PADLL: Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control

Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple concurrent applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.Comment: To appear at 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid'23

Rice Galaxy: An open resource for plant science

Background: Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non−computer savvy rice researchers. Findings: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice−bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions: Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science

Grand Challenges in Immersive Analytics

The definitive version will be published in CHI 2021, May 8–13, 2021, Yokohama, JapanInternational audienceImmersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and humancomputer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics

Conditional knockout of TMEM16A/anoctamin1 abolishes the calcium-activated chloride current in mouse vomeronasal sensory neurons.

Pheromones are substances released from animals that, when detected by the vomeronasal organ of other individuals of the same species, affect their physiology and behavior. Pheromone binding to receptors on microvilli on the dendritic knobs of vomeronasal sensory neurons activates a second messenger cascade to produce an increase in intracellular Ca2+concentration. Here, we used whole-cell and inside-out patch-clamp analysis to provide a functional characterization of currents activated by Ca2+in isolated mouse vomeronasal sensory neurons in the absence of intracellular K+. In whole-cell recordings, the average current in 1.5 \u3bcM Ca2+and symmetrical Cl-was -382 pA at -100 mV. Ion substitution experiments and partial blockade by commonly used Cl-channel blockers indicated that Ca2+activates mainly anionic currents in these neurons. Recordings from inside-out patches from dendritic knobs of mouse vomeronasal sensory neurons confirmed the presence of Ca2+-activated Cl-channels in the knobs and/or microvilli. We compared the electrophysiological properties of the native currents with those mediated by heterologously expressed TMEM16A/anoctamin1 or TMEM16B/anoctamin2 Ca2+-activated Cl-channels, which are coexpressed in microvilli of mouse vomeronasal sensory neurons, and found a closer resemblance to those of TMEM16A. We used the Cre-loxP system to selectively knock out TMEM16A in cells expressing the olfactory marker protein, which is found in mature vomeronasal sensory neurons. Immunohistochemistry confirmed the specific ablation of TMEM16A in vomeronasal neurons. Ca2+-activated currents were abolished in vomeronasal sensory neurons of TMEM16A conditional knockout mice, demonstrating that TMEM16A is an essential component of Ca2+-activated Cl-currents in mouse vomeronasal sensory neurons

Virtual Screening for High Specificity Inhibitors of SSH-2 Against Dual Specificity Phosphatases of Similar Homology

Abstract Dual Specificity Phosphatases (DSPs) are a subject of interest because of their ability as a protein tyrosine phosphatase group that can dephosphorylate phospho-tyrosine and phosphoserine/threonine. While relatively little is understood about them, their regulatory balance has been linked to the development of cancers, Alzheimer's disease, diabetes, and obesity. In order to rank compounds that bind specifically to a DSP called Slingshot Homolog 2 (SSH2), we use the program DOCK6 to test the docking energies for tens of thousands of ligand-protein combinations against two other proteins in the SSH family. A previously generated list of the top 1% of SSH2 binding ligands was found by screening the ZINC database with DOCK6, reducing number of possible candidates to 20,000. This top 1% is docked against SSH1 and SSH3 so that their binding energies can be compared to that of SSH2, determining which bind well to SSH2 but poorly to SSH1 and SSH3. Using this method across various DSPs, it is possible to discover a compound of medicinal significance. The screening process occurs in silico using models of DSP proteins generated in previous PRIME project