The genetics of a putative social trait in natural populations of yeast

The sharing of secreted invertase by yeast cells is a well established laboratory model for cooperation, but the only evidence that such cooperation occurs in nature is that the SUC loci, which encode invertase, vary in number and functionality. Genotypes that do not produce invertase can act as “cheats” in laboratory experiments, growing on the glucose that is released when invertase producers, or “cooperators”, digest sucrose. However, genetic variation for invertase production might instead be explained by adaptation of different populations to different local availabilities of sucrose, the substrate for invertase. Here we find that, 110 wild yeast strains isolated from natural habitats, all contained a single SUC locus and produced invertase; none were “cheats”. The only genetic variants we found were three strains isolated instead from sucrose-rich nectar, which produced higher levels of invertase from three additional SUC loci at their sub-telomeres. We argue that the pattern of SUC gene variation is better explained by local adaptation than by social conflict

The invisible power of fairness. How machine learning shapes democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.Comment: 12 pages, 1 figure, preprint version, submitted to The 32nd Canadian Conference on Artificial Intelligence that will take place in Kingston, Ontario, May 28 to May 31, 201

Breaking a species barrier by enabling hybrid recombination

Hybrid sterility maintains reproductive isolation between species by preventing them from exchanging genetic material1. Anti-recombination can contribute to hybrid sterility when different species' chromosome sequences are too diverged to cross over efficiently during hybrid meiosis, resulting in chromosome mis-segregation and aneuploidy. The genome sequences of the yeasts Saccharomyces cerevisiae and Saccharomyces paradoxus have diverged by about 12% and their hybrids are sexually sterile: nearly all of their gametes are aneuploid and inviable. Previous methods to increase hybrid yeast fertility have targeted the anti-recombination machinery by enhancing meiotic crossing over. However, these methods also have counteracting detrimental effects on gamete viability due to increased mutagenesis2 and ectopic recombination3. Therefore, the role of anti-recombination has not been fully revealed, and it is often dismissed as a minor player in speciation1. By repressing two genes, SGS1 and MSH2, specifically during meiosis whilst maintaining their mitotic expression, we were able to increase hybrid fertility 70-fold, to the level of non-hybrid crosses, confirming that anti-recombination is the principal cause of hybrid sterility. Breaking this species barrier allows us to generate, for the first time, viable euploid gametes containing recombinant hybrid genomes from these two highly diverged parent species

The Invisible Power of Fairness. How Machine Learning Shapes Democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy

Genome Resources for Climate‐Resilient Cowpea, an Essential Crop for Food Security

Cowpea (Vigna unguiculata L. Walp.) is a legume crop that is resilient to hot and drought‐prone climates, and a primary source of protein in sub‐Saharan Africa and other parts of the developing world. However, genome resources for cowpea have lagged behind most other major crops. Here we describe foundational genome resources and their application to the analysis of germplasm currently in use in West African breeding programs. Resources developed from the African cultivar IT97K‐499‐35 include a whole‐genome shotgun (WGS) assembly, a bacterial artificial chromosome (BAC) physical map, and assembled sequences from 4355 BACs. These resources and WGS sequences of an additional 36 diverse cowpea accessions supported the development of a genotyping assay for 51 128 SNPs, which was then applied to five bi‐parental RIL populations to produce a consensus genetic map containing 37 372 SNPs. This genetic map enabled the anchoring of 100 Mb of WGS and 420 Mb of BAC sequences, an exploration of genetic diversity along each linkage group, and clarification of macrosynteny between cowpea and common bean. The SNP assay enabled a diversity analysis of materials from West African breeding programs. Two major subpopulations exist within those materials, one of which has significant parentage from South and East Africa and more diversity. There are genomic regions of high differentiation between subpopulations, one of which coincides with a cluster of nodulin genes. The new resources and knowledge help to define goals and accelerate the breeding of improved varieties to address food security issues related to limited‐input small‐holder farming and climate stress

Development and implementation of high-throughput SNP genotyping in barley

<p>Abstract</p> <p>Background</p> <p>High density genetic maps of plants have, nearly without exception, made use of marker datasets containing missing or questionable genotype calls derived from a variety of genic and non-genic or anonymous markers, and been presented as a single linear order of genetic loci for each linkage group. The consequences of missing or erroneous data include falsely separated markers, expansion of cM distances and incorrect marker order. These imperfections are amplified in consensus maps and problematic when fine resolution is critical including comparative genome analyses and map-based cloning. Here we provide a new paradigm, a high-density consensus genetic map of barley based only on complete and error-free datasets and genic markers, represented accurately by graphs and approximately by a best-fit linear order, and supported by a readily available SNP genotyping resource.</p> <p>Results</p> <p>Approximately 22,000 SNPs were identified from barley ESTs and sequenced amplicons; 4,596 of them were tested for performance in three pilot phase Illumina GoldenGate assays. Data from three barley doubled haploid mapping populations supported the production of an initial consensus map. Over 200 germplasm selections, principally European and US breeding material, were used to estimate minor allele frequency (MAF) for each SNP. We selected 3,072 of these tested SNPs based on technical performance, map location, MAF and biological interest to fill two 1536-SNP "production" assays (BOPA1 and BOPA2), which were made available to the barley genetics community. Data were added using BOPA1 from a fourth mapping population to yield a consensus map containing 2,943 SNP loci in 975 marker bins covering a genetic distance of 1099 cM.</p> <p>Conclusion</p> <p>The unprecedented density of genic markers and marker bins enabled a high resolution comparison of the genomes of barley and rice. Low recombination in pericentric regions is evident from bins containing many more than the average number of markers, meaning that a large number of genes are recombinationally locked into the genetic centromeric regions of several barley chromosomes. Examination of US breeding germplasm illustrated the usefulness of BOPA1 and BOPA2 in that they provide excellent marker density and sensitivity for detection of minor alleles in this genetically narrow material.</p

Seismic Constraints on the Thickness and Structure of the Martian Crust from InSight

NASA¿s InSight mission [1] has for the first time placed a very broad-band seismometer on the surface of Mars. The Seismic Experiment for Interior Structure (SEIS) [2] has been collecting continuous data since early February 2019. The main focus of InSight is to enhance our understanding of the internal structure and dynamics of Mars, which includes the goal to better constrain the crustal thickness of the planet [3]. Knowing the present-day crustal thickness of Mars has important implications for its thermal evolution [4] as well as for the partitioning of silicates and heat-producing elements between the different layers of Mars. Current estimates for the crustal thickness of Mars are based on modeling the relationship between topography and gravity [5,6], but these studies rely on different assumptions, e.g. on the density of the crust and upper mantle, or the bulk silicate composition of the planet and the crust. The resulting values for the average crustal thickness differ by more than 100%, from 30 km to more than 100 km [7]. New independent constraints from InSight will be based on seismically determining the crustal thickness at the landing site. This single firm measurement of crustal thickness at one point on the planet will allow to constrain both the average crustal thickness of Mars as well as thickness variations across the planet when combined with constraints from gravity and topography [8]. Here we describe the determination of the crustal structure and thickness at the InSight landing site based on seismic receiver functions for three marsquakes compared with autocorrelations of InSight data [9].We acknowledge NASA, CNES, partner agencies and institutions (UKSA, SSO,DLR, JPL, IPGP-CNRS, ETHZ, IC, MPS-MPG) and the operators of JPL, SISMOC, MSDS, IRIS-DMC and PDS for providing SEED SEIS data. InSight data is archived in the PDS, and a full list of archives in the Geosciences, Atmospheres, and Imaging nodes is at https://pds-geosciences.wustl.edu/missions/insight/. This work was partially carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. ©2021, California Institute of Technology. Government sponsorship acknowledge