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Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling Dataset for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Figure 5 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 5. Ordering by Detrended Correspondence Analyses (Axes 1 and 2) based on the cuticular hydrocarbon profile of the samples of the 3 species whose colonies were nested in two types of environments. The points on Axis 1, between 0 and 50 on the left, represent compounds more characteristic of samples from more anthropized environments and between 100 and 150 on the right, from less anthropized environments.
Figure 4 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 4. Similarity dendrogram based on the cuticular hydrocarbon profile of the samples of the 3 species whose colonies were nested in two types of environments. Pentagon: more anthropized areas. Star: less anthropized areas.
Figure 3 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 3. Bar charts showing the relative abundance and number of compounds belonging to the different classes of cuticular hydrocarbons present in the samples of Polistes versicolor (A), Polybia paulista (B) and Polybia occidentalis (C) whose colonies were nesting in more anthropized environments (black box) and less anthropized (white box). LA = Linear Alkanes, BA = Branched Alkanes, AK = Alkenes, ALD = Alkadienes.
Figure 2 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 2. Similarity dendrogram generated based on the percentages of different types of land use in the municipalities where the colonies of the 3 species of social wasps were sampled. Pentagon: more anthropized areas. Star: less anthropized areas. DDS1: Dourados point 1; DDS2: Dourados point 2; IVIN: Ivinhema; MN1: Mundo Novo point 1; MN2: Mundo Novo point 2; PP: Ponta Porã.
Figure 1 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 1. Satellite image showing the places in the municipalities where the colonies of the 3 social wasp species were nesting and, the percentages (pie charts) of the different types of land use and occupation (adapted from the IBGE, 2013 definition). Number 1 and 2 indicates the two collection points in the same city.
Fig. 2 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile
Fig. 2. – Theodor Kotschy's botanical field book, 1840–1842.
Do changes in body mass alter white blood cell profiles and immune function in Australian cane toads (Rhinella marina)?
<p><span>Variation in food resources can result in dramatic fluctuations in the body condition of animals dependent on those resources. Decreases in body mass can disrupt patterns of energy allocation and impose stress, thereby altering immune function. In this study we investigated links between changes in body mass of captive cane toads (Rhinella marina), their circulating white blood cell populations, and their performance in immune assays. Captive toads that lost weight over a 3-month period had increased levels of monocytes and heterophils and reduced levels of eosinophils. Basophil and lymphocyte levels were unrelated to changes in mass. Because individuals that lost mass had higher heterophil levels but stable lymphocyte levels, the ratio of these cell types was also higher, partially consistent with a stress response. Phagocytic ability of whole blood was higher in toads that lost mass, due to increased circulating levels of phagocytic cells. Other measures of immune performance were unrelated to mass change. These results highlight the challenges faced by invasive species as they expand their range into novel environments which may impose substantial seasonal changes in food availability that were not present in the native range. Individuals facing energy restrictions may shift their immune function towards more economical and general avenues of combating pathogens. </span></p>
Samoan Passage Moored Profiler Data Archive
<p>Samoan Passage Moored Profiler Data Archive. Please note that the copy here on zenodo contains only the GitHub repository, data are stored elsewhere.</p> <p>Head to <a href="https://github.com/gunnarvoet/sp-data-archive-mp">https://github.com/gunnarvoet/sp-data-archive-mp</a> for instructions on how to clone the full dataset or download data files manually at <a href="https://osf.io/7b8fn/">https://osf.io/7b8fn/</a>.</p>
Genome assemblies and respective cgMLST profiles of a diverse dataset comprising 1,874 Listeria monocytogenes isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 1,748-loci core-genome (cg) Multiple Locus Sequence Type (MLST) profiles [Pasteur schema (<a href="https://pubmed.ncbi.nlm.nih.gov/27723724/">Moura et al. 2016</a>) available in <a href="https://chewbbaca.online/species/6/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,874 <em>Listeria monocytogenes</em> samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of Sequence Type [ST]). In total, 204 different STs are represented in this dataset, with ST121, ST6, ST9, ST1 and ST155 being in the top 5 and, together, corresponding to 37.9% of the dataset.</p> <p>File “Lm_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Lm_profile.tsv” corresponds to a tab separated file with the 1,748-loci cgMLST profile of each isolate presented in the metadata file. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>L. monocytogenes </em>genome assemblies, we collected information about the genetic diversity (STs) of the isolates available at <a href="https://bigsdb.pasteur.fr/listeria/">BIGSdb-Lm</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,957 samples associated with three previous studies (<a href="https://pubmed.ncbi.nlm.nih.gov/27723724/">Moura et al. 2016</a>; <a href="https://pubmed.ncbi.nlm.nih.gov/28827366/">Maury et al. 2017</a>; <a href="https://pubmed.ncbi.nlm.nih.gov/30775964/">Painset et al. 2019</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,874 isolates passed the dataset curation step and were included in the final dataset. cgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 1,748-loci Pasteur schema (<a href="https://pubmed.ncbi.nlm.nih.gov/27723724/">Moura et al. 2016</a>) available in <a href="https://chewbbaca.online/species/6/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on June 23<sup>rd</sup>, 2022.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Seven Fluorescent proteins profile in Chlamydomonas reinhardtii and R code statistic for analysis
<p><strong>Overview</strong></p> <p>Data points present in this dataset were obtained following the protocol described in dx.doi.org/10.17504/protocols.io.kfnctme. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m<sup>2</sup>s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Supernatant samples were obtained by spinning Deep-well plates at 3000 × <em>g</em> for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.</p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3</p> <p>Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content</p> <p>Analysis_Raw_FP.xlsx -> File with raw values organized in a spreadsheet </p> <p>pRFU_<strong>FLUORESCENT PROTEIN</strong>_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii </em></p> <p>sup_RFU_<strong>FLUORESCENT PROTEIN_</strong>+_bk.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>who_RFU_<strong>FLUORESCENT PROTEIN_</strong>+_bk.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>pRFU_<strong>FLUORESCENT PROTEIN</strong>_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>sup_RFU_<strong>FLUORESCENT PROTEIN_</strong>+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>who_RFU_<strong>FLUORESCENT PROTEIN_</strong>+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p> </p> <p><strong>Consider citing our work. </strong></p> <p>1. Molino JVD, de Carvalho JCM, Mayfield S. Evaluation of secretion reporters to microalgae biotechnology: blue to red fluorescent proteins. Algal Res. 2018;31: 252–261. doi:10.1016/j.algal.2018.02.018</p>
Potential profile, design parameters and thermal properties of asymmetric double-barrier heterostructures based on AlGaAs simulated with NEGF+H
<p>This is a repository for assymmetric double barrier heterostructures that were simulated using the NEGF coupled with the heat equation described in <a href="https://doi.org/10.1103/PhysRevApplied.14.064022">BESCOND:Phys. Rev. Applied:2020</a>.</p> <ul> <li>Relation between the csv columns and variables:</li> </ul> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Variable</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>1</td> <td>Lb1 [nm] </td> <td>First barrier length</td> </tr> <tr> <td>2</td> <td>LQW [nm] </td> <td>Quantum well length</td> </tr> <tr> <td>3</td> <td>Lb2 [nm] </td> <td>Second barrier length</td> </tr> <tr> <td>4</td> <td>γ</td> <td>Fraction on Al in AlGaAs alloy</td> </tr> <tr> <td>5</td> <td>V [V] </td> <td>Bias between emitter and collector</td> </tr> <tr> <td>6</td> <td>CP [W/m²] </td> <td>Cooling power</td> </tr> <tr> <td>7</td> <td>Te [K] </td> <td>Electron temperature in the Quantum well</td> </tr> <tr> <td>8</td> <td>W1 [eV] </td> <td>First activation energy</td> </tr> <tr> <td>9</td> <td>W2 [eV] </td> <td>Second activation energy</td> </tr> <tr> <td>10-1525</td> <td>PP [eV]</td> <td>Potential profile</td> </tr> </tbody> </table> <p>These data was used to feed the machine learning workflow shared in <a href="https://gitlab.citius.usc.es/modev/coolML">https://gitlab.citius.usc.es/modev/coolML</a>.</p> <p><br>This work was supported by the Spanish MICINN/AEI, Xunta de Galicia, and FEDER Funds under Grant RYC-2017-23312, Grant PID2019-104834GB-I00, Grant PID2022-141623NB-I00, Grant PID2022-142709OB-C21/PID2022-142709OA-C22, Grant ED431F 2020/008, Grant ED431C 2022/16 and GELATO ANR project (ANR-21-CE50-0017).</p>
Data from: Oral exposure to benzalkonium chlorides in male and female mice reveals sex-dependent alteration of the gut microbiome and bile acid profile
<p>Benzalkonium chlorides (BACs) are commonly used disinfectants in a variety of consumer and food-processing settings, and the COVID-19 pandemic has led to increased usage of BACs. The prevalence of BACs raises the concern that BAC exposure could disrupt the gastrointestinal microbiota, thus interfering with the beneficial functions of the microbes. We hypothesize that BAC exposure can alter the gut microbiome diversity and composition, which will disrupt bile acid homeostasis along the gut-liver axis. In this study, male and female mice were exposed orally to d<sub>7</sub>-C12- and d<sub>7</sub>-C16-BACs at 120 µg/g/day for one week. UPLC-MS/MS analysis of liver, blood, and fecal samples of BAC-treated mice demonstrated the absorption and metabolism of BACs. Both parent BACs and their metabolites were detected in all exposed samples. Additionally, 16S rRNA sequencing was carried out on the bacterial DNA isolated from the cecum intestinal content. For female mice, and to a lesser extent in males, we found that treatment with either d<sub>7</sub>-C12- or d<sub>7</sub>-C16-BAC led to decreased alpha diversity and differential composition of gut bacteria with notably decreased actinobacteria phylum. Lastly, through a targeted bile acid quantitation analysis, we observed decreases in secondary bile acids in BAC-treated mice, which was more pronounced in the female mice. This finding is supported by decreases in bacteria known to metabolize primary bile acids into secondary bile acids, such as the families of Ruminococcaceae and Lachnospiraceae. Together, these data signify the potential impact of BACs on human health through disturbance of the gut microbiome and gut-liver interactions.</p>
Culex pipiens merged anvi'o profiles from midgut and ovary metagenomes
<p>Anvi’o merged profile databases for <em>Culex pipiens</em> midgut and ovary samples. </p>
Mutational Profiling in Epithelial Ovarian Cancer: A Meta-Analysis of 435 Patients Dataset
<p><strong>Data Description</strong></p> <p>0 represents no mutation, 1 represents a mutation</p> <p>Survive relates to the survival outcome of the patient. SurviveTime is in days (7300 chosen for alive patients).</p> <p>Ovarian Cancer Patients</p> <ul> <li>361 count</li> <li>TP53, BRCA1, BRCA2, KMT2C, CDK12, CSMD3, TTN, MUC16, NF1, PALB2, RB1, PTEN, FOXM1, BRIP1, KRAS, RASA1, CHEK2, RAD50, RAD51C/D, MRE11A, NBN, FANCE, FANCD2, ATR, ATM, MSH2, PMS2, MLH1, MSH6, FAT1, FAT2, FAT3, FAT4</li> </ul> <p>Uterine Cancer Patients</p> <ul> <li>437 count</li> <li>MSH6, CDK12, PMS2, BRIP1, FAT1, CSMD3, FAT4, BRCA2, MUC16, PALB2, KRAS, BRCA1, MSH2, PTEN, TP53, NF1, KMT2C, FAT3, FANCD2, ATM, NBN, ATR, CHEK2, FANCE, MLH1</li> </ul> <p>Cervical Cancer Patients</p> <ul> <li>174 count</li> <li>CSMD3, CDK12, FAT1, BRIP1, RB1, FAT4, BRCA2, MUC16, PALB2, KRAS, TP53, PTEN, KMT2C, FAT3, BRCA1, NF1, ATM, FANCD2, NBN, ATR, MLH1</li> </ul>
Supplement: quadT: How Profilers Can Help Navigate Type Migration
<ul> <li>uquadT.tar.gz = boundary profile (aka contract profile)</li> <li>17-* = performance data</li> <li>uquadT_profile.tar.xz_* = statistical profile <ul> <li>This data is divided across 4 files thanks to the unix <code>split</code> command. Use <code>cat</code> to combine, then untar.</li> </ul> </li> </ul> <p>Reports on 3^14 = 4,782,969 configurations of quadT, so there should be that many files in the two profile folders and that many lines of running times in the .out file.</p>
Spatial profiling of benign and malignant melanocytic tumors via RNA-SMI (CosMx)
<p>Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we utilized single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi, primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative 2D-analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included upregulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 melanoma patients via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene regulatory landscapes pertinent to cancer progression and patient survival.</p>
Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours
<p><strong>Aims/hypothesis</strong>: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and subsequently type 2 diabetes. In this study, we explored the effects of <em>in vitro</em> hyperglycemic conditions on human pancreatic islet gene expression across 24 hours in six pancreatic cell types: alpha, beta, gamma, delta, ductal, and acinar cells. We hypothesized that genes associated with hyperglycemic conditions may be relevant to the onset and progression of diabetes.</p> <p><strong>Methods</strong>: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 hours <em>in vitro</em>. To assess the transcriptome, we performed single-cell RNA sequencing (scRNA-seq) at seven time points. We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterized the effect on insulin production and secretion using CRISPR interference to knockdown gene expression in EndoC-βH1 cells, followed by a glucose-stimulated insulin secretion assay.</p> <p><strong>Results</strong>: Across all cell types, we identified 1,447 genes associated with time, 680 genes associated with glucose exposure, and 418 genes associated with interaction effects between time and glucose. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2,449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose, and fasting blood glucose. Of these candidate effector genes, we showed that three—<em>ERO1B</em>, <em>HNRNPA2B1</em>, and <em>RHOBTB3</em>—exhibited an effect on glucose-stimulated insulin secretion and production in EndoC-βH1 cells.</p> <p><strong>Conclusions/interpretation</strong>: The findings of our study provide an in-depth characterization of the 24-hour transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production.</p>
Methylation profiling of Waldenstorm's Macroglobulinemia
<p><span>Currently, the role of DNA methylation in the IgM-monoclonal gammopathy disease spectrum remains poorly understood. In the present study, a multi-omics prospective analysis was conducted integrating DNA methylation, RNA-seq and WES data in 34 subjects [23 WM, 6 IgM-MGUS, 5 normal controls]. Analysis was focused on defining differences between IgM-gammopathies (WM/IgM-MGUS) compared to controls, and specifically between WM and IgM-MGUS. Between groups, genome-wide DNA methylation analysis demonstrated a significant number of differentially methylated regions which were annotated according to genomic region. Next, integration of RNA-seq data was performed to identify potentially epigenetically deregulated pathways. We found that pathways involved in cell cycle, metabolism, cytokine/immune signaling, cytoskeleton, tumor microenvironment, and intracellular signaling were differentially activated and potentially epigenetically regulated. Importantly, there was a positive enrichment of CXCR4 signaling pathway along with several interleukin (IL-6, IL-8, IL15) signaling pathways in WM compared to IgM-MGUS. Further assessment of known tumor suppressor genes and oncogenes uncovered differential promoter methylation of several targets with concordant change in gene expression, including <em>CCND1</em> and <em>CD79B</em>. Overall, this report define how aberrant DNA methylation in IgM-gammopathies may play acritical role in the epigenetic control of oncogenesis and key cellular functions. </span></p>
Shear wave velocity profiles from Italian Seismic Microzonation project
<p>The dataset SMDB contains the shear wave velocity profiles from Italian Seismic Microzonation project (14,897 profiles).</p> <p>The records are:</p> <p>-survey id;</p> <p>-survey latitude and longitude in UTM33N coordinates;</p> <p>-survey type;</p> <p>-depth in meters;</p> <p>-shear wave velocity value (Vs) in m/s;</p> <p>-seismic microzonation (SM) cluster id.</p> <p>The file percentile_sigma_ln_vs.csv contain sigma lnvs in depth for the three percentiles 16,50,84.</p>
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
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.