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13,113 results for “Resistivity”
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R1 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R2 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
Characterization of cefiderocol resistant spontaneous mutant variants of Klebsiella pneumoniae producing NDM-5 with single mutation in cirA
<p>Cefiderocol (CFDC) is a siderophore-cephalosporin antibiotic designed to combat highly resistant Gram-negative bacterial infections. Its mechanism involves a strong affinity for iron and active transport into bacterial cells, providing an alternative against strains resistant to common antibiotics. However, the emergence of CFDC resistance in Klebsiella is a growing concern. Recent reports highlight increasing CFDC resistance in K. pneumoniae, particularly associated with mutations in the cirA gene, responsible for encoding a siderophore receptor. Co-localization of blaNDM-like gene and cirA mutations correlates with higher CFDC resistance. The study focuses on a carbapenem-resistant K. pneumoniae strain (Kp-1) with carbapenemases blaNDM-5 and blaOXA-181, recovered from a post-surgery patient. The strain exhibited resistance to all tested antibiotics but susceptibility to CFDC. Heteroresistant populations with the halo on inhibition of CFDC were observed. Genomic analysis identified a novel mutation (W123*) in the cirA gene associated with CFDC resistance. Additionally, increased blaNDM-5 expression in Kp-1 IHC (intra-halo colony) compared to Kp-1 was noted. The coexistence of blaNDM-like and cirA variants, along with high blaNDM-5 expression, explains the observed 21-fold increase in Minimum Inhibition Concentration (MIC) in Kp-1 IHC. The study contributes to understanding the molecular mechanisms driving the emergence of cefiderocol resistance, emphasizing the significance of coexisting mutations in cirA and blaNDM-like genes.</p>
Resistive switching in benzylammonium-based Ruddlesden–Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing
<p><span>Structural, optoelectronic, and supplementary characterisation data for “</span><span>Resistive Switching in Benzylammonium-Based Ruddlesden-Popper Layered Hybrid Perovskites for Non-Volatile Memory and Neuromorphic Computing ”</span><span>, DOI:</span><span>10.1039/d3ma00618b</span><span>.</span></p>
Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)
<p>Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (<em>Brassica napus</em>)</p> <p><strong>Abstract</strong></p> <ul> <li>Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.</li> <li>Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with <em>Brassica napus</em> constitutive QDR to four contrasting fungal pathogens: <em>Alternaria brassicicola</em>, <em>Botrytis cinerea</em>, <em>Pyrenopeziza</em><em> brassicae</em> and <em>Verticillium longisporum. </em>We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to <em>V. longisporum </em>and potentially broad-spectrum QDR.</li> <li>This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in <em>B. napus </em>and is a significant resource for the scientific community. </li> </ul> <p><strong>Description of data files</strong></p> <p><strong>Full dataset for input into AT analysis </strong>Full datasets (infection phenotypes for <em>A. brassicicola, B. cinerea, </em>or <em>V.longisporum, </em>ROS measurements for chitin, flg22, or elf18) and link to original <em>P. brassicae </em>dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, <a href="https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075">https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075</a>). </p> <p><strong>Table S1 </strong>Mean, normalized phenotype data for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis.<strong> </strong></p> <p><strong>Table S2 </strong>Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.</p> <p><strong>Table S3</strong> Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). </p> <p><strong>Table S4 </strong>184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and<em> Verticillium longisporum</em>) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S5</strong> Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. </p> <p><strong>Table S6 R</strong>esults from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.</p> <p><strong>Table S7 </strong>Shared gene expression markers (GEMs) associated with resistance to different pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S8 </strong>List of genes in linkage disequilibrium with the top marker for <em>Verticillium longisporum</em> resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in <em>Brassica napus</em> reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the <em>B. napus</em> pantranscriptome is indicated. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p> </p>
A new repository of electrical resistivity tomography and ground penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard.
<p>We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (Western Svalbard, Brøggerhalvøya peninsula, Norway) as part of the project ICEtoFLUX (MUR/PRA2021 project-0027). The data set is composed of Electrical Resistivity Tomography (ERT) and GroundPenetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These examples can support the identification of the active layer and the occurrence of spatial variation of soil conditions at depth. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set is of major relevance because scant attention has been paid to the publication of geophysical data from the Ny-Ålesund area so far. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate changes studies on the Ny-Ålesund area. </p>
Aqueous geochemical measurements and speciation calculations with concurrent copper resistance gene counts from sediment metagenomes over a seasonal cycle from 2015 to 2016 on Silver Bow Creek and Blacktail Creek near Butte, MT
<p>This dataset contains information from concurrently gathered geochemical and metagenomic samples collected from Silver Bow Creek and Blacktail Creek near Butte, MT (SBC/BC) during 2015 and 2016. SBC/BC is recovering from metal contamination related to extensive mining in the area. Full geochemical measurements, geochemical speciation calculations, and gene counts of sequences mapping to copper resistance genes using MG-RAST are included. </p>
Genomic Typing, Antimicrobial Resistance Gene, Virulence Factor and Plasmid Replicon Dataset for the Important Pathogenic Bacteria Klebsiella pneumoniae
<p>The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p> <p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p> <p>In this dataset, we present the results of a genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen <em>Klebsiella pneumoniae</em> obtained from NCBI Genbank database. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), capsular (KL) and oligosaccharide (OL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to clonal groups (CG). The presence of antimicrobial resistance and virulence genes, as well as plasmid replicons, within the genomes is also reported. </p> <p>These data will be useful for researchers in the field of <em>K. pneumoniae</em> genomic epidemiology, resistance analysis and prevention measure development.</p>
Effect of sticky rice germ oil droplet spraying on chrysanthemum thrips resistance and metabolome
<p>This dataset contains experimental results from full plant assays with Chrysamthemum plants that were conducted to test the effectiveness of sprayng solutions containing sticky rice oil droplets for trapping of small arthropods on plants. The experiments were conducted at the Institute of Biology Leiden, Leiden University the Netherlands.</p> <p>The first dataset contains the results of the full plant assays with thrips.</p> <p>The second dataset contains the results of 1H NMR and GC-MS signals of leaf samples of sprayed chrysanthemum plants.</p> <p> </p> <p>Version history:</p> <p>Version 2: Included the RAW data on % coverage of plants for the two plant assays that had been left out during earlier submission</p> <p>Updated the metadatasheets within the excel files to be more complete.</p> <p>Version 3: Included a new excel sheet in the GC-MS and NMR data file in which a subset of the RAW HS-GC-MS and 1H NMR data, namely those peaks and delta signals that were identified and matchedd to compound id after untargeted analysis, are presented together with the name of the compounds or classes of compounds as mentioned in the manuscript.</p> <p>No changes were made to the plant assay data file</p> <p> </p> <p>In the "Dataset_TBierman_RGO_thrips_1HNMR_GC-MS_V3" excel file:</p> <p>Sheets: "Processed 1H NMR data" and "Processed HS-GC-MS data"</p> <p>contain processed 1H NMR and GC-MS data of chrysanthemum leaves, harvested after 10 or 25 days, of plants that were sprayed with water or vegetable-oil derived adhesives and infested with thrips or not.</p> <p>Sheet: "Quantitative data selected comp" contains a subset of the data where signals were found significant in the untargeted analysis have been annotated to their compound identity.</p> <p>In the "Dataset_TBierman_RGO_thrips_plantassay1_and_2_V3" excel file:</p> <p>Sheets "Plant_assay_1_RGO_thrips_d10_25" and "Plant_assay_2_RGO_thrips_d25" contain the raw plant assay data</p> <p>Sheets "Plant_assay_1_RGO_coverage" and "Plant_assay_2_RGO_coverage" contain the summary values of the estimated coverage with adhesive oil droplets of each respective experiment on the left side while on the right side the raw data is presented </p> <p> </p>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
Resistance, utilization factor curve and polarization curve of test campaign
<p>The datesets refer to the article "Experimental Procedures & First Results of an Innovative Solid Oxide Fuel Cell Test Rig: Parametric Analysis and Stability Test" (https://zenodo.org/badge/DOI/10.5281/zenodo.5763507.svg).</p> <p>Solid Oxide Fuel Cells are a promising technology for Solid Oxide Fuel Cells (SOFC) are a promising technology For high-efficiency electrochemical conversion of a vast range of fuel gas mixtures, thigh operating temperature conditions (650–900 ◦C) represent a challenge both at system level and at laboratory testing level, in terms of material properties and performance dynamics. In this work a detailed procedural analysis is presented for an innovative all-ceramic compact SOFC test rig and first experimental testing results are reported in terms of polarization curves obtained under parametric variation of operating conditions (H2 content, air ratio λ and temperature) and short-term voltage stability test under load (140 h at 0.3 A/cm2 ). The electrochemical characterization results confirm the validity of the used all-ceramic cell holder, showing excellent cell performances in terms of polarization. H2 content has the most impact on SOFC performance, followed by temperature and finally air ratio, whose impact in the analyzed range is hardly seen. From the short-term stability test, the test bench setup reliability is demonstrated, showing no significant performance degradation after 140 continuous hours under load, which confirms the high quality and reproducibility of the results.</p>
Targeted Re-sequencing Identifies Candidate Fusiform Rust Resistance Genes in Loblolly Pine
<p>A fasta file containing the subset of the v2.01 Pita genome in addition to the novel NLR genes that were targeted by hybridization probes. </p> <p>A bed file describing the intervals targeted by the hybridization probes.</p> <p>Trinity assemblies of the 30 RNAseq libraries along with predictions by transdecoder of CDS and peptide sequences from those trinity assemblies. </p>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</p>
The repurposing of tebipenem pivoxil as alternative therapy for severe gastrointestinal infections caused by extensively drug resistant Shigella spp.
<p><strong>dataset for The repurposing of tebipenem pivoxil as alternative therapy for severe gastrointestinal infections caused by extensively drug resistant <em>Shigella</em> spp.</strong></p> <p>Elena Fernández Alvaro <sup>1*</sup>, Phat Voong Vinh <sup>2</sup>, Cristina de Cozar <sup>1</sup>, David Wille <sup>1</sup>, Beatriz Urones <sup>1</sup>,</p> <p>Alan Price <sup>1</sup>, Nhu Tran Do Hoang <sup>2</sup>, Tuyen Ha Thanh <sup>2</sup>, Molly McCloskey <sup>3</sup>, Shareef Shaheen<sup> 3</sup>, Denise Dayao<sup> 4</sup>, Jaime de Mercado <sup>1</sup>, Pablo Castañeda <sup>1</sup>, Adolfo García-Perez <sup>1</sup>, Benson Singa <sup>5</sup>, Patricia Pavlinac <sup>6</sup>,</p> <p>Judd Walson<sup>3</sup>, Maria Santos Martínez-Martínez <sup>1</sup>, Samuel L.M. Arnold <sup>3</sup>, Tzipori Saul <sup>4</sup>, Lluis Ballell <sup>1#</sup>,</p> <p>and Stephen Baker <sup>7,8*</sup></p> <p> </p>
Interpolated Clay Fraction and Resistivity model of the Aare Valley, Switzerland
<p>The dataset is an underground model of the Upper Aare Valley in Switzerland. It has been made in the framework of the Phenix project at the University of Neuchâtel. It has been produced by applying the CF prediction method (doi : 10.5194/hess-18-4349-2014) to an EM dataset (doi : 10.5194/essd-13-2743-2021).</p> <p>The Model was then interpolated using Multiple Point statistics, with robust uncertainty quantification (doi : In review).</p> <p>The two files contain the same data, as pointset or gridVTK files. The data contained are :</p> <ul> <li>Log10(Resistivity)</li> <li>Log10(Resistivity) Uncertainty (STD)</li> <li>ClayFraction</li> <li>ClayFraction Uncertainty (STD)</li> </ul> <p>The X,Y,Z positions are provided in UTM32N (epsg : 32632).</p>
Late blight resistance in potato conferred by Rpi-Smira2/R8
<p>The data are related to Figure 7 of the publication Blatnik et al. (2022) Late blight resistance conferred by <em>Rpi-Smira2/R8</em> in potato genotypes<em> in vitro</em> depends on the genetic background, published in Plants 11: 1319 (https://doi.org/10.3390/plants11101319)</p> <p>The data represent late blight (<em>Phytophtora infestans</em>) disease scores of progeny <em>R8</em> genotypes and parental cultivars inoculated with four <em>P. infestans </em>isolates <em>in vitro</em>. The disease scores were evaluated daily for an eight day period post inoculation according to the late blight disease rating scale (see publication and info sheet of the data).</p>
Resistance test to bean common mosaic virus (BCMV) in common bean
<p>This video is part of a series of videos prepared by SERIDA partner for the BRESOV project (GA 774244) The video briefly describes a test for resistance to BCMV, a common disease in bean crops</p> <p> </p> <p>https://www.youtube.com/watch?v=ukEVm_yC26Q</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.