Showing papers on "Selection (genetic algorithm) published in 2015"

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

Abstract: 3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Less is More: an Adaptive Branch-Site Random Effects Model for Efficient Detection of Episodic Diversifying Selection

Abstract: Over the past two decades, comparative sequence analysis using codon-substitution models has been honed into a powerful and popular approach for detecting signatures of natural selection from molecular data. A substantial body of work has focused on developing a class of “branch-site” models which permit selective pressures on sequences, quantified by the ω ratio, to vary among both codon sites and individual branches in the phylogeny. We develop and present a method in this class, adaptive branch-site random effects likelihood (aBSREL), whose key innovation is variable parametric complexity chosen with an information theoretic criterion. By applying models of different complexity to different branches in the phylogeny, aBSREL delivers statistical performance matching or exceeding best-in-class existing approaches, while running an order of magnitude faster. Based on simulated data analysis, we offer guidelines for what extent and strength of diversifying positive selection can be detected reliably and suggest that there is a natural limit on the optimal parametric complexity for “branch-site” models. An aBSREL analysis of 8,893 Euteleostomes gene alignments demonstrates that over 80% of branches in typical gene phylogenies can be adequately modeled with a single ω ratio model, that is, current models are unnecessarily complicated. However, there are a relatively small number of key branches, whose identities are derived from the data using a model selection procedure, for which it is essential to accurately model evolutionary complexity.

RELAX: Detecting Relaxed Selection in a Phylogenetic Framework

Abstract: Relaxation of selective strength, manifested as a reduction in the efficiency or intensity of natural selection, can drive evolutionary innovation and presage lineage extinction or loss of function. Mechanisms through which selection can be relaxed range from the removal of an existing selective constraint to a reduction in effective population size. Standard methods for estimating the strength and extent of purifying or positive selection from molecular sequence data are not suitable for detecting relaxed selection, because they lack power and can mistake an increase in the intensity of positive selection for relaxation of both purifying and positive selection. Here, we present a general hypothesis testing framework (RELAX) for detecting relaxed selection in a codon-based phylogenetic framework. Given two subsets of branches in a phylogeny, RELAX can determine whether selective strength was relaxed or intensified in one of these subsets relative to the other. We establish the validity of our test via simulations and show that it can distinguish between increased positive selection and a relaxation of selective strength. We also demonstrate the power of RELAX in a variety of biological scenarios where relaxation of selection has been hypothesized or demonstrated previously. We find that obligate and facultative γ-proteobacteria endosymbionts of insects are under relaxed selection compared with their free-living relatives and obligate endosymbionts are under relaxed selection compared with facultative endosymbionts. Selective strength is also relaxed in asexual Daphnia pulex lineages, compared with sexual lineages. Endogenous, nonfunctional, bornavirus-like elements are found to be under relaxed selection compared with exogenous Borna viruses. Finally, selection on the short-wavelength sensitive, SWS1, opsin genes in echolocating and nonecholocating bats is relaxed only in lineages in which this gene underwent pseudogenization; however, selection on the functional medium/long-wavelength sensitive opsin, M/LWS1, is found to be relaxed in all echolocating bats compared with nonecholocating bats.

Gene-Wide Identification of Episodic Selection

Abstract: We present BUSTED, a new approach to identifying gene-wide evidence of episodic positive selection, where the non-synonymous substitution rate is transiently greater than the synonymous rate. BUSTED can be used either on an entire phylogeny (without requiring an a priori hypothesis regarding which branches are under positive selection) or on a pre-specified subset of foreground lineages (if a suitable a priori hypothesis is available). Selection is modeled as varying stochastically over branches and sites, and we propose a computationally inexpensive evidence metric for identifying sites subject to episodic positive selection on any foreground branches. We compare BUSTED with existing models on simulated and empirical data. An implementation is available on www.datamonkey.org/busted, with a widget allowing the interactive specification of foreground branches.

Reliable Detection of Loci Responsible for Local Adaptation: Inference of a Null Model through Trimming the Distribution of FST*

Abstract: Loci responsible for local adaptation are likely to have more genetic differentiation among populations than neutral loci. However, neutral loci can vary widely in their amount of genetic differentiation, even over the same geographic range. Unfortunately, the distribution of differentiation—as measured by an index such as FST—depends on the details of the demographic history of the populations in question, even without spatially heterogeneous selection. Many methods designed to detect FST outliers assume a specific model of demographic history, which can result in extremely high false positive rates for detecting loci under selection. We develop a new method that infers the distribution of FST for loci unlikely to be strongly affected by spatially diversifying selection, using data on a large set of loci with unknown selective properties. Compared to previous methods, this approach, called OutFLANK, has much lower false positive rates and comparable power, as shown by simulation.

Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation.

Abstract: Biological invasions are 'natural' experiments that can improve our understanding of contemporary evolution. We evaluate evidence for population differentiation, natural selection and adaptive evolution of invading plants and animals at two nested spatial scales: (i) among introduced populations (ii) between native and introduced genotypes. Evolution during invasion is frequently inferred, but rarely confirmed as adaptive. In common garden studies, quantitative trait differentiation is only marginally lower (~3.5%) among introduced relative to native populations, despite genetic bottlenecks and shorter timescales (i.e. millennia vs. decades). However, differentiation between genotypes from the native vs. introduced range is less clear and confounded by nonrandom geographic sampling; simulations suggest this causes a high false-positive discovery rate (>50%) in geographically structured populations. Selection differentials (¦s¦) are stronger in introduced than in native species, although selection gradients (¦β¦) are not, consistent with introduced species experiencing weaker genetic constraints. This could facilitate rapid adaptation, but evidence is limited. For example, rapid phenotypic evolution often manifests as geographical clines, but simulations demonstrate that nonadaptive trait clines can evolve frequently during colonization (~two-thirds of simulations). Additionally, QST-FST studies may often misrepresent the strength and form of natural selection acting during invasion. Instead, classic approaches in evolutionary ecology (e.g. selection analysis, reciprocal transplant, artificial selection) are necessary to determine the frequency of adaptive evolution during invasion and its influence on establishment, spread and impact of invasive species. These studies are rare but crucial for managing biological invasions in the context of global change.

SmileFinder: A Resampling-Based Approach to Evaluate Signatures of Selection from Genome-Wide Sets of Matching Allele Frequency Data in Two or More Diploid Populations

Abstract: Background Adaptive alleles may rise in frequency as a consequence of positive selection, creating a pattern of decreased variation in the neighboring loci, known as a selective sweep. When the region containing this pattern is compared to another population with no history of selection, a rise in variance of allele frequencies between populations is observed. One challenge presented by large genome-wide datasets is the ability to differentiate between patterns that are remnants of natural selection from those expected to arise at random and/or as a consequence of selectively neutral demographic forces acting in the population.

Perspectives for Genomic Selection Applications and Research in Plants

Abstract: Genomic selection (GS) has created a lot of excitement and expectations in the animal- and plant-breeding research communities. In this review, we briefly describe how genomic pre-diction can be integrated into breeding efforts and point out achievements and areas where more research is needed. Genomic selec-tion provides many opportunities to increase genetic gain in plant breeding per unit time and cost. Early empirical and simulation results are promising, but for GS to deliver genetic gains, careful consideration of the problem of optimal resource allocation is needed. Consideration of the cost-benefit balance of using markers for each trait and stage of the breeding cycle is needed, moving beyond only focusing on recur -rent selection with GS on a few complex traits, using prediction on unphenotyped individuals. With decreasing marker cost, phenotype data is quickly becoming the most valuable asset and marker-assisted selection strategies should focus on making the most of scarce and expen -sive phenotypes. It is important to realize that markers can also improve accuracy of selection for phenotyped individuals. Use of markers as an aid to phenotype analysis suggests a num-ber of new strategies in terms of experimental design and multi-trait models. GS also provides new ways to analyze and deal with genotype by environment interactions. Lastly, we point to some recent results showing that new models are needed to improve predictions particularly with respect to the use of distantly related indi-viduals in the training population.N. Heslot, J.-L. Jannink, and M.E. Sorrells, Cornell Univ., Dep. of Plant Breeding and Genetics, 240 Emerson Hall, Ithaca, NY 14853. J.-L. Jannink, USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell Univ., Ithaca, NY 14853. N. Heslot, Limagrain Europe, CS3911, Chappes, 63720 France. Received 27 Mar. 2014. *Corresponding author (mes12@cornell.edu).

Training set optimization under population structure in genomic selection.

Abstract: Key message Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance.

Natural selection constrains neutral diversity across a wide range of species.

Abstract: The neutral theory of molecular evolution predicts that the amount of neutral polymorphisms within a species will increase proportionally with the census population size (Nc). However, this prediction has not been borne out in practice: while the range of Nc spans many orders of magnitude, levels of genetic diversity within species fall in a comparatively narrow range. Although theoretical arguments have invoked the increased efficacy of natural selection in larger populations to explain this discrepancy, few direct empirical tests of this hypothesis have been conducted. In this work, we provide a direct test of this hypothesis using population genomic data from a wide range of taxonomically diverse species. To do this, we relied on the fact that the impact of natural selection on linked neutral diversity depends on the local recombinational environment. In regions of relatively low recombination, selected variants affect more neutral sites through linkage, and the resulting correlation between recombination and polymorphism allows a quantitative assessment of the magnitude of the impact of selection on linked neutral diversity. By comparing whole genome polymorphism data and genetic maps using a coalescent modeling framework, we estimate the degree to which natural selection reduces linked neutral diversity for 40 species of obligately sexual eukaryotes. We then show that the magnitude of the impact of natural selection is positively correlated with Nc, based on body size and species range as proxies for census population size. These results demonstrate that natural selection removes more variation at linked neutral sites in species with large Nc than those with small Nc and provides direct empirical evidence that natural selection constrains levels of neutral genetic diversity across many species. This implies that natural selection may provide an explanation for this longstanding paradox of population genetics.

Relay-Selection Improves the Security-Reliability Trade-Off in Cognitive Radio Systems

Abstract: We consider a cognitive radio (CR) network consisting of a secondary transmitter (ST), a secondary destination (SD) and multiple secondary relays (SRs) in the presence of an eavesdropper, where the ST transmits to the SD with the assistance of SRs, while the eavesdropper attempts to intercept the secondary transmission. We rely on careful relay selection for protecting the ST-SD transmission against the eavesdropper with the aid of both single-relay and multi-relay selection. To be specific, only the “best” SR is chosen in the single-relay selection for assisting the secondary transmission, whereas the multi-relay selection invokes multiple SRs for simultaneously forwarding the ST's transmission to the SD. We analyze both the intercept probability and outage probability of the proposed single-relay and multi-relay selection schemes for the secondary transmission relying on realistic spectrum sensing. We also evaluate the performance of classic direct transmission and artificial noise based methods for the purpose of comparison with the proposed relay selection schemes. It is shown that as the intercept probability requirement is relaxed, the outage performance of the direct transmission, the artificial noise based and the relay selection schemes improves, and vice versa. This implies a trade-off between the security and reliability of the secondary transmission in the presence of eavesdropping attacks, which is referred to as the security-reliability trade-off (SRT). Furthermore, we demonstrate that the SRTs of the single-relay and multi-relay selection schemes are generally better than that of classic direct transmission, explicitly demonstrating the advantage of the proposed relay selection in terms of protecting the secondary transmissions against eavesdropping attacks. Moreover, as the number of SRs increases, the SRTs of the proposed single-relay and multi-relay selection approaches significantly improve. Finally, our numerical results show that as expected, the multi-relay selection scheme achieves a better SRT performance than the single-relay selection.

Recent advances and emerging challenges of feature selection in the context of big data

Abstract: The explosion of big data has posed important challenges to researchers.Feature selection is paramount when dealing with high-dimensional datasets.We review the state-of-the-art and recent contributions in feature selection.The emerging challenges in feature selection are identified and discussed. In an era of growing data complexity and volume and the advent of big data, feature selection has a key role to play in helping reduce high-dimensionality in machine learning problems. We discuss the origins and importance of feature selection and outline recent contributions in a range of applications, from DNA microarray analysis to face recognition. Recent years have witnessed the creation of vast datasets and it seems clear that these will only continue to grow in size and number. This new big data scenario offers both opportunities and challenges to feature selection researchers, as there is a growing need for scalable yet efficient feature selection methods, given that existing methods are likely to prove inadequate.

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

Abstract: Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown to have several advantages over the lasso; these penalties may also be extended to the group selection problem, giving rise to group SCAD and group MCP methods. Here, we describe algorithms for fitting these models stably and efficiently. In addition, we present simulation results and real data examples comparing and contrasting the statistical properties of these methods.

Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi-parental Maize Populations under Drought Stress

Abstract: Genomic selection incorporates all the available marker information into a model to predict genetic values of breeding progenies for selection. The objective of this study was to estimate genetic gains in grain yield from genomic selection (GS) in eight bi-parental maize populations under managed drought stress environments. In each population, 148 to 300 F₂:₃ (C₀) progenies were derived and crossed to a single-cross tester from a complementary heterotic group. The resulting testcrosses of each population were evaluated under two to four managed drought stress and three to four well-watered conditions in different locations and genotyped with 191 to 286 single nucleotide polymorphism (SNP) markers. The top 10% families were selected from C₀ using a phenotypic selection index and were intermated to form C₁. Selections both at C₁ and C₂ were based on genomic estimated breeding values (GEBVs). The best lines from C₀ were also advanced using a pedigree selection scheme. For genetic gain studies, a total of 55 entries representing the eight populations were crossed to a single-cross tester, and evaluated in four managed drought stress environments. Each population was represented by bulk seed containing equal amounts of seed of C₀, C₁, C₂, C₃, parents, F₁s, and lines developed via pedigree selection. Five commercial checks were included for comparison. The average gain from genomic selection per cycle across eight populations was 0.086 Mg ha–¹. The average grain yield of C₃–derived hybrids was significantly higher than that of hybrids derived from C₀. Hybrids derived from C₃ produced 7.3% (0.176 Mg ha–¹) higher grain yield than those developed through the conventional pedigree breeding method. The study demonstrated that genomic selection is more effective than pedigree-based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.

A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes

Abstract: Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines. A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies. Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.

FASTAptamer: A Bioinformatic Toolkit for High-throughput Sequence Analysis of Combinatorial Selections

Abstract: High-throughput sequence (HTS) analysis of combinatorial selection populations accelerates lead discovery and optimization and offers dynamic insight into selection processes. An underlying principle is that selection enriches high-fitness sequences as a fraction of the population, whereas low-fitness sequences are depleted. HTS analysis readily provides the requisite numerical information by tracking the evolutionary trajectory of individual sequences in response to selection pressures. Unlike genomic data, for which a number of software solutions exist, user-friendly tools are not readily available for the combinatorial selections field, leading many users to create custom software. FASTAptamer was designed to address the sequence-level analysis needs of the field. The open source FASTAptamer toolkit counts, normalizes and ranks read counts in a FASTQ file, compares populations for sequence distribution, generates clusters of sequence families, calculates fold-enrichment of sequences throughout the course of a selection and searches for degenerate sequence motifs. While originally designed for aptamer selections, FASTAptamer can be applied to any selection strategy that can utilize next-generation DNA sequencing, such as ribozyme or deoxyribozyme selections, in vivo mutagenesis and various surface display technologies (peptide, antibody fragment, mRNA, etc.). FASTAptamer software, sample data and a user's guide are available for download at http://burkelab.missouri.edu/fastaptamer.html.

Feature Selection for High-Dimensional Data

Abstract: This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

Comparison of Bayesian predictive methods for model selection

Abstract: The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendations about the preferred approaches. We focus on the variable subset selection for regression and classification and perform several numerical experiments using both simulated and real world data. The results show that the optimization of a utility estimate such as the cross-validation (CV) score is liable to finding overfitted models due to relatively high variance in the utility estimates when the data is scarce. This can also lead to substantial selection induced bias and optimism in the performance evaluation for the selected model. From a predictive viewpoint, best results are obtained by accounting for model uncertainty by forming the full encompassing model, such as the Bayesian model averaging solution over the candidate models. If the encompassing model is too complex, it can be robustly simplified by the projection method, in which the information of the full model is projected onto the submodels. This approach is substantially less prone to overfitting than selection based on CV-score. Overall, the projection method appears to outperform also the maximum a posteriori model and the selection of the most probable variables. The study also demonstrates that the model selection can greatly benefit from using cross-validation outside the searching process both for guiding the model size selection and assessing the predictive performance of the finally selected model.

Detecting balancing selection in genomes: limits and prospects

Abstract: In spite of the long-term interest in the process of balancing selection, its frequency in genomes and evolutionary significance remain unclear due to challenges related to its detection. Current statistical approaches based on patterns of variation observed in molecular data suffer from low power and a high incidence of false positives. This raises the question whether balancing selection is rare or is simply difficult to detect. We discuss genetic signatures produced by this mode of selection and review the current approaches used for their identification in genomes. Advantages and disadvantages of the available methods are presented, and areas where improvement is possible are identified. Increased specificity and reduced rate of false positives may be achieved by using a demographic model, applying combinations of tests, appropriate sampling scheme and taking into account intralocus variation in selection pressures. We emphasize novel solutions, recently developed model-based approaches and good practices that should be implemented in future studies looking for signals of balancing selection. We also draw attention of the readers to the results of recent theoretical studies, which suggest that balancing selection may be ubiquitous but transient, leaving few signatures detectable by existing methods. Testing this new theory may require the development of novel high-throughput methods extending beyond genomic scans.

Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm

Abstract: The model of support vector regression with adaptive genetic algorithm and the seasonal mechanism is proposed.Parameters selection and seasonal adjustment should be carefully selected.We focus on latest and representative holiday daily data in China.Two experiments are used to prove the effect of the model.The AGASSVR is superior to AGA-SVR and BPNN. Accurate holiday daily tourist flow forecasting is always the most important issue in tourism industry. However, it is found that holiday daily tourist flow demonstrates a complex nonlinear characteristic and obvious seasonal tendency from different periods of holidays as well as the seasonal nature of climates. Support vector regression (SVR) has been widely applied to deal with nonlinear time series forecasting problems, but it suffers from the critical parameters selection and the influence of seasonal tendency. This article proposes an approach which hybridizes SVR model with adaptive genetic algorithm (AGA) and the seasonal index adjustment, namely AGA-SSVR, to forecast holiday daily tourist flow. In addition, holiday daily tourist flow data from 2008 to 2012 for Mountain Huangshan in China are employed as numerical examples to validate the performance of the proposed model. The experimental results indicate that the AGA-SSVR model is an effective approach with more accuracy than the other alternative models including AGA-SVR and back-propagation neural network (BPNN).

The Relative Age Effect in Sport: A Developmental Systems Model

Abstract: The policies that dictate the participation structure of many youth sport systems involve the use of a set selection date (e.g. 31 December), which invariably produces relative age differences between those within the selection year (e.g. 1 January to 31 December). Those born early in the selection year (e.g. January) are relatively older—by as much as 12 months minus 1 day—than those born later in the selection year (e.g. December). Research in the area of sport has identified a number of significant developmental effects associated with such relative age differences. However, a theoretical framework that describes the breadth and complexity of relative age effects (RAEs) in sport does not exist in the literature. This paper reviews and summarizes the existing literature on relative age in sport, and proposes a constraints-based developmental systems model for RAEs in sport.

A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization

Abstract: Habitat selection and compensation for Doppler effect are incorporated into algorithm.Algorithm possesses the quantum search operator and mechanical search operator.Self-adaptive local search is proposed.Algorithm shows significant performance in comparison with more than 20 methods. A novel bat algorithm (NBA) is proposed for optimization in this paper, which focuses on further mimicking the bats' behaviors and improving bat algorithm (BA) in view of biology. The proposed algorithm incorporates the bats' habitat selection and their self-adaptive compensation for Doppler effect in echoes into the basic BA. The bats' habitat selection is modeled as the selection between their quantum behaviors and mechanical behaviors. Having considered the bats' self-adaptive compensation for Doppler effect in echoes and the individual's difference in the compensation rate, the echolocation characteristics of bats can be further simulated in NBA. A self-adaptive local search strategy is also embedded into NBA. Simulations and comparisons based on twenty benchmark problems and four real-world engineering designs demonstrate the effectiveness, efficiency and stability of NBA compared with the basic BA and some well-known algorithms, and suggest that to improve algorithm based on biological basis should be very efficient. Further research topics are also discussed.

Selection and implementation of expanded CODIS core loci in the United States

Abstract: This letter is to update the DNA community on the Federal Bureau of Investigation (FBI) CODIS Core Loci Working Group’s progress and the evaluation of additional core loci. As announced in previous communications initially published online in April of 2011 [1,2], the FBI launched this effort to determine additional core loci that could be implementedintothe CODISProgram to support lawenforcement DNA databases. The goals of this expansion include reducing the number of adventitious matches, increasing international compatibility and increasing the power of discrimination for criminal and missing person cases. The current CODIS core 13 loci were given primary consideration, with one exception, for inclusion within the proposed core set of loci for CODIS. In addition, other loci that are currently part of DNA typing kits used in the United States were considered as potential new loci [3]. Loci used internationally for forensic DNA databasing purposes were also identified for consideration [4]. It is important to note that the loci chosen for this project have no known predictive value for medical condition ordisease. The Working Group developed and published an implementation timeline for the public in June 2011[5]. As noted in the initial explanation of this Project, a ranked list of loci were recommended (Table 1). In the fall of 2012, manufacturer grade PCR amplification kits containing those loci became available for use in the United States. The CODIS Core Loci Working Group selected a consortium of 11 CODIS laboratories representing casework, databasing and missing person laboratories to evaluate the available PCR amplification kits: Life Technologies’ GlobalFiler, Life Technologies GlobalFiler Express and Promega Powerplex Fusion. Over an eighteen month period, these laboratories performed validation experiments in accordance with the FBI Director’s Quality Assurance Standards that included studies on the following: known or non-probative samples, precision, reproducibility, sensitivity and stochastic, mixture, and casework challenge samples. With the assistance of the National Institute of Standards and Technology (NIST), the data generated through these validation studies were compiled, reviewed and analyzed. During this Project, the FBI has provided updates to the DNA community and stakeholders through presentations at annual National CODIS Conferences, semiannual CODIS State Administrators meetings, semiannual meetings of the Scientific Working Group on DNA Analysis Methods (SWGDAM), as well as the annual International Symposium on Human Identification. Consistent with the initial considerations in the selection of the proposed core loci, the Working Group acknowledged that there are almost 14 million STR profiles in the National DNA Index

Correlation and instance based feature selection for electricity load forecasting

Abstract: Appropriate feature (variable) selection is crucial for accurate forecasting. In this paper we consider the task of forecasting the future electricity load from a time series of previous electricity loads, recorded every 5min. We propose a two-step approach that identifies a set of candidate features based on the data characteristics and then selects a subset of them using correlation and instance-based feature selection methods, applied in a systematic way. We evaluate the performance of four feature selection methods - one traditional (autocorrelation) and three advanced machine learning (mutual information, RReliefF and correlation-based), in conjunction with state-of-the-art prediction algorithms (neural networks, linear regression and model tree rules), using two years of Australian electricity load data. Our results show that all feature selection methods were able to identify small subsets of highly relevant features. The best two prediction models utilized instance and autocorrelation based feature selectors and an efficient neural network prediction algorithm. They were more accurate than advanced exponential smoothing prediction models, a typical industry model and other baselines used for comparison.

The concept of fitness in fluctuating environments

Abstract: Fitness is a central concept in evolutionary biology, but there is no unified definition. We review recent theoretical developments showing that including fluctuating environments and density dependence has important implications for how differences among phenotypes in their contributions to future generations should be quantified. The rate of phenotypic evolution will vary through time because of environmental stochasticity. Density dependence may produce fluctuating selection for large growth rates at low densities but for larger carrying capacities when population sizes are large. In general, including ecologically realistic assumptions when defining the concept of fitness is crucial for estimating the potential of evolutionary rescue of populations affected by environmental perturbations such as climate change.

Uplink transmissions in wireless communications

Abstract: Methods and devices for offloading and/or aggregation of resources to coordinate uplink transmissions when interacting with different schedulers are disclosed herein. A method in a WTRU includes functionality for coordinating with a different scheduler for each eNB associated with the WTRU's configuration. Disclosed methods include autonomous WTRU grant selection and power scaling, and dynamic prioritization of transmission and power scaling priority.