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3,576 results for “strain”
Data and code for "Symmetries in TEM imaging of crystals with strain"
<p>Code and data related to manuscript "On Symmetries in TEM imaging of crystals with strain",<br> arXiv preprint arXiv:2206.01689. To be published in Proc. Royal. Soc. A</p> <p> </p>
Gene annotation of complete genome sequence of Achromobacter sp. strain E1
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Sliding trajectories of strained Graphullerene/graphene and Graphullerene/h-BN heterojunction
<p>This dataset contains the sliding trajectories of strained GF/graphene and GF/<em>h</em>-BN along three directions (including total energy and atomic coordinates in extend xyz format) described in the manuscript "Superlubric Graphullerene" (to be submitted). </p>
Functional profiles of 14 strains of Salinicola using EggNOG online service
<p>Functional profiles of 14 Salinicola strains using the EggNOG online service (http://eggnog-mapper.embl.de).</p> <p>The zip file contains tsv and xlsx files generated using eggNOG to classify the gene-encoding proteins according to the next EggNOG categories:</p> <p>*Information Storage and Processing*<br>A) RNA processing and modification <br>B) Chromatin structure and dynamics<br>J) Translation, ribosomal structure and biogenesis<br>K) Transcription<br>L) Replication, recombination and repair <br>*Cellular processes and signaling*<br>D) Cell cycle control, cell division, chromosome partitioning <br>M) Cell wall/membrane/envelope biogenesis <br>N) Cell motility <br>O) Posttranslational modification, protein turnover, chaperones <br>T) Signal transduction mechanisms <br>U) Intracellular trafficking, secretion, and vesicular transport <br>V) Defense mechanisms <br>*Metabolism*<br>C) Energy production and conversion <br>E) Amino acid transport and metabolism <br>F) Nucleotide transport and metabolism<br>G) Carbohydrate transport and metabolism <br>H) Coenzyme transport and metabolism <br>I) Lipid transport and metabolism <br>P) Inorganic ion transport and metabolism <br>Q) Secondary metabolites biosynthesis, transport and catabolism <br>*Poorly characterized<br>S) Function unknown </p> <div> <pre>-. .-. .-. .-. .-. .-. . ||\|||\ /|||\|||\ /|||\|||\ /| |/ \|||\|||/ \|||\|||/ \|||\|| ~ `-~ `-` `-~ `-` `-~ `- </pre> </div> <p>Profiles generated on 3 June 2024</p> <p> </p>
Dataset from the first wave of a pre-post study to test an interactive blended learning tool for delirium management in Belgian Nursing Homes by measuring delirium knowledge and strain of care for delirium
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Strain partitioning, transfer and implications for ongoing processes of the intra-continental graben formation in the NW margin of the Ordos block, China: insights from densified GNSS measurements
<p><strong><span>GNSS data and processing</span></strong></p> <p><strong><span>1. Intensive observations</span></strong></p> <p><span>In this study, in addition to GNSS data surveyed around the Ordos block provided by <span>Hao et al. (2021)</span>, we collected GNSS data surveyed west of 103°E from </span><span>Phases</span><span> I and II of <span>the Crustal Movement Observation Network of China (CMONOC)</span></span><span>, which</span><span> has conducted measurements every one or two years from 1999 to 2019.</span><span> In particular</span><span>, we also collected data from 70 GNSS sites </span><span>deployed by<span> <a name="OLE_LINK49"></a><a name="OLE_LINK50"></a><a name="OLE_LINK82"></a><span><span>the National Geodetic Control Network of China (NGCNC)</span></span>. These sites were distributed in the northwestern boundary of the Ordos block and the interior of the Alashan and Ejina blocks and </span></span><span>first</span><span> measured 72 hours of data in 2014 or 2015. From September to October 2021, we conducted the second set of measurements for these GNSS sites of 96 continuous hours.</span></p> <p><span> </span></p> <p><strong><span>2. Data processing</span></strong></p> <p><span>T</span><span>he GAMIT </span><span>package </span><span>(</span><span>Herring et al., 2015</span><span>a</span><span>) </span><span>was employed </span><span>to process</span><span> the </span><span>double-differenced carrier phase observations</span><span>, and daily </span><span>loosely constrained solutions</span><span> were obtained</span><span>. </span><span>The primary geophysical models and parameters used in the processing, such as</span><span> the </span><span>FES2004 tidal loading model, GPT2 global pressure and temperature model (</span><span>Lagler et al., 2013</span><span>), and VMF1 </span><span>mapping</span><span> function (</span><span>Boehm et al., 2006</span><span>),</span><span> are</span><span> the</span><span> same </span><span>as </span><span>those in </span><span>Hao et al. (2021)</span><span>. Instead of incorporating global daily solutions provided by the IGS analysis centers, we also processed data from ~</span><span>70 evenly distributed global International Terrestrial Reference Frame (ITRF) core tracking </span><span>GNSS sites</span><span> using the GAMIT package with the same models. </span><span>We estimated</span><span> daily coordinates and uncertainties by constraining the daily regional solutions with global solutions, and the daily free network solutions were transformed into </span><span>the </span><span>ITRF2014 (</span><span>Altamimi et al., 2017</span><span>) </span><span>reference frame by utilizing</span><span> the GLOBK package</span><span> (</span><span>Herring et al., 2015</span><span>b</span><span>)</span><span>.</span><span> For campaign GNSS sites, the weighted least-squares adjustment was used to solve the linear velocity with respect to the ITRF2014.</span></p> <p><span>To analyze the differential crustal motion between blocks, we selected the stable Ordos block as the regional reference frame and transformed the GNSS velocity field referenced to the ITRF2014 into the Ordos-fixed frame through Euler rotation. The absolute Euler pole of the Ordos block is located at 75.083°N ± 0.850°</span><span> and</span><span> 139.692°W ± 3.701°, with an angular rotation rate of 0.352 ± 0.</span><span>006°/Ma </span><span>(</span><span>Hao et al., 2021</span><span>).</span></p> <p><span> </span></p> <p><strong><span>References</span></strong></p> <p><span>Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model.Geophys. J. Int. 209:1906–1912</span></p> <p><span>Boehm, J., Werl, B., and Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. Journal of Geophysical Research, 111, B0240</span><span><a href="https://doi.org/10.1029/2005JB003629"><span>. doi:10.1029/2005JB003629</span></a></span><span>.</span></p> <p><span>Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021.“Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008.</span></p> <p><span>Herring, T.A., King, R.W., McClusky, S.C., 2015b.GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6.Massachusetts Institute of Technology, Cambridge.</span></p> <p><span>Herring, T.A., King, R.W.,McClusky, S.C., 2015a.GAMIT reference manual, GPS analysis at MIT, Release 10.6.Massachusetts Institute of Technology, Cambridge.</span></p> <p><span>Lagler, K., Schindelegger, M., Böhm, J., Kr</span><span>á</span><span>sn</span><span>á</span><span>, H., and Nilsson, T. (2013). GPT2: empirical slant delay model for radio space geodetic techniques. GeophysicalResearchLetters, 40, 1069-1073. doi: 10.1002/grl.50288.</span></p>
Contrasting disease progression, microglia reactivity, tolerance, and resistance to Toxoplasma gondii infection in two mouse strains
<p><strong><span>Figure S1</span></strong><span>. Stereological Sampling: Low-power photomicrographs (A and B) displaying the molecular layer of the dentate gyrus, the area of interest, alongside the sampling grid (C). Additionally, a high-power photomicrograph (D) showcases IBA-1 immunolabeled microglia (the object of interest). Scale bar: A - 250µm, and D: 25µm.</span></p> <p> </p> <p><strong><span>Figure S2</span></strong><span>. The recovery of BALB/c microglia in the molecular layer of dentate gyrus was observed 43 days after infection. At this point, only minor morphological changes were observed, and all the morphological changes induced by <em>T. gondii</em> infection at 22 dpi have disappeared. The analysis methods used included hierarchical cluster analysis (A), discriminant function analysis (C and E), morphological complexity (B), and convex hull volume (D).</span></p>
Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</p>
Uncatalyzed Diboron Activation by a Strained Hydrocarbon: Experimental and Theoretical Study of [1.1.1]Propellane Diborylation
<p>Raw data for the Article "Uncatalyzed Diboron Activation by a Strained Hydrocarbon:<br>Experimental and Theoretical Study of [1.1.1]Propellane Diborylation" (https://doi.org/10.1002/chem.202402152)</p>
Data of "Ultra-thin Strain Hardening Cementitious Composite (SHCC) layer in reinforced concrete cover zone for crack width control"
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Strain fingerpringting of exciton valley character in 2D semiconductors
<p>Raw data which are used to generate the figures in the publication 'Strain fingerpringting of exciton valley character in 2D semiconductors'.</p> <p>Abstract of the paper:</p> <p>Intervalley excitons with electron and hole wavefunctions residing in different valleys determine the long-range transport and dynamics observed in many semiconductors. However, these excitons with vanishing oscillator strength do not directly couple to light and, hence, remain largely unstudied. Here, we develop a simple nanomechanical technique to control the energy hierarchy of valleys via their contrasting response to mechanical strain. We use our technique to discover previously inaccessible intervalley excitons associated with K, Γ, or Q valleys in prototypical 2D semiconductors WSe2 and WS2. We also demonstrate a new brightening mechanism, rendering an otherwise “dark” intervalley exciton visible via strain-controlled hybridization with an intravalley exciton. Moreover, we classify various localized excitons from their distinct strain response and achieve large tuning of their energy. Overall, our valley engineering approach establishes a new way to identify intervalley excitons and control their interactions in a diverse class of 2D systems.</p>
Fig 3. Chaetoceros decipiens. Strain D10 in Diversity in the Globally Distributed Diatom Genus Chaetoceros (Bacillariophyceae): Three New Species from Warm-Temperate Waters
Fig 3. Chaetoceros decipiens. Strain D10, SEM (A–C) and TEM (D). A: Solitary cell with silica fringes. B: Intercalary cells with overlapping silica membrane (arrow). C: Detail of fused seta bases, silica membrane and fringes on the mantle (arrowhead). D: Rows of poroids on the mantle. A and B scale bars, 10 μm. C and D scale bars, 2 μm. doi:10.1371/journal.pone.0168887.g003
Data for the article "Elastic strain engineering for ultralow mechanical dissipation"
<p>Device fabrication masks, raw experimental data and data processing scripts</p>
Bamfiles DNA sequencing reads HCT strain Fo1A and Fo2A
<p>Reads obtained from DNA sequencing of two strains resulting from the horizontal chromosome transfer experiment described in [doi: 10.1038/nature08850] and submitted to ENA [PRJEB29294] . For each strains two paired-end libraries were generated, one with ifragment length of about 170 bp and one with fragmentlength of 500 bp. They were first mapped to the genome of Fo47, unmapped reads were subsequently mapped to the genome of Fol4287, as described in [doi: 10.1101/465070]. Putative PCR duplicates were removed using Picard tools and resulting bamfiles (one for the 170 fragment library and one for the 500 bp fragment library) were merged.</p> <p>These files are the resulting bamfiles.</p> <p>Fo1A_L170_L500.unmapped_to_Fo47illumina.Fol4287broad.bowtie2.sorted_dedup.bam (one chromosome: chr 14)</p> <p>Fo2A_L170_L500.unmapped_to_Fo47illumina.Fol4287broad.bowtie2.sorted_dedup.bam (two chromosomes: chr 14 + small chromosome)</p>
The data and analysis code used to produce the nanomechanical plots in "Stress control of tensile-strained In_{1-x}Ga_{x}P nanomechanical string resonators"
<p>Data and code for figures in "Stress control of tensile-strained In_{1-x}Ga_{x}P nanomechanical string resonators"</p>
Data for "In-situ strain tuning in hBN-encapsulated graphene electronic devices"
<p>Data for the publication "In-situ strain tuning in hBN-encapsulated graphene electronic devices"</p>
Data for "Mobility enhancement in graphene by in situ reduction of random strain fluctuations"
<p>Data for "Mobility enhancement in graphene by in situ reduction of random strain fluctuations"</p>
Benchmarking datasets used in the manuscript "HyLight: Strain aware assembly of low coverage metagenomes"
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Creating a 7,000 strains genotype-phenotype dataset of E. coli and antimicrobial resistance phenotypes
<h1>Description</h1> <p>This Zenodo repository contains the data (except for the input fastq files available on SRA and intermediary files generated during the variant calling process) and code to recapitulate the study from <a href="https://doi.org/10.57844/arcadia-d2cf-ebe5">https://doi.org/10.57844/arcadia-d2cf-ebe5</a> and the associated GitHub repository, where the code, pipelines, and analysis are described in more detail.</p> <p> </p> <h1>Work summary</h1> <p>In this work, we established a framework for compiling large genotype-phenotype datasets and produced a large-scale dataset of more than 7,000 <em>E. coli</em> strains and antimicrobial resistance phenotypes.</p> <p>We leveraged the genetic information and antimicrobial resistance (AMR) phenotype data available for the bacterium <em>Escherichia coli</em> to construct our dataset and took advantage of the existing knowledge about genetic variations and AMR phenotypes to validate our approach and dataset. We performed variant calling and compiled a genotype-phenotype dataset for more than 7,000 <em>E. coli</em> strains. Briefly, variant calling consists of identifying all genetic variations and their associated genotypes in a population compared to a reference genome. This is performed by aligning sequencing reads for each strain of the population against a reference genome, then identifying polymorphic regions in the population, and finally characterizing variants and their genotypes at each of these polymorphic regions.</p> <p>We have generated a dataset that successfully revealed significant genetic diversity and identified 2.4 million variants. By focusing on non-silent variants within genes associated with AMR, we confirmed the dataset's accuracy. </p> <p>We hope this study is a foundational resource for conducting large-scale genotype-phenotype studies that will offer valuable insights for genetics investigations, informing the development of treatments and prevention strategies for AMR. This resource is invaluable for microbiologists and epidemiologists seeking to understand AMR mechanisms and improve genotype-phenotype predictions in pathogenic <em>E. coli</em> outbreaks. Additionally, it's of particular interest to geneticists and evolutionary biologists, providing a dataset to develop strategies for studying genetic interactions and broader applications in phenotype-phenotype predictions and phylogenetic research.</p> <h1>Data organization</h1> <p>Data are organized in the compressed folder. Briefly, they’re divided into two main folders.</p> <p>The first folder, <strong>dataset_generation</strong>, includes the code and information necessary to build the genotype dataset and perform the variant calling. It covers major steps like the generation of the reference pangenome used for variant calling, the variant calling pipeline applied to each of the 7,000 strains, the filtering of false positive variants, and the annotation of the variants. </p> <p>The second section, <strong>dataset_analysis</strong>, includes the code and information used to process and analyze the dataset and generate figures for the Pub (https://doi.org/10.57844/arcadia-d2cf-ebe5). It includes the preliminary analysis of AMR phenotypes within the population and the analysis of variants regarding known AMR phenotypes.</p> <p> </p> <h1>Files description</h1> <p>The following table provides a list and description of the different files and their locations.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Location</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>variant_calling_pipeline</td> <td>dataset_generation/scripts/</td> <td>Snakefile: performs variant calling from raw paired-end sequencing files and generate one vcf.gz file per sample</td> </tr> <tr> <td>snakemake_ECOR72_annotation</td> <td>Snakefile: performs Prokka annotation on inputs whole genome fastq files</td> </tr> <tr> <td>ECOR72_and_DP_threshold_analysis.Rmd</td> <td>R markdown: analyses the coverage of known present and absebt loci in the ECOR population</td> </tr> <tr> <td>average_coverage_41.csv</td> <td>dataset_generation/data/dp_threshold/</td> <td>Pangenome loci read coverage information for 40 ECOR strains</td> </tr> <tr> <td>average_coverage_last32.csv</td> <td>Pangenome loci read coverage information for 32 ECOR strains</td> </tr> <tr> <td>whole_pan_ecor_presence_absence.csv</td> <td>Reformated pangenome loci presence-absence in ECOR strains</td> </tr> <tr> <td>pangenome_genomes_SRA_GCA.csv</td> <td>Correspondance table between ECOR72 strains genome names and raw sequencing files SRA accession number</td> </tr> <tr> <td>index_loci_pangenome_good.txt</td> <td>List of indexed positions in the pangenome</td> </tr> <tr> <td>list_ecor_txtfiles.txt</td> <td>List of txt files (containing the DP information per nucleotide) to use - This corresponds to the files for each 72 ECOR strains</td> </tr> <tr> <td>ECOR72_SRA_and_assembly_accessions.csv</td> <td>dataset_generation/data/</td> <td>List of Genome accession number and the SRA accession number of the associated sequencing files for the 72 ECOR strains</td> </tr> <tr> <td>sample_list_SRA.csv</td> <td>List of SRA accession numbers of the E. coli strains used for variant calling</td> </tr> <tr> <td>gene_presence_absence.csv</td> <td>dataset_generation/results/pangenome_cds/</td> <td>Roary output of presence-absence of the pangenome cds loci in the ECOR72 strains</td> </tr> <tr> <td>genes.gff</td> <td>Annotation file of the pangenome cds sequences (Prokka output)</td> </tr> <tr> <td>pangenome_cds.fa</td> <td>Roary output cds_pangenome sequencing file</td> </tr> <tr> <td>summary_statistics.txt</td> <td>Roary statistics output of creations of the cds pangenome</td> </tr> <tr> <td>roary_output</td> <td>Roary output folder</td> </tr> <tr> <td>IGR_presence_absence.csv</td> <td>dataset_generation/results/pangenome_igr/</td> <td>Piggy output of presence-absence of the pangenome igr loci in the ECOR72 strains</td> </tr> <tr> <td>pangenome_igr.fasta</td> <td>Piggy output igr_pangenome sequencing file</td> </tr> <tr> <td>piggy_output</td> <td>Piggy output folder</td> </tr> <tr> <td>whole_pangenome.fasta</td> <td>dataset_generation/results/pangenome_whole/</td> <td>whole pangenome sequences</td> </tr> <tr> <td>annot_summary_filtered.html</td> <td>dataset_generation/results/vcf/</td> <td>Summary of snpEff annotations</td> </tr> <tr> <td>annotated_output.vcf.gz</td> <td>snpEff annotated vcf file</td> </tr> <tr> <td>annotated_output.vcf.gz.csi</td> <td>indexed annotated vcf file</td> </tr> <tr> <td>filtered_output.vcf.gz</td> <td>filtered vcf file (removed low coveraged and low quality variants)</td> </tr> <tr> <td>filtered_output.vcf.gz.csi</td> <td>indexed filtered vcf files</td> </tr> <tr> <td>output.non_silent.vcf.gz</td> <td>vcf file containing only the nonsilent variants in the pangenome cds loci</td> </tr> <tr> <td>merged_output_listN.vcg.gz</td> <td>intermediary vcf files of 1000 merged strains vcf - these intermediary merged files are numbered from 1 to 7</td> </tr> <tr> <td>merged_output_all.vcf.gz</td> <td>final vcf.gz files of all merged vcf files in this study</td> </tr> <tr> <td>List_N_merging.txt</td> <td>dataset_generation/data/vcf_merging/</td> <td>List of the 1000 vcf.gz files to be merged together. There are 7 lists, numbered from 1 to 7</td> </tr> <tr> <td>ecor72_array.txt</td> <td>dataset_generation/results/ecor72_DP/</td> <td>Consolidate DP information per nucleotide for each ECOR strain</td> </tr> <tr> <td>variants_pos.tsv</td> <td>dataset_analysis/data/variant_analysis/</td> <td>List of all the variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_freqs.txt</td> <td>Variant frequency informations</td> </tr> <tr> <td>variants_non_silent_pos.tsv</td> <td>List of all the non-silent variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_non_silent_freqs.txt</td> <td>Non-silent variant frequency informations</td> </tr> <tr> <td>cds_eggNog.tsv</td> <td>eggNog output file of the pangenome annotation</td> </tr> <tr> <td>COG_functional_categories.csv</td> <td>Correspondance between COG functional categories and higher-order annotation</td> </tr> <tr> <td>BVBRC_genome_May31.csv</td> <td>dataset_analysis/data/dataset_analysis</td> <td>List of E. coli with available genomes as reported in BCBRV database</td> </tr> <tr> <td>BVBRC_genome_amr_May31.csv</td> <td>E. coli antimicrobial resistance information available in BCBRV database</td> </tr> <tr> <td>antibiotic_class.csv</td> <td>Antibiotic name and Antibiotic class information</td> </tr> <tr> <td>resistance_output.non_silent.vcf.gz</td> <td>dataset_analysis/data/antimicrobial_resistance_analysis</td> <td>vcf.gz file of the loci expected to be associated with antimicrobial resistance</td> </tr> <tr> <td>antibiotic_resistance_freq.csv</td> <td>Frequency information for the non-silent variant in the selected antimicrobial genes</td> </tr> <tr> <td>SRA_to_genome_name.csv</td> <td>correspondence between strain SRA accession number and genome name (as reported in BVBRC)</td> </tr> <tr> <td>Dataset_metainfo_AMR_analysis.Rmd</td> <td>dataset_analysis/scripts</td> <td>R markdown: conducts the characterization of the population and analysis of the AMR phenotype distribution</td> </tr> <tr> <td>Variant_population_analysis.Rmd</td> <td>R markdown: conducts the analysis and investigation of identified variants in the population</td> </tr> <tr> <td>Antimicrobial_resistance_investigation.Rmd</td> <td>R markdown: conducts the antimicrobial resistance investigation </td> </tr> </tbody> </table> <p> </p> <p> </p>
FIGURES 29–35. Entomoneis tenera strain PMFEN2 in Entomoneis tenera sp. nov., a new marine planktonic diatom (Entomoneidaceae, Bacillariophyta) from the Adriatic Sea
FIGURES 29–35. Entomoneis tenera strain PMFEN2, SEM and TEM. VC-valvocopula; C-copula. Girdle views (Figs 29–35). (29) Frustule with the girdle. (30) Fine structure of the copulae. (31) Fine structure of valvocopulae with teardrop shaped areolae and interareolae thickenings (arrow). (32) Cingulum. (33) Valve with cingulum and decussate appearance of the costae on the valve between valvocopulae (arrowhead) and junction line. (34, 35) Fine structure of copulae. Scale bars: Figs 29, 30=2 μm: Figs 32, 33=1 μm; Figs 31, 34, 35=300 nm.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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