berg, Heidi; Culham, Jody C; Makin, Tamar R

2018-05-01

The human brain contains multiple hand-selective areas, in both the sensorimotor and visual systems. Could our brain repurpose neural resources, originally developed for supporting hand function, to represent and control artificial limbs? We studied individuals with congenital or acquired hand-loss (hereafter one-handers) using functional MRI. We show that the more one-handers use an artificial limb (prosthesis) in their everyday life, the stronger visual hand-selective areas in the lateral occipitotemporal cortex respond to prosthesis images. This was found even when one-handers were presented with images of active prostheses that share the functionality of the hand but not necessarily its visual features (e.g. a 'hook' prosthesis). Further, we show that daily prosthesis usage determines large-scale inter-network communication across hand-selective areas. This was demonstrated by increased resting state functional connectivity between visual and sensorimotor hand-selective areas, proportional to the intensiveness of everyday prosthesis usage. Further analysis revealed a 3-fold coupling between prosthesis activity, visuomotor connectivity and usage, suggesting a possible role for the motor system in shaping use-dependent representation in visual hand-selective areas, and/or vice versa. Moreover, able-bodied control participants who routinely observe prosthesis usage (albeit less intensively than the prosthesis users) showed significantly weaker associations between degree of prosthesis observation and visual cortex activity or connectivity. Together, our findings suggest that altered daily motor behaviour facilitates prosthesis-related visual processing and shapes communication across hand-selective areas. This neurophysiological substrate for prosthesis embodiment may inspire rehabilitation approaches to improve usage of existing substitutionary devices and aid implementation of future assistive and augmentative technologies.

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.

Multi-probe-based resonance-frequency electrical impedance spectroscopy for detection of suspicious breast lesions: improving performance using partial ROC optimization

NASA Astrophysics Data System (ADS)

Lederman, Dror; Zheng, Bin; Wang, Xingwei; Wang, Xiao Hui; Gur, David

2011-03-01

We have developed a multi-probe resonance-frequency electrical impedance spectroscope (REIS) system to detect breast abnormalities. Based on assessing asymmetry in REIS signals acquired between left and right breasts, we developed several machine learning classifiers to classify younger women (i.e., under 50YO) into two groups of having high and low risk for developing breast cancer. In this study, we investigated a new method to optimize performance based on the area under a selected partial receiver operating characteristic (ROC) curve when optimizing an artificial neural network (ANN), and tested whether it could improve classification performance. From an ongoing prospective study, we selected a dataset of 174 cases for whom we have both REIS signals and diagnostic status verification. The dataset includes 66 "positive" cases recommended for biopsy due to detection of highly suspicious breast lesions and 108 "negative" cases determined by imaging based examinations. A set of REIS-based feature differences, extracted from the two breasts using a mirror-matched approach, was computed and constituted an initial feature pool. Using a leave-one-case-out cross-validation method, we applied a genetic algorithm (GA) to train the ANN with an optimal subset of features. Two optimization criteria were separately used in GA optimization, namely the area under the entire ROC curve (AUC) and the partial area under the ROC curve, up to a predetermined threshold (i.e., 90% specificity). The results showed that although the ANN optimized using the entire AUC yielded higher overall performance (AUC = 0.83 versus 0.76), the ANN optimized using the partial ROC area criterion achieved substantially higher operational performance (i.e., increasing sensitivity level from 28% to 48% at 95% specificity and/ or from 48% to 58% at 90% specificity).

A prototype of behavior selection mechanism based on emotion

NASA Astrophysics Data System (ADS)

Zhang, Guofeng; Li, Zushu

2007-12-01

In bionic methodology rather than in design methodology more familiar with, summarizing the psychological researches of emotion, we propose the biologic mechanism of emotion, emotion selection role in creature evolution and a anima framework including emotion similar to the classical control structure; and consulting Prospect Theory, build an Emotion Characteristic Functions(ECF) that computer emotion; two more emotion theories are added to them that higher emotion is preferred and middle emotion makes brain run more efficiently, emotional behavior mechanism comes into being. A simulation of proposed mechanism are designed and carried out on Alife Swarm software platform. In this simulation, a virtual grassland ecosystem is achieved where there are two kinds of artificial animals: herbivore and preyer. These artificial animals execute four types of behavior: wandering, escaping, finding food, finding sex partner in their lives. According the theories of animal ethnology, escaping from preyer is prior to other behaviors for its existence, finding food is secondly important behavior, rating is third one and wandering is last behavior. In keeping this behavior order, based on our behavior characteristic function theory, the specific functions of emotion computing are built of artificial autonomous animals. The result of simulation confirms the behavior selection mechanism.

Genetic Divergence and Chemotype Diversity in the Fusarium Head Blight Pathogen Fusarium poae.

PubMed

Vanheule, Adriaan; De Boevre, Marthe; Moretti, Antonio; Scauflaire, Jonathan; Munaut, Françoise; De Saeger, Sarah; Bekaert, Boris; Haesaert, Geert; Waalwijk, Cees; van der Lee, Theo; Audenaert, Kris

2017-08-23

Fusarium head blight is a disease caused by a complex of Fusarium species. F. poae is omnipresent throughout Europe in spite of its low virulence. In this study, we assessed a geographically diverse collection of F. poae isolates for its genetic diversity using AFLP (Amplified Fragment Length Polymorphism). Furthermore, studying the mating type locus and chromosomal insertions, we identified hallmarks of both sexual recombination and clonal spread of successful genotypes in the population. Despite the large genetic variation found, all F. poae isolates possess the nivalenol chemotype based on Tri7 sequence analysis. Nevertheless, Tri gene clusters showed two layers of genetic variability. Firstly, the Tri1 locus was highly variable with mostly synonymous mutations and mutations in introns pointing to a strong purifying selection pressure. Secondly, in a subset of isolates, the main trichothecene gene cluster was invaded by a transposable element between Tri5 and Tri6 . To investigate the impact of these variations on the phenotypic chemotype, mycotoxin production was assessed on artificial medium. Complex blends of type A and type B trichothecenes were produced but neither genetic variability in the Tri genes nor variability in the genome or geography accounted for the divergence in trichothecene production. In view of its complex chemotype, it will be of utmost interest to uncover the role of trichothecenes in virulence, spread and survival of F. poae .

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.

Artificial intelligence techniques for embryo and oocyte classification.

PubMed

Manna, Claudio; Nanni, Loris; Lumini, Alessandra; Pappalardo, Sebastiana

2013-01-01

One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in the capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. This work concentrates the efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the local binary patterns). The proposed system was tested on two data sets of 269 oocytes and 269 corresponding embryos from 104 women and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they show an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in our capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. In this work, we concentrate our efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the 'local binary patterns'). The proposed system is tested on two data sets, of 269 oocytes and 269 corresponding embryos from 104 women, and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they showed an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. Copyright © 2012 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Using Association Mapping in Teosinte (Zea Mays ssp Parviglumis) to Investigate the Function of Selection-Candidate Genes

USDA-ARS?s Scientific Manuscript database

Large-scale screens of the maize genome identified 48 genes that show the putative signature of artificial selection during maize domestication or improvement. These selection-candidate genes may act as quantitative trait loci (QTL) that control the phenotypic differences between maize and its proge...

Recurrent selection performance for FOV race 4 resistance in selected cotton germplasm and progeny

USDA-ARS?s Scientific Manuscript database

Recurrent selection is being used to improve Fusarium oxysporum f. sp. vasinfectum race 4 (FOV4) resistance in Upland (Gossypium hirsutum L.) and Pima (G. barbadense L.) cotton using naturally infested fields and artificially inoculum-greenhouse sites. One of our target objectives is to introduce a ...

Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model.

PubMed

Jung, Won-Mo; Park, In-Soo; Lee, Ye-Seul; Kim, Chang-Eop; Lee, Hyangsook; Hahm, Dae-Hyun; Park, Hi-Joon; Jang, Bo-Hyoung; Chae, Younbyoung

2018-04-12

Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.

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.

Genetic Divergence between Camellia sinensis and Its Wild Relatives Revealed via Genome-Wide SNPs from RAD Sequencing.

PubMed

Yang, Hua; Wei, Chao-Ling; Liu, Hong-Wei; Wu, Jun-Lan; Li, Zheng-Guo; Zhang, Liang; Jian, Jian-Bo; Li, Ye-Yun; Tai, Yu-Ling; Zhang, Jing; Zhang, Zheng-Zhu; Jiang, Chang-Jun; Xia, Tao; Wan, Xiao-Chun

2016-01-01

Tea is one of the most popular beverages across the world and is made exclusively from cultivars of Camellia sinensis. Many wild relatives of the genus Camellia that are closely related to C. sinensis are native to Southwest China. In this study, we first identified the distinct genetic divergence between C. sinensis and its wild relatives and provided a glimpse into the artificial selection of tea plants at a genome-wide level by analyzing 15,444 genomic SNPs that were identified from 18 cultivated and wild tea accessions using a high-throughput genome-wide restriction site-associated DNA sequencing (RAD-Seq) approach. Six distinct clusters were detected by phylogeny inferrence and principal component and genetic structural analyses, and these clusters corresponded to six Camellia species/varieties. Genetic divergence apparently indicated that C. taliensis var. bangwei is a semi-wild or transient landrace occupying a phylogenetic position between those wild and cultivated tea plants. Cultivated accessions exhibited greater heterozygosity than wild accessions, with the exception of C. taliensis var. bangwei. Thirteen genes with non-synonymous SNPs exhibited strong selective signals that were suggestive of putative artificial selective footprints for tea plants during domestication. The genome-wide SNPs provide a fundamental data resource for assessing genetic relationships, characterizing complex traits, comparing heterozygosity and analyzing putatitve artificial selection in tea plants.

7 CFR 357.2 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Regulations of the Department of Agriculture (Continued) ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF AGRICULTURE CONTROL OF ILLEGALLY TAKEN PLANTS § 357.2 Definitions. Artificial selection. The process of selecting plants for particular traits, through such means as breeding, cloning, or genetic...

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.

Cotton genotypes selection through artificial neural networks.

PubMed

Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

2017-09-27

Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

Development of a novel tissue-engineered nitinol frame artificial trachea with native-like physical characteristics.

PubMed

Sakaguchi, Yasuto; Sato, Toshihiko; Muranishi, Yusuke; Yutaka, Yojiro; Komatsu, Teruya; Omori, Koichi; Nakamura, Tatsuo; Date, Hiroshi

2018-04-24

Tracheal reconstruction is complicated by the short length to which a trachea can be resected. We previously developed a biocompatible polypropylene frame artificial trachea, but it lacked the strength and flexibility of the native trachea. In contrast, nitinol may provide these physical characteristics. We developed a novel nitinol frame artificial trachea and examined its biocompatibility and safety in canine models. We constructed several nitinol frame prototypes and selected the frame that most closely reproduced the strength of the native canine trachea. This frame was used to create a collagen-coated artificial trachea that was implanted into 5 adult beagle dogs. The artificial trachea was first implanted into the pedicled omentum and placed in the abdomen. Three weeks later, the omentum-wrapped artificial trachea was moved into the thoracic cavity. The thoracic trachea was then partially resected and reconstructed using the artificial trachea. Follow-up bronchoscopic evaluation was performed, and the artificial trachea was histologically examined after the dogs were sacrificed. Stenosis at the anastomosis sites was not observed in any dog. Survival for 18 months or longer was confirmed in all dogs but 1, which died after 9 months due to reasons unrelated to the artificial trachea. Histological examination confirmed respiratory epithelial regeneration on the artificial trachea's luminal surface. Severe foreign body reaction was not detected around the nitinol frame. The novel nitinol artificial trachea reproduced the physical characteristics of the native trachea. We have confirmed cell engraftment, good biocompatibility, and survival of 18 months or longer for this artificial trachea in canine models. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Short communication: Field fertility in Holstein bulls: Can type of breeding strategy (artificial insemination following estrus versus timed artificial insemination) alter service sire fertility?

PubMed

Batista, E O S; Vieira, L M; Sá Filho, M F; Carvalho, P D; Rivera, H; Cabrera, V; Wiltbank, M C; Baruselli, P S; Souza, A H

2016-03-01

The aim of this study was to compare pregnancy per artificial insemination (P/AI) from service sires used on artificial insemination after estrus detection (EAI) or timed artificial insemination (TAI) breedings. Confirmed artificial insemination outcome records from 3 national data centers were merged and used as a data source. Criteria edits were herd's overall P/AI within 20 and 60%, a minimum of 30 breedings reported per herd-year, service sires that were used in at least 10 different herds with no more than 40% of the breedings performed in a single herd, breeding records from lactating Holstein cows receiving their first to fifth postpartum breedings occurring within 45 to 375 d in milk, and cows with 1 to 5 lactations producing a minimum of 6,804 kg. Initially 1,142,859 breeding records were available for analysis. After editing, a subset of the data (n=857,539) was used to classify breeding codes into either EAI or TAI based on weekly insemination profile in each individual herd. The procedure HPMIXED of SAS was used and took into account effects of state, farm, cow identification, breeding month, year, parity, days in milk at breeding, and service sire. This model was used independently for the 2 types osires f breeding codes (EAI vs. TAI), and service sire P/AI rankings within each breeding code were performed for sires with >700 breedings (94 sires) and for with >1,000 breedings (n=56 sires) following both EAI and TAI. Correlation for service sire fertility rankings following EAI and TAI was performed with the PROC CORR of SAS. Service sire P/AI rankings produced with EAI and TAI were 0.81 (for sires with >700 breedings) and 0.84 (for sires with >1,000 breedings). In addition, important changes occurred in service sire P/AI ranking to EAI and TAI for sires with less than 10,000 recorded artificial inseminations. In conclusion, the type of breeding strategy (EAI or TAI) was associated with some changes in service sire P/AI ranking, but ranking changes declined as number of breedings per service sire increased. Future randomized studies need to explore whether changes in P/AI ranking to EAI versus TAI are due to specific semen characteristics. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

NASA Astrophysics Data System (ADS)

Li, Zhichen; An, Qiu; Ji, Changying

The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

Microbial Communities as Experimental Units

PubMed Central

DAY, MITCH D.; BECK, DANIEL; FOSTER, JAMES A.

2011-01-01

Artificial ecosystem selection is an experimental technique that treats microbial communities as though they were discrete units by applying selection on community-level properties. Highly diverse microbial communities associated with humans and other organisms can have significant impacts on the health of the host. It is difficult to find correlations between microbial community composition and community-associated diseases, in part because it may be impossible to define a universal and robust species concept for microbes. Microbial communities are composed of potentially thousands of unique populations that evolved in intimate contact, so it is appropriate in many situations to view the community as the unit of analysis. This perspective is supported by recent discoveries using metagenomics and pangenomics. Artificial ecosystem selection experiments can be costly, but they bring the logical rigor of biological model systems to the emerging field of microbial community analysis. PMID:21731083

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

Translating an AI application from Lisp to Ada: A case study

NASA Technical Reports Server (NTRS)

Davis, Gloria J.

1991-01-01

A set of benchmarks was developed to test the performance of a newly designed computer executing both Lisp and Ada. Among these was AutoClassII -- a large Artificial Intelligence (AI) application written in Common Lisp. The extraction of a representative subset of this complex application was aided by a Lisp Code Analyzer (LCA). The LCA enabled rapid analysis of the code, putting it in a concise and functionally readable form. An equivalent benchmark was created in Ada through manual translation of the Lisp version. A comparison of the execution results of both programs across a variety of compiler-machine combinations indicate that line-by-line translation coupled with analysis of the initial code can produce relatively efficient and reusable target code.

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.

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.

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…

A genome-wide scan for signatures of directional selection in domesticated pigs.

PubMed

Moon, Sunjin; Kim, Tae-Hun; Lee, Kyung-Tai; Kwak, Woori; Lee, Taeheon; Lee, Si-Woo; Kim, Myung-Jick; Cho, Kyuho; Kim, Namshin; Chung, Won-Hyong; Sung, Samsun; Park, Taesung; Cho, Seoae; Groenen, Martien Am; Nielsen, Rasmus; Kim, Yuseob; Kim, Heebal

2015-02-25

Animal domestication involved drastic phenotypic changes driven by strong artificial selection and also resulted in new populations of breeds, established by humans. This study aims to identify genes that show evidence of recent artificial selection during pig domestication. Whole-genome resequencing of 30 individual pigs from domesticated breeds, Landrace and Yorkshire, and 10 Asian wild boars at ~16-fold coverage was performed resulting in over 4.3 million SNPs for 19,990 genes. We constructed a comprehensive genome map of directional selection by detecting selective sweeps using an F ST-based approach that detects directional selection in lineages leading to the domesticated breeds and using a haplotype-based test that detects ongoing selective sweeps within the breeds. We show that candidate genes under selection are significantly enriched for loci implicated in quantitative traits important to pig reproduction and production. The candidate gene with the strongest signals of directional selection belongs to group III of the metabolomics glutamate receptors, known to affect brain functions associated with eating behavior, suggesting that loci under strong selection include loci involved in behaviorial traits in domesticated pigs including tameness. We show that a significant proportion of selection signatures coincide with loci that were previously inferred to affect phenotypic variation in pigs. We further identify functional enrichment related to behavior, such as signal transduction and neuronal activities, for those targets of selection during domestication in pigs.

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem.

PubMed

Zamdborg, Leonid; Holloway, David M; Merelo, Juan J; Levchenko, Vladimir F; Spirov, Alexander V

2015-06-10

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of "genomic parasites", such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem

PubMed Central

Zamdborg, Leonid; Holloway, David M.; Merelo, Juan J.; Levchenko, Vladimir F.; Spirov, Alexander V.

2015-01-01

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of “genomic parasites”, such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts. PMID:25767296

Vegetation in transition: the Southwest's dynamic past century

Treesearch

Raymond M. Turner

2005-01-01

Monitoring that follows long-term vegetation changes often requires selection of a temporal baseline. Any such starting point is to some degree artificial, but in some instances there are aids that can be used as guides to baseline selection. Matched photographs duplicating scenes first recorded on film a century or more ago reveal changes that help select the starting...

[Algorithms of artificial neural networks--practical application in medical science].

PubMed

Stefaniak, Bogusław; Cholewiński, Witold; Tarkowska, Anna

2005-12-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach

NASA Astrophysics Data System (ADS)

Pchelintseva, Svetlana V.; Runnova, Anastasia E.; Musatov, Vyacheslav Yu.; Hramov, Alexander E.

2017-03-01

In the paper we study the problem of recognition type of the observed object, depending on the generated pattern and the registered EEG data. EEG recorded at the time of displaying cube Necker characterizes appropriate state of brain activity. As an image we use bistable image Necker cube. Subject selects the type of cube and interpret it either as aleft cube or as the right cube. To solve the problem of recognition, we use artificial neural networks. In our paper to create a classifier we have considered a multilayer perceptron. We examine the structure of the artificial neural network and define cubes recognition accuracy.

On the Tesseral-Harmonics Resonance Problem in Artificial-Satellite Theory, Part 2

NASA Technical Reports Server (NTRS)

Romanowicz, B. A.

1976-01-01

Equations were derived for the perturbations on an artificial satellite when the motion of the satellite is commensurable with that of the earth. This was done by first selecting the tesseral harmonics that contribute the most to the perturbations and then by applying Hori's method by use of Lie series. Here, are introduced some modifications to the perturbations, which now result in better agreement with numerical integration.

Army Acquisition Management: A Quest for Excellence or a Tilting of Windmills

DTIC Science & Technology

1991-04-12

dollars, time, and decisions. And its results are so 48 artificial that they may relate to subsequent field data. I am a strong proponent of testing...An entire complex, nearly undecipherable artificial world has been constructed by the testers. No one but they understand the rules, the criteria...the Army HSCI House Select Committee on Intellegence IS information system IRM information resources management ISC Information Systems Command

Artificial sunlight and ultraviolet light induced photo-epoxidation of propylene over V-Ti/MCM-41 photocatalyst

PubMed Central

Nguyen, Van-Huy; Bai, Hsunling

2014-01-01

Summary The light irradiation parameters, including the wavelength spectrum and intensity of light source, can significantly influence a photocatalytic reaction. This study examines the propylene photo-epoxidation over V-Ti/MCM-41 photocatalyst by using artificial sunlight (Xe lamp with/without an Air Mass 1.5 Global Filter at 1.6/18.5 mW·cm−2) and ultraviolet light (Mercury Arc lamp with different filters in the range of 0.1–0.8 mW·cm−2). This is the first report of using artificial sunlight to drive the photo-epoxidation of propylene. Over V-Ti/MCM-41 photocatalyst, the propylene oxide (PO) formation rate is 193.0 and 112.1 µmol·gcat −1·h−1 with a PO selectivity of 35.0 and 53.7% under UV light and artificial sunlight, respectively. A normalized light utilization (NLU) index is defined and found to correlate well with the rate of both PO formation and C3H6 consumption in log–log scale. The light utilization with a mercury arc lamp is better than with a xenon lamp. The selectivity to PO remains practically unchanged with respect to NLU, suggesting that the photo-epoxidation occurs through the same mechanism under the conditions tested in this study. PMID:24991493

Impact of alternative regeneration methods on genetic diversity in coastal Douglas-fir

USGS Publications Warehouse

Adams, W.T.; Zuo, J.; Shimizu, J.Y.; Tappeiner, J. C.

1998-01-01

Genetic implications of natural and artificial regeneration following three regeneration methods (group selection, shelterwood, and clearcut) were investigated in coastal Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) using genetic markers (17 allozyme loci). In general, harvesting followed by either natural or artificial regeneration resulted in offspring populations little altered from those in the previous generation. Cutting the smallest trees to form shelterwoods, however, resulted in the removal of rare, presumably deleterious, alleles, such that slightly fewer alleles per locus were observed among residual trees (2.76) and natural regeneration (2.75) than found in uncut (control) stands (2.86). Thus, although the shelterwood regime appears quite compatible with gene conservation, it would be best to leave parent trees of a range of sizes in shelterwoods designated as gene conservation reserves, in order to maximize the number of alleles (regardless of current adaptive value) in naturally regenerated offspring. Seedling stocks used for artificial regeneration in clearcut, shelterwood, and group selection stands (7 total) had significantly greater levels of genetic diversity, on average, than found in natural regeneration. This is probably because the seeds used in artificial seedling stocks came from many wild stands and thus, sampled more diversity than found in single populations. For. Sci. 44(3): 390-396.

Argus II retinal prosthesis system: a review of patient selection criteria, surgical considerations, and post-operative outcomes.

PubMed

Finn, Avni P; Grewal, Dilraj S; Vajzovic, Lejla

2018-01-01

Retinitis pigmentosa (RP) is a group of heterogeneous inherited retinal degenerative disorders characterized by progressive rod and cone dysfunction and ensuing photoreceptor loss. Many patients suffer from legal blindness by their 40s or 50s. Artificial vision is considered once patients have lost all vision to the point of bare light perception or no light perception. The Argus II retinal prosthesis system is one such artificial vision device approved for patients with RP. This review focuses on the factors important for patient selection. Careful pre-operative screening, counseling, and management of patient expectations are critical for the successful implantation and visual rehabilitation of patients with the Argus II device.

Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

PubMed

Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

2014-12-24

This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

Recent Advances in Skin-Inspired Sensors Enabled by Nanotechnology

NASA Astrophysics Data System (ADS)

Loh, Kenneth J.; Azhari, Faezeh

2012-07-01

The highly optimized performance of nature's creations and biological assemblies has inspired the development of their bio-inspired artificial counterparts that can potentially outperform conventional systems. In particular, the skin of humans, animals, and insects exhibits unique functionalities and properties and has subsequently led to active research in developing skin-inspired sensors. This paper presents a summary of selected work related to skin-inspired tactile, distributed strain, and artificial hair cell flow sensors, with a particular focus on technologies enabled by recent advancements in the nanotechnology domain. The purpose is not to present a comprehensive review on this broad subject matter but rather to use selected work to outline the diversity of current research activities.

Use of Cement Kiln Dust, Blast Furnace Slag and Marble Sludge in the Manufacture of Sustainable Artificial Aggregates by Means of Cold Bonding Pelletization.

PubMed

Colangelo, Francesco; Cioffi, Raffaele

2013-07-25

In this work, three different samples of solid industrial wastes cement kiln dust (CKD), granulated blast furnace slag and marble sludge were employed in a cold bonding pelletization process for the sustainable production of artificial aggregates. The activating action of CKD components on the hydraulic behavior of the slag was explored by evaluating the neo-formed phases present in several hydrated pastes. Particularly, the influence of free CaO and sulfates amount in the two CKD samples on slag reactivity was evaluated. Cold bonded artificial aggregates were characterized by determining physical and mechanical properties of two selected size fractions of the granules for each studied mixture. Eighteen types of granules were employed in C28/35 concrete manufacture where coarser natural aggregate were substituted with the artificial ones. Finally, lightweight concretes were obtained, proving the suitability of the cold bonding pelletization process in artificial aggregate sustainable production.

Use of Cement Kiln Dust, Blast Furnace Slag and Marble Sludge in the Manufacture of Sustainable Artificial Aggregates by Means of Cold Bonding Pelletization

PubMed Central

Colangelo, Francesco; Cioffi, Raffaele

2013-01-01

In this work, three different samples of solid industrial wastes cement kiln dust (CKD), granulated blast furnace slag and marble sludge were employed in a cold bonding pelletization process for the sustainable production of artificial aggregates. The activating action of CKD components on the hydraulic behavior of the slag was explored by evaluating the neo-formed phases present in several hydrated pastes. Particularly, the influence of free CaO and sulfates amount in the two CKD samples on slag reactivity was evaluated. Cold bonded artificial aggregates were characterized by determining physical and mechanical properties of two selected size fractions of the granules for each studied mixture. Eighteen types of granules were employed in C28/35 concrete manufacture where coarser natural aggregate were substituted with the artificial ones. Finally, lightweight concretes were obtained, proving the suitability of the cold bonding pelletization process in artificial aggregate sustainable production. PMID:28811427

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.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

A genome-wide scan for selection signatures in Nelore cattle

USDA-ARS?s Scientific Manuscript database

Brazilian Nelore cattle have been selected for growth traits over more than four decades. In recent years, reproductive and meat quality traits have become more important because of increasing consumption, exports and consumer demand. The identification of genomic regions altered by artificial selec...

Notice of release of Fowler germplasm green needlegrass

USDA-ARS?s Scientific Manuscript database

Fowler Germplasm is a new pre-variety germplasm release of green needlegrass developed by artificial selection from five local populations collected in southern Alberta. Selection emphasis was placed on seed yield and germinability. This plant material is expected to be used for restoration and wi...

Evolving artificial metalloenzymes via random mutagenesis

NASA Astrophysics Data System (ADS)

Yang, Hao; Swartz, Alan M.; Park, Hyun June; Srivastava, Poonam; Ellis-Guardiola, Ken; Upp, David M.; Lee, Gihoon; Belsare, Ketaki; Gu, Yifan; Zhang, Chen; Moellering, Raymond E.; Lewis, Jared C.

2018-03-01

Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free cofactor must be eliminated. Here we report an efficient platform to create and screen libraries of artificial metalloenzymes (ArMs) via random mutagenesis, which we use to evolve highly selective dirhodium cyclopropanases. Error-prone PCR and combinatorial codon mutagenesis enabled multiplexed analysis of random mutations, including at sites distal to the putative ArM active site that are difficult to identify using targeted mutagenesis approaches. Variants that exhibited significantly improved selectivity for each of the cyclopropane product enantiomers were identified, and higher activity than previously reported ArM cyclopropanases obtained via targeted mutagenesis was also observed. This improved selectivity carried over to other dirhodium-catalysed transformations, including N-H, S-H and Si-H insertion, demonstrating that ArMs evolved for one reaction can serve as starting points to evolve catalysts for others.

Are voluntary wheel running and open-field behavior correlated in mice? Different answers from comparative and artificial selection approaches.

PubMed

Careau, Vincent; Bininda-Emonds, Olaf R P; Ordonez, Genesis; Garland, Theodore

2012-09-01

Voluntary wheel running and open-field behavior are probably the two most widely used measures of locomotion in laboratory rodents. We tested whether these two behaviors are correlated in mice using two approaches: the phylogenetic comparative method using inbred strains of mice and an ongoing artificial selection experiment on voluntary wheel running. After taking into account the measurement error and phylogenetic relationships among inbred strains, we obtained a significant positive correlation between distance run on wheels and distance moved in the open-field for both sexes. Thigmotaxis was negatively correlated with distance run on wheels in females but not in males. By contrast, mice from four replicate lines bred for high wheel running did not differ in either distance covered or thigmotaxis in the open field as compared with mice from four non-selected control lines. Overall, results obtained in the selection experiment were generally opposite to those observed among inbred strains. Possible reasons for this discrepancy are discussed.

Prediction of Aerosol Optical Depth in West Asia: Machine Learning Methods versus Numerical Models

NASA Astrophysics Data System (ADS)

Omid Nabavi, Seyed; Haimberger, Leopold; Abbasi, Reyhaneh; Samimi, Cyrus

2017-04-01

Dust-prone areas of West Asia are releasing increasingly large amounts of dust particles during warm months. Because of the lack of ground-based observations in the region, this phenomenon is mainly monitored through remotely sensed aerosol products. The recent development of mesoscale Numerical Models (NMs) has offered an unprecedented opportunity to predict dust emission, and, subsequently Aerosol Optical Depth (AOD), at finer spatial and temporal resolutions. Nevertheless, the significant uncertainties in input data and simulations of dust activation and transport limit the performance of numerical models in dust prediction. The presented study aims to evaluate if machine-learning algorithms (MLAs), which require much less computational expense, can yield the same or even better performance than NMs. Deep blue (DB) AOD, which is observed by satellites but also predicted by MLAs and NMs, is used for validation. We concentrate our evaluations on the over dry Iraq plains, known as the main origin of recently intensified dust storms in West Asia. Here we examine the performance of four MLAs including Linear regression Model (LM), Support Vector Machine (SVM), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS). The Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) and the Dust REgional Atmosphere Model (DREAM) are included as NMs. The MACC aerosol re-analysis of European Centre for Medium-range Weather Forecast (ECMWF) is also included, although it has assimilated satellite-based AOD data. Using the Recursive Feature Elimination (RFE) method, nine environmental features including soil moisture and temperature, NDVI, dust source function, albedo, dust uplift potential, vertical velocity, precipitation and 9-month SPEI drought index are selected for dust (AOD) modeling by MLAs. During the feature selection process, we noticed that NDVI and SPEI are of the highest importance in MLAs predictions. The data set was divided into a training (2003-2010) and a testing (2011-2013) subset. The evaluation using the two subsets shows that ANN outperformed all other MLAs and NMs. Verified to monthly mean MODIS DB AOD, ANN yielded a Spearman correlation coefficient (SCC) of 0.74, whereas SCC of 0.71 was allotted to WRF-chem simulations, as the most successful NM. In terms of simulation accuracy, SVM and MARS have yielded the lowest bias (-0.001) and RMSE (0.16). DREAM showed the poorest performance with a SCC of 0.52, a bias of -0.17 and a RMSE of 0.29.

Determination of Corrosion Rate of Artificial Bone Made of Metal at Different pH Conditions using X-Ray Radiography

NASA Astrophysics Data System (ADS)

Sutikno; Handayani, L.; Edi, S. S.; Susilo; Elvira

2018-03-01

The purpose of this research is to observe the mechanism and the rate of corrosion of artificial bone made of metal by using x-ray radiography technique. Artificial bones can be made of metallic materials and composites which are biomaterials. The most commonly used metal for bone graft is stainless steel. The interaction between artificial bone and human tissue will have important medical impacts that need to be observed and examined. This interaction can be a mechanical or chemical interaction. X-ray radiography technique is selected because it uses non-destructive method. This method is done by x-ray radiation exposure on the observed body part. The bone density and bone fracture can be seen on the resulted radiographic film or image on the monitor screen.

Flying by Ear: Blind Flight with a Music-Based Artificial Horizon

NASA Technical Reports Server (NTRS)

Simpson, Brian D.; Brungart, Douglas S.; Dallman, Ronald C.; Yasky, Richard J., Jr.; Romigh, Griffin

2008-01-01

Two experiments were conducted in actual flight operations to evaluate an audio artificial horizon display that imposed aircraft attitude information on pilot-selected music. The first experiment examined a pilot's ability to identify, with vision obscured, a change in aircraft roll or pitch, with and without the audio artificial horizon display. The results suggest that the audio horizon display improves the accuracy of attitude identification overall, but differentially affects response time across conditions. In the second experiment, subject pilots performed recoveries from displaced aircraft attitudes using either standard visual instruments, or, with vision obscured, the audio artificial horizon display. The results suggest that subjects were able to maneuver the aircraft to within its safety envelope. Overall, pilots were able to benefit from the display, suggesting that such a display could help to improve overall safety in general aviation.

A critical review and analysis of ethical issues associated with the artificial pancreas.

PubMed

Quintal, A; Messier, V; Rabasa-Lhoret, R; Racine, E

2018-04-25

The artificial pancreas combines a hormone infusion pump with a continuous glucose monitoring device, supported by a dosing algorithm currently installed on the pump. It allows for dynamic infusions of insulin (and possibly other hormones such as glucagon) tailored to patient needs. For patients with type 1 diabetes the artificial pancreas has been shown to prevent more effectively hypoglycaemic events and hyperglycaemia than insulin pump therapy and has the potential to simplify care. However, the potential ethical issues associated with the upcoming integration of the artificial pancreas into clinical practice have not yet been discussed. Our objective was to identify and articulate ethical issues associated with artificial pancreas use for patients, healthcare professionals, industry and policymakers. We performed a literature review to identify clinical, psychosocial and technical issues raised by the artificial pancreas and subsequently analysed them through a common bioethics framework. We identified five sensitive domains of ethical issues. Patient confidentiality and safety can be jeopardized by the artificial pancreas' vulnerability to security breaches or unauthorized data sharing. Public and private coverage of the artificial pancreas could be cost-effective and warranted. Patient selection criteria need to ensure equitable access and sensitivity to patient-reported outcomes. Patient coaching and support by healthcare professionals or industry representatives could help foster realistic expectations in patients. Finally, the artificial pancreas increases the visibility of diabetes and could generate issues related to personal identity and patient agency. The timely consideration of these issues will optimize the technological development and clinical uptake of the artificial pancreas. Copyright © 2018. Published by Elsevier Masson SAS.

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs

PubMed Central

Schlamp, Florencia; van der Made, Julian; Stambler, Rebecca; Chesebrough, Lewis; Boyko, Adam R.; Messer, Philipp W.

2015-01-01

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analyzed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used, and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks. PMID:26589239

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs.

PubMed

Schlamp, Florencia; van der Made, Julian; Stambler, Rebecca; Chesebrough, Lewis; Boyko, Adam R; Messer, Philipp W

2016-01-01

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analysed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks. © 2015 John Wiley & Sons Ltd.

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

Quantitative impact of thymic selection on Foxp3+ and Foxp3- subsets of self-peptide/MHC class II-specific CD4+ T cells.

PubMed

Moon, James J; Dash, Pradyot; Oguin, Thomas H; McClaren, Jennifer L; Chu, H Hamlet; Thomas, Paul G; Jenkins, Marc K

2011-08-30

It is currently thought that T cells with specificity for self-peptide/MHC (pMHC) ligands are deleted during thymic development, thereby preventing autoimmunity. In the case of CD4(+) T cells, what is unclear is the extent to which self-peptide/MHC class II (pMHCII)-specific T cells are deleted or become Foxp3(+) regulatory T cells. We addressed this issue by characterizing a natural polyclonal pMHCII-specific CD4(+) T-cell population in mice that either lacked or expressed the relevant antigen in a ubiquitous pattern. Mice expressing the antigen contained one-third the number of pMHCII-specific T cells as mice lacking the antigen, and the remaining cells exhibited low TCR avidity. In mice lacking the antigen, the pMHCII-specific T-cell population was dominated by phenotypically naive Foxp3(-) cells, but also contained a subset of Foxp3(+) regulatory cells. Both Foxp3(-) and Foxp3(+) pMHCII-specific T-cell numbers were reduced in mice expressing the antigen, but the Foxp3(+) subset was more resistant to changes in number and TCR repertoire. Therefore, thymic selection of self-pMHCII-specific CD4(+) T cells results in incomplete deletion within the normal polyclonal repertoire, especially among regulatory T cells.

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.

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.

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.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-01-01

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R 2 =0.82), but it was not robust in extrapolation (R 2 =0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

A Compact Optical Instrument with Artificial Neural Network for pH Determination

PubMed Central

Capel-Cuevas, Sonia; López-Ruiz, Nuria; Martinez-Olmos, Antonio; Cuéllar, Manuel P.; Pegalajar, Maria del Carmen; Palma, Alberto José; de Orbe-Payá, Ignacio; Capitán-Vallvey, Luis Fermin

2012-01-01

The aim of this work was the determination of pH with a sensor array-based optical portable instrument. This sensor array consists of eleven membranes with selective colour changes at different pH intervals. The method for the pH calculation is based on the implementation of artificial neural networks that use the responses of the membranes to generate a final pH value. A multi-objective algorithm was used to select the minimum number of sensing elements required to achieve an accurate pH determination from the neural network, and also to minimise the network size. This helps to minimise instrument and array development costs and save on microprocessor energy consumption. A set of artificial neural networks that fulfils these requirements is proposed using different combinations of the membranes in the sensor array, and is evaluated in terms of accuracy and reliability. In the end, the network including the response of the eleven membranes in the sensor was selected for validation in the instrument prototype because of its high accuracy. The performance of the instrument was evaluated by measuring the pH of a large set of real samples, showing that high precision can be obtained in the full range. PMID:22778668

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.

Elimination of a genetic correlation between the sexes via artificial correlational selection.

PubMed

Delph, Lynda F; Steven, Janet C; Anderson, Ingrid A; Herlihy, Christopher R; Brodie, Edmund D

2011-10-01

Genetic correlations between the sexes can constrain the evolution of sexual dimorphism and be difficult to alter, because traits common to both sexes share the same genetic underpinnings. We tested whether artificial correlational selection favoring specific combinations of male and female traits within families could change the strength of a very high between-sex genetic correlation for flower size in the dioecious plant Silene latifolia. This novel selection dramatically reduced the correlation in two of three selection lines in fewer than five generations. Subsequent selection only on females in a line characterized by a lower between-sex genetic correlation led to a significantly lower correlated response in males, confirming the potential evolutionary impact of the reduced correlation. Although between-sex genetic correlations can potentially constrain the evolution of sexual dimorphism, our findings reveal that these constraints come not from a simple conflict between an inflexible genetic architecture and a pattern of selection working in opposition to it, but rather a complex relationship between a changeable correlation and a form of selection that promotes it. In other words, the form of selection on males and females that leads to sexual dimorphism may also promote the genetic phenomenon that limits sexual dimorphism. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

Evolution of brain region volumes during artificial selection for relative brain size.

PubMed

Kotrschal, Alexander; Zeng, Hong-Li; van der Bijl, Wouter; Öhman-Mägi, Caroline; Kotrschal, Kurt; Pelckmans, Kristiaan; Kolm, Niclas

2017-12-01

The vertebrate brain shows an extremely conserved layout across taxa. Still, the relative sizes of separate brain regions vary markedly between species. One interesting pattern is that larger brains seem associated with increased relative sizes only of certain brain regions, for instance telencephalon and cerebellum. Till now, the evolutionary association between separate brain regions and overall brain size is based on comparative evidence and remains experimentally untested. Here, we test the evolutionary response of brain regions to directional selection on brain size in guppies (Poecilia reticulata) selected for large and small relative brain size. In these animals, artificial selection led to a fast response in relative brain size, while body size remained unchanged. We use microcomputer tomography to investigate how the volumes of 11 main brain regions respond to selection for larger versus smaller brains. We found no differences in relative brain region volumes between large- and small-brained animals and only minor sex-specific variation. Also, selection did not change allometric scaling between brain and brain region sizes. Our results suggest that brain regions respond similarly to strong directional selection on relative brain size, which indicates that brain anatomy variation in contemporary species most likely stem from direct selection on key regions. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Metabolomics differences between silkworms (Bombyx mori) reared on fresh mulberry (Morus) leaves or artificial diets.

PubMed

Dong, Hui-Ling; Zhang, Sheng-Xiang; Tao, Hui; Chen, Zhuo-Hua; Li, Xue; Qiu, Jian-Feng; Cui, Wen-Zhao; Sima, Yang-Hu; Cui, Wei-Zheng; Xu, Shi-Qing

2017-09-08

Silkworms (Bombyx mori) reared on artificial diets have great potential applications in sericulture. However, the mechanisms underlying the enhancement of metabolic utilization by altering silkworm nutrition are unclear. The aim of this study was to investigate the mechanisms responsible for the poor development and low silk protein synthesis efficiency of silkworms fed artificial diets. After multi-generational selection of the ingestive behavior of silkworms to artificial diets, we obtained two strains, one of which developed well and another in which almost all its larvae starved to death on the artificial diets. Subsequently, we analyzed the metabolomics of larval hemolymph by gas chromatography/liquid chromatography-mass spectrometry, and the results showed that vitamins were in critically short supply, whereas the nitrogen metabolic end product of urea and uric acid were enriched substantially, in the hemolymph of the silkworms reared on the artificial diets. Meanwhile, amino acid metabolic disorders, as well as downregulation of carbohydrate metabolism, energy metabolism, and lipid metabolism, co-occurred. Furthermore, 10 male-dominant metabolites and 27 diet-related metabolites that differed between male and female silkworms were identified. These findings provide important insights into the regulation of silkworm metabolism and silk protein synthesis when silkworms adapt to an artificial diet.

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.

Natural and Artificial Satellite Dynamics and Evolution around Near-Earth Asteroids with Solar Radiation Pressure

NASA Astrophysics Data System (ADS)

Rieger, Samantha M.

Natural and artificial satellites are subject to perturbations when orbiting near-Earth asteroids. These perturbations include non-uniform gravity from the asteroid, third-body disturbances from the Sun, and solar radiation pressure. For small natural (1 cm-15 m) and artificial satellites, solar radiation pressure is the primary perturbation that will cause their orbits to go unstable. For the asteroid Bennu, the future target of the spacecraft OSIRIS-REx, the possibility of natural satellites having stable orbits around the asteroid and characterize these stable regions is investigated. It has been found that the main orbital phenomena responsible for the stability or instability of these possible natural satellites are Sun-synchronous orbits, the modified Laplace plane, and the Kozai resonance. These findings are applied to other asteroids as well as to artificial satellites. The re-emission of solar radiation pressure through BYORP is also investigated for binary asteroid systems. Specifically, the BYORP force is combined with the Laplace plane such that BYORP expands the orbit of the binary system along the Laplace surface where the secondary increases in inclination. For obliquities from 68.875° - 111.125° the binary will eventually extend into the Laplace instability region, where the eccentricity of the orbit will increase. A subset of the instability region leads to eccentricities high enough that the secondary will impact the primary. This result inspired the development of a hypothesis of a contact-binary binary cycle described briefly in the following. YORP will increase the spin rate of a contact binary while also driving the spin-pole to an obliquity of 90°. Eventually, the contact binary will fission. The binary will subsequently become double-synchronous, thus allowing the BYORP acceleration to have secular effects on the orbit. The orbit will then expand along the Laplace surface to the Laplace plane instability region eventually leading to an impact and the start of a new cycle with the YORP process.

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

Predicting tool life in turning operations using neural networks and image processing

NASA Astrophysics Data System (ADS)

Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.

2018-05-01

A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

CHIPMUNK: A Virtual Synthesizable Small-Molecule Library for Medicinal Chemistry, Exploitable for Protein-Protein Interaction Modulators.

PubMed

Humbeck, Lina; Weigang, Sebastian; Schäfer, Till; Mutzel, Petra; Koch, Oliver

2018-03-20

A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein-protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Selective Photothermolysis to target Sebaceous Glands: Theoretical Estimation of Parameters and Preliminary Results Using a Free Electron Laser

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

Fernanda Sakamoto, Apostolos Doukas, William Farinelli, Zeina Tannous, Michelle D. Shinn, Stephen Benson, Gwyn P. Williams, H. Dylla, Richard Anderson

2011-12-01

The success of permanent laser hair removal suggests that selective photothermolysis (SP) of sebaceous glands, another part of hair follicles, may also have merit. About 30% of sebum consists of fats with copious CH2 bond content. SP was studied in vitro, using free electron laser (FEL) pulses at an infrared CH2 vibrational absorption wavelength band. Absorption spectra of natural and artificially prepared sebum were measured from 200 nm to 3000 nm, to determine wavelengths potentially able to target sebaceous glands. The Jefferson National Accelerator superconducting FEL was used to measure photothermal excitation of aqueous gels, artificial sebum, pig skin, humanmore » scalp and forehead skin (sebaceous sites). In vitro skin samples were exposed to FEL pulses from 1620 to 1720 nm, spot diameter 7-9.5 mm with exposure through a cold 4C sapphire window in contact with the skin. Exposed and control tissue samples were stained using H and E, and nitroblue tetrazolium chloride staining (NBTC) was used to detect thermal denaturation. Natural and artificial sebum both had absorption peaks near 1210, 1728, 1760, 2306 and 2346 nm. Laser-induced heating of artificial sebum was approximately twice that of water at 1710 and 1720 nm, and about 1.5x higher in human sebaceous glands than in water. Thermal camera imaging showed transient focal heating near sebaceous hair follicles. Histologically, skin samples exposed to {approx}1700 nm, {approx}100-125 ms pulses showed evidence of selective thermal damage to sebaceous glands. Sebaceous glands were positive for NBTC staining, without evidence of selective loss in samples exposed to the laser. Epidermis was undamaged in all samples. Conclusions: SP of sebaceous glands appears to be feasible. Potentially, optical pulses at {approx}1720 nm or {approx}1210 nm delivered with large beam diameter and appropriate skin cooling in approximately 0.1 s may provide an alternative treatment for acne.« less

Artificial genetic selection for an efficient translation initiation site for expression of human RACK1 gene in Escherichia coli

PubMed Central

Zhelyabovskaya, Olga B.; Berlin, Yuri A.; Birikh, Klara R.

2004-01-01

In bacterial expression systems, translation initiation is usually the rate limiting and the least predictable stage of protein synthesis. Efficiency of a translation initiation site can vary dramatically depending on the sequence context. This is why many standard expression vectors provide very poor expression levels of some genes. This notion persuaded us to develop an artificial genetic selection protocol, which allows one to find for a given target gene an individual efficient ribosome binding site from a random pool. In order to create Darwinian pressure necessary for the genetic selection, we designed a system based on translational coupling, in which microorganism survival in the presence of antibiotic depends on expression of the target gene, while putting no special requirements on this gene. Using this system we obtained superproducing constructs for the human protein RACK1 (receptor for activated C kinase). PMID:15034151

Modeling selective attention using a neuromorphic analog VLSI device.

PubMed

Indiveri, G

2000-12-01

Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability.

PubMed

Ahn, Jae Joon; Kim, Young Min; Yoo, Keunje; Park, Joonhong; Oh, Kyong Joo

2012-11-01

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.

Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

NASA Technical Reports Server (NTRS)

Krishnan, G. S.

1997-01-01

A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

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…

Red-cockaded woodpecker nest-cavity selection: relationships with cavity age and resin production

Treesearch

Richard N. Conner; Daniel Saenz; D. Craig Rudolph; William G. Ross; David L. Kulhavy

1998-01-01

The authors evaluated selection of nest sites by male red-cockaded woodpeckers (Picoides borealis) in Texas relative to the age of the cavity when only cavities excavated by the woodpeckers were available and when both naturally excavated cavities and artificial cavities were available. They also evaluated nest-cavity selection relative to the ability of naturally...

Economic indicators selection for crime rates forecasting using cooperative feature selection

NASA Astrophysics Data System (ADS)

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

2013-04-01

Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

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

Selective attention and control of action: comparative psychology of an artificial, evolved agent and people.

PubMed

Ward, Robert; Ward, Ronnie

2008-10-01

This study examined the selective attention abilities of a simple, artificial, evolved agent and considered implications of the agent's performance for theories of selective attention and action. The agent processed two targets in continuous time, catching one and then the other. This task required many cognitive operations, including prioritizing the first target (T1) over the second (T2); selectively focusing responses on T1, while preventing T2 from interfering with responses; creating a memory for the unselected T2 item, so that it could be efficiently processed later; and reallocating processing towards T2 after catching T1. The evolved agent demonstrated all these abilities. Analysis shows that the agent used reactive inhibition to selectively focus behavior. That is, the more salient T2, the more strongly responses towards T2 were inhibited and the slower the agent was to subsequently reallocate processing towards T2. Reactive inhibition was also suggested in two experiments with people, performing a virtually identical catch task. The presence of reactive inhibition in the simple agent and in people suggests that it is an important mechanism for selective processing.

Differences in sorption behavior of the herbicide 4-chloro-2-methylphenoxyacetic acid on artificial soils as a function of soil pre-aging.

PubMed

Waldner, Georg; Friesl-Hanl, Wolfgang; Haberhauer, Georg; Gerzabek, Martin H

The sorption behavior of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) to three different artificial soil mixtures was investigated. Artificial soils serve as model systems for improving understanding of sorption phenomena. The soils consisted of quartz, ferrihydrite, illite, montmorillonite, and charcoal. In a previous study, several selected mixtures had been inoculated with organic matter, and microbial aging (incubation) had been performed for different periods of time (3, 12, and 18 months) before conducting the sorption experiments. The effect of this pre-incubation time on the sorption behavior was determined. Interaction of MCPA with soil surfaces was monitored by aqueous phase sorption experiments, using high-performance liquid chromatography/ultraviolet and in selected cases Fourier-transformed infrared spectroscopy. The sorption behavior showed large differences between differently aged soils; Freundlich and linear sorption model fits (with sorption constants K f , 1/ n exponents, and K d values, respectively) were given for pH = 3 and the unbuffered pH of ∼7. The largest extent of sorption from diluted solutions was found on the surfaces with a pre-incubation time of 3 months. Sorption increased at acidic pH values. Regarding the influence of aging of artificial soils, the following conclusions were drawn: young artificial soils exhibit stronger sorption at lower concentrations, with a larger K f value than aged soils. A correlation with organic carbon content was not confirmed. Thus, the sorption characteristics of the soils are more influenced by the aging of the organic carbon than by the organic carbon content itself.

Brazilian and Mexican experiences in the study of incipient domestication.

PubMed

Lins Neto, Ernani Machado de Freitas; Peroni, Nivaldo; Casas, Alejandro; Parra, Fabiola; Aguirre, Xitlali; Guillén, Susana; Albuquerque, Ulysses Paulino

2014-04-02

Studies of domestication enables a better understanding of human cultures, landscape changes according to peoples' purposes, and evolutionary consequences of human actions on biodiversity. This review aimed at discussing concepts, hypotheses, and current trends in studies of domestication of plants, using examples of cases studied in regions of Mesoamerica and Brazil. We analyzed trends of ethnobiological studies contributing to document processes of domestication and to establish criteria for biodiversity conservation based on traditional ecological knowledge. Based on reviewing our own and other authors' studies we analyzed management patterns and evolutionary trends associated to domestication occurring at plant populations and landscape levels. Particularly, we systematized information documenting: ethnobotanical aspects about plant management and artificial selection mechanisms, morphological consequences of plant management, population genetics of wild and managed plant populations, trends of change in reproduction systems of plants associated to management, and other ecological and physiological aspects influenced by management and domestication. Based on the analysis of study cases of 20 native species of herbs, shrubs and trees we identified similar criteria of artificial selection in different cultural contexts of Mexico and Brazil. Similar evolutionary trends were also identified in morphology (selection in favor of gigantism of useful and correlated parts); organoleptic characteristics such as taste, toxicity, color, texture; reproductive biology, mainly breeding system, phenological changes, and population genetics aspects, maintenance or increasing of genetic diversity in managed populations, high gene flow with wild relatives and low structure maintained by artificial selection. Our review is a first attempt to unify research methods for analyzing a high diversity of processes. Further research should emphasize deeper analyses of contrasting and diverse cultural and ecological contexts for a better understanding of evolution under incipient processes of domestication. Higher research effort is particularly required in Brazil, where studies on this topic are scarcer than in Mexico but where diversity of human cultures managing their also high plant resources diversity offer high potential for documenting the diversity of mechanisms of artificial selection and evolutionary trends. Comparisons and evaluations of incipient domestication in the regions studied as well as the Andean area would significantly contribute to understanding origins and diffusion of the experience of managing and domesticating plants.

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

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

Maclisp extensions

NASA Technical Reports Server (NTRS)

Bawden, A.; Burke, G. S.; Hoffman, C. W.

1981-01-01

A common subset of selected facilities available in Maclisp and its derivatives (PDP-10 and Multics Maclisp, Lisp Machine Lisp (Zetalisp), and NIL) is decribed. The object is to add in writing code which can run compatibly in more than one of these environments.

Designing basin-customized combined drought indices via feature extraction

NASA Astrophysics Data System (ADS)

Zaniolo, Marta; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

The socio-economic costs of drought are progressively increasing worldwide 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 how to combine them, we use an advanced feature extraction algorithm called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS). The W-QEISS algorithm 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 (cardinality) and optimizing relevance and redundancy of the subset. The accuracy objective is evaluated trough the calibration of a pre-defined model (i.e., 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 proposed methodology is tested in the case study of Lake Como in northern Italy, a regulated lake mainly operated for irrigation supply to four downstream agricultural districts. 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 the most 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 framework succeeds in constructing a combined drought index that reproduces the soil moisture deficit. Moreover, this 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.

Artificial immune system algorithm in VLSI circuit configuration

NASA Astrophysics Data System (ADS)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

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.

Immune Reactions against Gene Gun Vaccines Are Differentially Modulated by Distinct Dendritic Cell Subsets in the Skin

PubMed Central

Deressa, Tekalign; Strandt, Helen; Florindo Pinheiro, Douglas; Mittermair, Roberta; Pizarro Pesado, Jennifer; Thalhamer, Josef; Hammerl, Peter; Stoecklinger, Angelika

2015-01-01

The skin accommodates multiple dendritic cell (DC) subsets with remarkable functional diversity. Immune reactions are initiated and modulated by the triggering of DC by pathogen-associated or endogenous danger signals. In contrast to these processes, the influence of intrinsic features of protein antigens on the strength and type of immune responses is much less understood. Therefore, we investigated the involvement of distinct DC subsets in immune reactions against two structurally different model antigens, E. coli beta-galactosidase (betaGal) and chicken ovalbumin (OVA) under otherwise identical conditions. After epicutaneous administration of the respective DNA vaccines with a gene gun, wild type mice induced robust immune responses against both antigens. However, ablation of langerin+ DC almost abolished IgG1 and cytotoxic T lymphocytes against betaGal but enhanced T cell and antibody responses against OVA. We identified epidermal Langerhans cells (LC) as the subset responsible for the suppression of anti-OVA reactions and found regulatory T cells critically involved in this process. In contrast, reactions against betaGal were not affected by the selective elimination of LC, indicating that this antigen required a different langerin+ DC subset. The opposing findings obtained with OVA and betaGal vaccines were not due to immune-modulating activities of either the plasmid DNA or the antigen gene products, nor did the differential cellular localization, size or dose of the two proteins account for the opposite effects. Thus, skin-borne protein antigens may be differentially handled by distinct DC subsets, and, in this way, intrinsic features of the antigen can participate in immune modulation. PMID:26030383

Existence of CD8α-like dendritic cells with a conserved functional specialization and a common molecular signature in distant mammalian species.

PubMed

Contreras, Vanessa; Urien, Céline; Guiton, Rachel; Alexandre, Yannick; Vu Manh, Thien-Phong; Andrieu, Thibault; Crozat, Karine; Jouneau, Luc; Bertho, Nicolas; Epardaud, Mathieu; Hope, Jayne; Savina, Ariel; Amigorena, Sebastian; Bonneau, Michel; Dalod, Marc; Schwartz-Cornil, Isabelle

2010-09-15

The mouse lymphoid organ-resident CD8alpha(+) dendritic cell (DC) subset is specialized in Ag presentation to CD8(+) T cells. Recent evidence shows that mouse nonlymphoid tissue CD103(+) DCs and human blood DC Ag 3(+) DCs share similarities with CD8alpha(+) DCs. We address here whether the organization of DC subsets is conserved across mammals in terms of gene expression signatures, phenotypic characteristics, and functional specialization, independently of the tissue of origin. We study the DC subsets that migrate from the skin in the ovine species that, like all domestic animals, belongs to the Laurasiatheria, a distinct phylogenetic clade from the supraprimates (human/mouse). We demonstrate that the minor sheep CD26(+) skin lymph DC subset shares significant transcriptomic similarities with mouse CD8alpha(+) and human blood DC Ag 3(+) DCs. This allowed the identification of a common set of phenotypic characteristics for CD8alpha-like DCs in the three mammalian species (i.e., SIRP(lo), CADM1(hi), CLEC9A(hi), CD205(hi), XCR1(hi)). Compared to CD26(-) DCs, the sheep CD26(+) DCs show 1) potent stimulation of allogeneic naive CD8(+) T cells with high selective induction of the Ifngamma and Il22 genes; 2) dominant efficacy in activating specific CD8(+) T cells against exogenous soluble Ag; and 3) selective expression of functional pathways associated with high capacity for Ag cross-presentation. Our results unravel a unifying definition of the CD8alpha(+)-like DCs across mammalian species and identify molecular candidates that could be used for the design of vaccines applying to mammals in general.

Minimizing the average distance to a closest leaf in a phylogenetic tree.

PubMed

Matsen, Frederick A; Gallagher, Aaron; McCoy, Connor O

2013-11-01

When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this article, we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Around Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, whereas PAM only gives a solution for the prespecified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, whereas the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.

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.

A Genome-Wide Scan for Evidence of Selection in a Maize Population Under Long-Term Artificial Selection for Ear Number

PubMed Central

Beissinger, Timothy M.; Hirsch, Candice N.; Vaillancourt, Brieanne; Deshpande, Shweta; Barry, Kerrie; Buell, C. Robin; Kaeppler, Shawn M.; Gianola, Daniel; de Leon, Natalia

2014-01-01

A genome-wide scan to detect evidence of selection was conducted in the Golden Glow maize long-term selection population. The population had been subjected to selection for increased number of ears per plant for 30 generations, with an empirically estimated effective population size ranging from 384 to 667 individuals and an increase of more than threefold in the number of ears per plant. Allele frequencies at >1.2 million single-nucleotide polymorphism loci were estimated from pooled whole-genome resequencing data, and FST values across sliding windows were employed to assess divergence between the population preselection and the population postselection. Twenty-eight highly divergent regions were identified, with half of these regions providing gene-level resolution on potentially selected variants. Approximately 93% of the divergent regions do not demonstrate a significant decrease in heterozygosity, which suggests that they are not approaching fixation. Also, most regions display a pattern consistent with a soft-sweep model as opposed to a hard-sweep model, suggesting that selection mostly operated on standing genetic variation. For at least 25% of the regions, results suggest that selection operated on variants located outside of currently annotated coding regions. These results provide insights into the underlying genetic effects of long-term artificial selection and identification of putative genetic elements underlying number of ears per plant in maize. PMID:24381334

Complex network structure of musical compositions: Algorithmic generation of appealing music

NASA Astrophysics Data System (ADS)

Liu, Xiao Fan; Tse, Chi K.; Small, Michael

2010-01-01

In this paper we construct networks for music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurring connections. We analyze classical music from Bach, Mozart, Chopin, as well as other types of music such as Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. We conjecture that preserving the universal network properties is a necessary step in artificial composition of music. Power-law exponents of node degree, node strength and/or edge weight distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be composed artificially using a controlled random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. By generating a large number of compositions, we find that this algorithm generates music which has the necessary qualities to be subjectively judged as appealing.

Conservation of soil, water and nutrients in surface runoff using riparian plant species.

PubMed

Srivastava, Prabodh; Singh, Shipra

2012-01-01

Three riparian plant species viz. Cynodon dactylon (L.) Pers., Saccharum bengalensis Retz. and Parthenium hysterophorus L. were selected from the riparian zone of Kali river at Aligarh to conduct the surface runoff experiment to compare their conservation efficiencies for soil, water and nutrients (phosphorus and nitrogen). Experimental plots were prepared on artificial slopes in botanical garden and on natural slopes on study site. Selected riparian plant species showed the range of conservation values for soil and water from 47.11 to 95.22% and 44.06 to 72.50%, respectively on artificial slope and from 44.53 to 95.33% and 48.36 to 73.15%, respectively on natural slope. Conservation values for phosphorus and nitrogen ranged from 40.83 to 88.89% and 59.78 to 82.22%, respectively on artificial slope and from 50.01 to 90.16% and 68.07 to 85.62%, respectively on natural slope. It was observed that Cynodon dactylon was the most efficient riparian species in conservation of soil, water and nutrients in surface runoff.

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

PubMed

Noorizadeh, Hadi; Farmany, Abbas; Narimani, Hojat; Noorizadeh, Mehrab

2013-05-01

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.

Modeling of lighting behaviour of a hybrid lighting system in inner spaces of Building of Electrical Engineering

NASA Astrophysics Data System (ADS)

Amado, L.; Osma, G.; Villamizar, R.

2016-07-01

This paper presents the modelling of lighting behaviour of a hybrid lighting system - HLS in inner spaces for tropical climate. HLS aims to mitigate the problem of high electricity consumption used by artificial lighting in buildings. These systems integrate intelligently the daylight and artificial light through control strategies. However, selection of these strategies usually depends on expertise of designer and of available budget. In order to improve the selection process of the control strategies, this paper analyses the Electrical Engineering Building (EEB) case, initially modelling of lighting behaviour is established for the HLS of a classroom and an office. This allows estimating the illuminance level of the mixed lighting in the space, and energy consumption by artificial light according to different lighting control techniques, a control strategy based on occupancy and a combination of them. The model considers the concept of Daylight Factor (DF) for the estimating of daylight illuminance on the work plane for tropical climatic conditions. The validation of the model was carried out by comparing the measured and model-estimated indoor illuminances.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

MISR Regional SAMUM Imagery Overview

Atmospheric Science Data Center

2016-08-24

... View Data  |  Download Data About this Web Site: Visualizations of select MISR Level 3 data for special regional ... regional version used in support of the SAMUM Campaign. More information about the Level 1 and Level 2 products subsetted for the SAMUM ...

MISR Regional VBBE Imagery Overview

Atmospheric Science Data Center

2016-08-24

... View Data  |  Download Data About this Web Site: Visualizations of select MISR Level 3 data for special regional ... regional version used in support of the VBBE Campaign. More information about the Level 1 and Level 2 products subsetted for the VBBE ...