Optimizing collective fieldtaxis of swarming agents through reinforcement learning

Swarming of animal groups enthralls scientists in fields ranging from biology to physics to engineering. Complex swarming patterns often arise from simple interactions between individuals to the benefit of the collective whole. The existence and success of swarming, however, nontrivially depend on microscopic parameters governing the interactions. Here we show that a machine-learning technique can be employed to tune these underlying parameters and optimize the resulting performance. As a concrete example, we take an active matter model inspired by schools of golden shiners, which collectively conduct phototaxis. The problem of optimizing the phototaxis capability is then mapped to that of maximizing benefits in a continuum-armed bandit game. The latter problem accepts a simple reinforcement-learning algorithm, which can tune the continuous parameters of the model. This result suggests the utility of machine-learning methodology in swarm-robotics applications.Comment: 6 pages, 3 figure

ISAG - Design and use of the MNEc670k SNP array for precision SNP imputation to millions of markers in 15 horse breeds

Single nucleotide polymorphism (SNP) genotyping arrays containing 54K-74 thousand (K) markers for the horse have enabled genome wide association studies examining disease and performance traits, as well as quantitation of variation within and between populations. We recently designed the MNEc670k array for denser genotyping capability, as well as genotype imputation, or the statistical inference of sample haplotypes from a smaller set of markers. As part of this design a cohort of 485 horses having 2 million (M) SNP genotype data (n=332) or whole genome sequence (n=153) was used to select “tagging SNPs” that were informative for differentiating haplotypes both across and within 15 breed tagging groups. Across all breed tagging groups, 355,903 SNPs were needed to reconstruct haplotypes with minor allele frequency (MAF) > 0.01 with an r2 > 0.99. In each of the 15 breed tagging groups, between 144,175 and 387,279 SNPs were required for haplotype reconstruction. All SNPs that were informative across breed groups, as well as SNPs that were informative in five or more breed tagging groups, were included on the MNEc670k SNP array.<br><br><br>The performance of the MNEc670k array for SNP imputation was assessed in several scenarios. Genotypes of the 485 horses with either 2M or WGS data were masked down to the MNEc670k density, as well as legacy array 54/75K SNP density, in a random 1/3 subset of individuals in each of the 15 breed tagging groups. After removing the imputation targets, the remaining horses were used as a reference population. Imputation concordance from 54/65K SNPs to 2M SNPs in breed tagging groups ranged from 82-96% depending on breed group, while concordance from 670k to 2M SNPs ranged between 97-99%. Imputation from 670K to 14M SNPs (WGS) was assessed in a cohort of 38 Standardbred and 20 Thoroughbred horses yielding a concordance of 96 and 97% respectively. Additionally, we report the gains in accuracy of imputation using breed-specific haplotype and recombination maps, which improve SNP accuracy in scenarios where breed specific parameters can be reliably estimated. Read the pre-print manuscript describing this work in more detail here: https://doi.org/10.1101/112979 <br

Additional file 1: Table S1. of Identification and validation of risk loci for osteochondrosis in standardbreds

Named genes located within the top regions of association on ECA14 from the GWA analysis. Table S2. Haplotype analysis within the top regions of association on ECA14 from the GWA analysis. Table S3. Top GWA SNPs from GEMMA mixed model analysis of data imputed to 670 k and 2 M SNP lists. Table S4. Summary of variants by type and region. Table S5. Regions of interest for which detailed annotation of SNPs was performed. Table S6. Putative risk variants for OC that were selected for inclusion in the custom Sequenom genotyping assay (n = 240). Table S7. Frequency of alternate allele in cases and controls for each SNP in the Sequenom platform that genotyped successfully in the discovery or validation populations. (DOCX 93 kb

Genotypic and Environmental Impact on Natural Variation of Nutrient Composition in 50 Non Genetically Modified Commercial Maize Hybrids in North America

This study was designed to assess natural variation in composition and metabolites in 50 genetically diverse non genetically modified maize hybrids grown at six locations in North America. Results showed that levels of compositional components in maize forage were affected by environment more than genotype. Crude protein, all amino acids except lysine, manganese, and β-carotene in maize grain were affected by environment more than genotype; however, most proximates and fibers, all fatty acids, lysine, most minerals, vitamins, and secondary metabolites in maize grain were affected by genotype more than environment. A strong interaction between genotype and environment was seen for some analytes. The results could be used as reference values for future nutrient composition studies of genetically modified crops and to expand conventional compositional data sets. These results may be further used as a genetic basis for improvement of the nutritional value of maize grain by molecular breeding and biotechnology approaches

Fertinatural Ltda.

Fertinatural Ltda. es una empresa que incursiona en el mercado nacional empezando por el Valle del Cauca y sus alrededores, a través de un fertilizante hecho a base de ácido húmico el cual genera mayores beneficios para las cosechas, a diferencia de los fertilizantes químicos generalmente utilizados por los agricultore

Additional file 3: of A GJA9 frameshift variant is associated with polyneuropathy in Leonberger dogs

Haplotype reconstruction for the centromeric region of acrocentric CFA15 of 314 dogs. (XLSX 3145 kb

Probability, random processes, and ergodic properties

In this new edition of this classic text, much of the material has been rearranged and revised for pedagogical reasons. Many classic inequalities and proofs are now incorporated into the text, and many citations have been added

Additional file 1: Table S1. of Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds

Whole genome sequencing (WGS) samples. Whole genome sequencing samples with horse identification and read depth. 153 Individuals representing 24 breeds (Twilight is included as 4 entries). Table includes horse identifier, breed, contributing laboratory and coverage statistics for each individual in both the nuclear and mitochondrial genomes. Variant calling groups indicate which individuals were grouped together during variant discovery. (XLSX 21 kb

Additional file 2: Table S2. of Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds

SNP50 breed informativeness. Impact of breed on SNP50 informativeness. Table shows informativeness (MAF ≥ 0.05) of the legacy, SNP50 SNP set for light, draft, and pony breeds described by McCue et al. [1, 2]. Mean number of informative SNPs per breed was ~42,000 (~77%). 19,427 SNPs were informative in all 14 breeds. (XLSX 8 kb

Additional file 11: Figure S3. of Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds

MNEc2M Inter-SNP distance and informativeness by breed. Distance between SNPs on the MNEc2M array were calculated by breed group (See Additional file 5: Table S5) at various minor allele frequency cutoffs (0, 0.01, 0.03, 0.05, 0.10). Breed groups with asterisk (*) indicate a combination of studbook breeds. (PNG 1152 kb