Genomic Analysis of Immune Response against Vibrio Cholerae Hemolysin in Caenorhabditis elegans

Vibrio cholerae cytolysin (VCC) is among the accessory V. cholerae virulence factors that may contribute to disease pathogenesis in humans. VCC, encoded by hlyA gene, belongs to the most common class of bacterial toxins, known as poreforming toxins (PFTs). V. cholerae infects and kills Caenorhabditis elegans via cholerae toxin independent manner. VCC is required for the lethality, growth retardation and intestinal cell vacuolation during the infection. However, little is known about the host gene expression responses against VCC. To address this question we performed a microarray study in C. elegans exposed to V. cholerae strains with intact and deleted hlyA genes. Many of the VCC regulated genes identified, including C-type lectins, Prion-like (glutamine [Q]/asparagine [N]-rich)-domain containing genes, genes regulated by insulin/ IGF-1-mediated signaling (IIS) pathway, were previously reported as mediators of innate immune response against other bacteria in C. elegans. Protective function of the subset of the genes up-regulated by VCC was confirmed using RNAi. By means of a machine learning algorithm called FastMEDUSA, we identified several putative VCC induced immune regulatory transcriptional factors and transcription factor binding motifs. Our results suggest that VCC is a major virulence factor, which induces a wide variety of immune response- related genes during V. cholerae infection in C. elegans

Electrical spin injection from an organic-based ferrimagnet in a hybrid organic/inorganic heterostructure

We report the successful extraction of spin polarized current from the organic-based room temperature ferrimagnetic semiconductor V[TCNE]x (x~2, TCNE: tetracyanoethylene; TC ~ 400 K, EG ~ 0.5 eV, s ~ 10-2 S/cm) and its subsequent injection into a GaAs/AlGaAs light-emitting diode (LED). The spin current tracks the magnetization of V[TCNE]x~2, is weakly temperature dependent, and exhibits heavy hole / light hole asymmetry. This result has implications for room temperature spintronics and the use of inorganic materials to probe spin physics in organic and molecular systems

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

Corpora in Text-Based Russian Studies

This chapter focuses on textual data that are collected for a specific purpose, which are usually referred to as corpora. Scholars use corpora when they examine existing instances of a certain phenomenon or to conduct systematic quantitative analyses of occurrences, which in turn reflect habits, attitudes, opinions, or trends. For these contexts, it is extremely useful to combine different approaches. For example, a linguist might analyze the frequency of a certain buzzword, whereas a scholar in the political, cultural, or sociological sciences might attempt to explain the change in language usage from the data in question.Peer reviewe

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

An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra

After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections

Surface waves and crustal structure on Mars

We detected surface waves from two meteorite impacts on Mars. By measuring group velocity dispersion along the impact-lander path, we obtained a direct constraint on crustal structure away from the InSight lander. The crust north of the equatorial dichotomy had a shear wave velocity of approximately 3.2 kilometers per second in the 5- to 30-kilometer depth range, with little depth variation. This implies a higher crustal density than inferred beneath the lander, suggesting either compositional differences or reduced porosity in the volcanic areas traversed by the surface waves. The lower velocities and the crustal layering observed beneath the landing site down to a 10-kilometer depth are not a global feature. Structural variations revealed by surface waves hold implications for models of the formation and thickness of the martian crust.D.K., S.C., D.G., J.C., C.D., A. K., S.C.S., N.D., and G.Z. were supported by the ETH+ funding scheme (ETH+02 19-1: “Planet Mars”). Marsquake Service operations at ETH Zürich were supported by ETH Research grant ETH-06 17-02. N.C.S. and V.L. were supported by NASA PSP grant no. 80NSSC18K1628. Q.H. and E.B. are funded by NASA grant 80NSSC18K1680. C.B. and J.L. were supported by NASA InSight PSP grant no. 80NSSC18K1679. S.D.K. was supported by NASA InSight PSP grant no. 80NSSC18K1623. P.L., E.B., M.D., H.S., E.S., M.W., Z.X., T.W., M.P., R.F.G. were supported by CNES and the Agence Nationale de la Recherche (ANR-19-CE31-0008-08 MAGIS) for SEIS operation and SEIS Science analysis. A.H., C.C. and W.T.P. were supported by the UKSA under grant nos. ST/R002096/1, ST/ W002523/1 and ST/V00638X/1. Numerical computations of McMC Approach 2 were performed on the S-CAPAD/DANTE platform (IPGP, France) and using the HPC resources of IDRIS under the allocation A0110413017 made by GENCI. A.H. was supported by the UKSA under grant nos. ST/R002096/1 and ST/W002523/1. F.N. was supported by InSight PSP 80NSSC18K1627. I.J.D. was supported by NASA InSight PSP grant no. 80NSSC20K0971. L.V.P. was funded by NASANNN12AA01C with subcontract JPL-1515835. The research was carried out in part by W.B.B., M.G. and M.P.P. at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004)Peer reviewe

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