Publications by authors named "Rouillard A"

Cold atmospheric pressure plasma (ionized gas) is an innovative medical tool for the treatment of infected wounds thanks to its potential to inactivate drug-resistant microorganisms and promote tissue regeneration and vascularization. The low power consumption, compactness, and versatility of Cold Atmospheric Pressure Plasma (CAPP) devices make them an ideal tool for risk mitigation associated with human spaceflights. This work presents results in microgravity on the operability of CAPP and its antimicrobial effect.

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Late Pliocene and Early Pleistocene epochs 3.6 to 0.8 million years ago had climates resembling those forecasted under future warming.

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Geomagnetic storms are an important aspect of space weather and can result in significant impacts on space- and ground-based assets. The majority of strong storms are associated with the passage of interplanetary coronal mass ejections (ICMEs) in the near-Earth environment. In many cases, these ICMEs can be traced back unambiguously to a specific coronal mass ejection (CME) and solar activity on the frontside of the Sun.

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During the last glacial-interglacial cycle, Arctic biotas experienced substantial climatic changes, yet the nature, extent and rate of their responses are not fully understood. Here we report a large-scale environmental DNA metagenomic study of ancient plant and mammal communities, analysing 535 permafrost and lake sediment samples from across the Arctic spanning the past 50,000 years. Furthermore, we present 1,541 contemporary plant genome assemblies that were generated as reference sequences.

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Genetic variants are the primary driver of congenital heart disease (CHD) pathogenesis. However, our ability to identify causative variants is limited. To identify causal CHD genes that are associated with specific molecular functions, the study used prior knowledge to filter de novo variants from 2,881 probands with sporadic severe CHD.

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NASA's Parker Solar Probe mission recently plunged through the inner heliosphere of the Sun to its perihelia, about 24 million kilometres from the Sun. Previous studies farther from the Sun (performed mostly at a distance of 1 astronomical unit) indicate that solar energetic particles are accelerated from a few kiloelectronvolts up to near-relativistic energies via at least two processes: 'impulsive' events, which are usually associated with magnetic reconnection in solar flares and are typically enriched in electrons, helium-3 and heavier ions, and 'gradual' events, which are typically associated with large coronal-mass-ejection-driven shocks and compressions moving through the corona and inner solar wind and are the dominant source of protons with energies between 1 and 10 megaelectronvolts. However, some events show aspects of both processes and the electron-proton ratio is not bimodally distributed, as would be expected if there were only two possible processes.

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Remote observations of the solar photospheric light scattered by electrons (the K-corona) and dust (the F-corona or zodiacal light) have been made from the ground during eclipses and from space at distances as small as 0.3 astronomical units to the Sun. Previous observations of dust scattering have not confirmed the existence of the theoretically predicted dust-free zone near the Sun.

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Sediment cores from Kløverbladvatna, a threshold lake in Wahlenbergfjorden, Nordaustlandet, Svalbard were used to reconstruct Holocene glacier fluctuations. Meltwater from Etonbreen spills over a threshold to the lake, only when the glacier is significantly larger than at present. Lithological logging, loss-on-ignition, ITRAX scanning and radiocarbon dating of the cores show that Kløverbladvatna became isolated from Wahlenbergfjorden c.

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Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success.

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We present an advance toward accurately predicting the arrivals of coronal mass ejections (CMEs) at the terrestrial planets, including Earth. For the first time, we are able to assess a CME prediction model using data over two thirds of a solar cycle of observations with the Heliophysics System Observatory. We validate modeling results of 1337 CMEs observed with the Solar Terrestrial Relations Observatory (STEREO) heliospheric imagers (HI) (science data) from 8 years of observations by five in situ observing spacecraft.

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The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.

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Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO).

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Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse.

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Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download.

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The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS.

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With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction.

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With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction.

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Motivation: Identification of differentially expressed genes is an important step in extracting knowledge from gene expression profiling studies. The raw expression data from microarray and other high-throughput technologies is deposited into the Gene Expression Omnibus (GEO) and served as Simple Omnibus Format in Text (SOFT) files. However, to extract and analyze differentially expressed genes from GEO requires significant computational skills.

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Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs.

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Summary: Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts.

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