Statistical Learning Theory for Control: A Finite Sample Perspective

This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been substantial progress across all areas of control, the theory is most well-developed when it comes to linear system identification and learning for the linear quadratic regulator, which are the focus of this manuscript. From a theoretical perspective, much of the labor underlying these advances has been in adapting tools from modern high-dimensional statistics and learning theory. While highly relevant to control theorists interested in integrating tools from machine learning, the foundational material has not always been easily accessible. To remedy this, we provide a self-contained presentation of the relevant material, outlining all the key ideas and the technical machinery that underpin recent results. We also present a number of open problems and future directions.Comment: Survey Paper, Submitted to Control Systems Magazine. Second version contains additional motivation for finite sample statistics and more detailed comparison with classical literatur

Combined GWAS and ‘guilt by association’-based prioritization analysis identifies functional candidate genes for body size in sheep

International audienceAbstractBackgroundBody size in sheep is an important indicator of productivity, growth and health as well as of environmental adaptation. It is a composite quantitative trait that has been studied with high-throughput genomic methods, i.e. genome-wide association studies (GWAS) in various mammalian species. Several genomic markers have been associated with body size traits and genes have been identified as causative candidates in humans, dog and cattle. A limited number of related GWAS have been performed in various sheep breeds and have identified genomic regions and candidate genes that partly account for body size variability. Here, we conducted a GWAS in Frizarta dairy sheep with phenotypic data from 10 body size measurements and genotypic data (from Illumina ovineSNP50 BeadChip) for 459 ewes.ResultsThe 10 body size measurements were subjected to principal component analysis and three independent principal components (PC) were constructed, interpretable as width, height and length dimensions, respectively. The GWAS performed for each PC identified 11 significant SNPs, at the chromosome level, one on each of the chromosomes 3, 8, 9, 10, 11, 12, 19, 20, 23 and two on chromosome 25. Nine out of the 11 SNPs were located on previously identified quantitative trait loci for sheep meat, production or reproduction. One hundred and ninety-seven positional candidate genes within a 1-Mb distance from each significant SNP were found. A guilt-by-association-based (GBA) prioritization analysis (PA) was performed to identify the most plausible functional candidate genes. GBA-based PA identified 39 genes that were significantly associated with gene networks relevant to body size traits. Prioritized genes were identified in the vicinity of all significant SNPs except for those on chromosomes 10 and 12. The top five ranking genes were TP53, BMPR1A, PIK3R5, RPL26 and PRKDC.ConclusionsThe results of this GWAS provide evidence for 39 causative candidate genes across nine chromosomal regions for body size traits, some of which are novel and some are previously identified candidates from other studies (e.g. TP53, NTN1 and ZNF521). GBA-based PA has proved to be a useful tool to identify genes with increased biological relevance but it is subjected to certain limitations

The effects of geographic origin and antibiotic treatment on the gut symbiotic communities of <i>Bactrocera oleae</i> populations

The olive fruit fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae), is the major insect pest of olive orchards (Olea europaea L.), causing extensive damages on cultivated olive crops worldwide. Due to its economic importance, it has been the target species for a variety of population control approaches including the sterile insect technique (SIT). However, the inefficiency of the current mass-rearing techniques impedes the successful application of area-wide integrated pest management programs with an SIT component. It has been shown that insect mass rearing and quality of sterile insects can be improved by the manipulation of the insect gut microbiota and probiotic applications. In order to exploit the gut bacteria, it is important to investigate the structure of the gut microbial community. In the current study, we characterized the gut bacterial profile of two wild olive fruit fly populations introduced in laboratory conditions using next generation sequencing of two regions of the 16S rRNA gene. We compared the microbiota profiles regarding the geographic origin of the samples. Additionally, we investigated potential changes in the gut bacteria community before and after the first exposure of the wild adult flies to artificial adult diet with and without antibiotics. Various genera - such as Erwinia, Providencia, Enterobacter, and Klebsiella - were detected for the first time in B. oleae. The most dominant species was Candidatus Erwinia dacicola Capuzzo et al. and it was not affected by the antibiotics in the artificial adult diet used in the first generation of laboratory rearing. Geographic origin affected the overall structure of the gut community of the olive fruit fly, but antibiotic treatment in the first generation did not significantly alter the gut microbiota community