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Fig. 1 in Defensive warning behavior expressed by three species of polistine wasps
Fig. 1. Diagram of foraging area, nest yard, and sting threshold point. Foraging territory is the area surrounding the nest in which colony workers scout for and obtain their prey, paper, and other resources. Outside the nest yard, they remain non-defensive unless they are subjected to personal threat. The nest yard includes the area around the nest but outside of the sting threshold point in which warning behaviors are expressed. An intruder who approaches the nest in the area within the sting threshold point is likely to experience stinging behavior.
Fig. 2 in Defensive warning behavior expressed by three species of polistine wasps
Fig. 2. Flow chart of defensive behavior expressed by polistine wasp workers toward vertebrate intrusion, as determined in this research and other investigations (Hermann 2017; Hermann & Dirks 1974). As initial lines of defense, aposematic coloration and pattern, along with nest location, offer some degree of colony protection. Once an intruder enters the nest yard, warning expressions are displayed by workers. Size of the nest yard and the stinging threshold point generally are species specific. Following the initial pretarsal twitching and the more obvious antennal raising and wing spreading, a wide array of defensive expressions ensue, terminating in stinging flights. There also are some interspecific differences in the display of certain warning expressions, as discussed in the text.
Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos / Practical Transcriptomics: Differential gene expression applied to food production
<p>Data for the eLearning tutorial Practical Transcriptomics: Differential gene expression applied to food production</p> <p>Datos para el tutorial eLearning Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos</p>
Facial Expression and Landmark Tracking (FELT) dataset
<p><strong>Contact Information</strong></p> <p>If you would like further information about the Facial expression and landmark tracking data set, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>. </p> <p><strong>Facial Expression examples</strong></p> <p>Watch a sample of the <a href="https://youtu.be/OA5gZ6iycPc">facial expression tracking results</a>.</p> <p><strong>Commercial Licenses</strong></p> <p>Commercial licenses for this dataset can be purchased. For more information, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>.</p> <p><strong>Description</strong></p> <p>The <a href="https://affectivedatascience.com/datasets.html#felt">Facial Expression and Landmark Tracking (FELT) dataset</a> dataset contains tracked facial expression movements and animated videos from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [<a href="../record/1188976">RAVDESS Zenodo page</a>]. Tracking data and videos were produced by Py-Feat 0.6.2 (2024-03-29 release) (Cheong, J.H., Jolly, E., Xie, T. <em>et al.</em> Py-Feat: Python Facial Expression Analysis Toolbox. <em>Affec Sci</em> <strong>4</strong>, 781–796 (2023). https://doi.org/10.1007/s42761-023-00191-4) and custom code (<a href="https://github.com/harveyliao/Py-feat-RAVDESS">github repo</a>). Tracked information includes: facial emotion classification estimates, facial landmark detection (68 points), head pose estimation (yaw, pitch, roll, x, y), and facial Action Unit (AU) recognition. Videos include: landmark overlay videos, AU activation animations, and landmark plot animations.</p> <p>The FELT dataset was created at the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>.</p> <p>This dataset contains tracking data and videos for all 2452 RAVDESS trials. Raw and smoothed tracking data are provided. All tracking movement data are contained in the following archives: raw_motion_speech.zip, smoothed_motion_speech.zip, raw_motion_song.zip, and smoothed_motion_song.zip. Each actor has 104 tracked trials (60 speech, 44 song). Note, there are no song files for Actor 18.</p> <p>Total Tracked Files = (24 Actors x 60 Speech trials) + (23 Actors x 44 Song trials) = 2452 CSV files.</p> <p>Tracking results for each trial are provided as individual comma separated value files (CSV format). File naming convention of raw and smoothed tracked files is identical to that of the RAVDESS. For example, smoothed tracked file "01-01-01-01-01-01-01.csv" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4". For a complete description of the RAVDESS file naming convention and experimental manipulations, please see the <a href="../record/1188976">RAVDESS Zenodo page</a>. </p> <p>Landmark overlays, AU activation, and landmark plot videos for all trials are also provided (720p h264, .mp4). Landmark overlays present tracked landmarks and head pose overlaid on the original RAVDESS actor video. As the RAVDESS does not contain "ground truth" facial landmark locations, the overlay videos provide a visual 'sanity check' for researchers to confirm the general accuracy of the tracking results. Landmark plot animations present landmarks only, anchored to the top left corner of the head bounding box with translational head motion removed. AU activation animations visualize intensity of AU activations (0-1 normalized) as a heatmap over time. The file naming convention of all videos also matches that of the RAVDESS. For example, "Landmark_Overlay/01-01-01-01-01-01-01.mp4", "Landmark_Plot/01-01-01-01-01-01-01.mp4", "ActionUnit_Animation/01-01-01-01-01-01-01.mp4", all correspond to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4".</p> <p><strong>Smoothing procedure<br></strong></p> <p>Raw tracking data were first low-pass filtered with a 5th order butterworth filter (cutoff_freq = 6, sampling_freq = 29.97, order = 5) to remove high-frequency noise. Data were then smoothed with a Savitzky-Golay filter (window_length = 11, poly_order = 5). Scipy.signal (v 1.13.1) was used for both procedures.</p> <p><strong>Landmark Tracking models</strong></p> <p>Six separate machine learning models were used by Py-Feat to perform various aspects of tracking and classification. Video outputs generated by different combinations of ML models were visually compared, with final model choice determined by voting of first and second authors. Models were specified in the call to <code>Detector</code> class (<a href="https://py-feat.org/basic_tutorials/02_detector_imgs.html?highlight=facescore">described here</a>). Exact function call as follows:</p> <p><code> Detector(face_model='img2pose',</code><br><code> landmark_model='mobilenet',</code><br><code> au_model='xgb',</code><br><code> emotion_model='resmasknet',</code><br><code> facepose_model='img2pose-c',</code><br><code> identity_model='facenet',</code><br><code> device='cuda',</code><br><code> n_jobs=1,</code><br><code> verbose=False,</code><br><code> )</code></p> <p>Default Py_feat parameters to each model were used in most cases. Non-defaults were specified in the call to <code>detect_video</code> function (<a href="https://py-feat.org/pages/api.html?highlight=detect_video">described here</a>). Exact function call as follows:<br><br><code> (video_path,</code><br><code> skip_frames=None,</code><br><code> output_size=(720, 1280),</code><br><code> batch_size=5,</code><br><code> num_workers=0,</code><br><code> pin_memory=False,</code><br><code> face_detection_threshold=0.83,</code><br><code> face_identity_threshold=0.8</code><br><code> )</code></p> <p><strong>Tracking File Output Format</strong></p> <p>This data set retained Py-Feat's data output format. The resolution of all input videos was 1280x720. Tracking output units are in pixels, their range of values is (0,0) (top left corner) to (1280,720) (bottom right corner). </p> <p><em>Column 1 = Timing information</em></p> <ul> <li>1. frame - The number of the frame (source videos 29.97 fps), range = 1 to n</li> </ul> <p><em>Columns 2-5 = Head bounding box<br></em></p> <ul> <li>2-3. FaceRectX, FaceRectY - X and Y coordinates of top-left corner of head bounding box (pixels)</li> <li>4-5. FaceRectWidth, FaceRectHeightF - Width and Height of head bounding box (pixels)</li> </ul> <p><em>Column 6 = Face detection confidence<br></em></p> <ul> <li>FaceScore - Confidence level that a human face was deteceted, range = 0 to 1</li> </ul> <p><em>Columns 7-142 = Facial landmark locations in 2D<br></em></p> <ul> <li>7-142. x_0, ..., x_67, y_0,...y_67 - Location of 2D landmarks in pixels. A figure describing the landmark index <a href="https://raw.githubusercontent.com/TadasBaltrusaitis/OpenFace/master/imgs/landmark_scheme_68.png">can be found here</a>.</li> </ul> <p><em>Columns 143-145 = Head pose</em></p> <ul> <li>143-145. Pitch, Roll, Yaw - Rotation of the head in degrees (<a href="https://py-feat.org/basic_tutorials/02_detector_imgs.html?highlight=facerect#:~:text=single_face_prediction.poses%20%23%20(in%20degrees)">described here</a>). The rotation is in world coordinates with the camera being located at the origin.</li> </ul> <p><em>Columns 146-165 = </em><em>Facial Action Units</em></p> <p>Facial Action Units (AUs) are a way to describe human facial movements (Ekman, Friesen, and Hager, 2002) [<a href="https://en.wikipedia.org/wiki/Facial_Action_Coding_System">wiki link</a>]. More information on Py-Feat's implementation of AUs <a href="https://py-feat.org/pages/au_reference.html">can be found here</a>.</p> <ul> <li>145-150, 152-153, 155-158, 160-165. AU01, AU02, AU04, AU05, AU06, AU09, AU10, AU12, AU14, AU15, AU17, AU23, AU24, AU25, AU26, AU28, AU43 - Intensity of AU movement, range from 0 (no muscle contraction) to 1 (maximal muscle contraction).</li> <li>151, 154, 159. AU07, AU11, AU20 - Presence or absence of AUs, range 0 (absent, not detected) to 1 (present, detected).</li> </ul> <p><em>Columns 166-172 = Emotion classification confidence<br></em></p> <ul> <li>162-172. anger, disgust, fear, happiness, sadness, surprise, neutral - Confidence of classified emotion category, range 0 (0%) to 1 (100%) confidence.</li> </ul> <p><em>Columns 173-685 = Face identity score<br></em></p> <p>Identity of faces contained in the video were classified using the FaceNet model (<a href="https://arxiv.org/pdf/1503.03832">described here</a>). This procedure generates at 512 dimension Euclidean embedding space.</p> <ul> <li>173. Identity - Predicated individual identifyed in the RAVDESS video. Note, value is always Person_0, as each video only contains a single actor at all times (categorical).</li> <li>174-685. Identity_1, ..., Identity_512 - Face embedding vector used by FaceNet to perform facial identity matching.</li> </ul> <p><em>Column 686 = Input video<br></em></p> <ul> <li>686. frame - The number of the frame (source videos 29.97 fps), range = 1 to n</li> </ul> <p><em>Columns 687-688 = Timing information<br></em></p> <ul> <li>687. frame.1 - The number of the frame (source videos 29.97 fps), duplicated column, range = 1 to n</li> <li>688. approx_time - Approximate time of current frame (0.0 to x.x, in seconds)</li> </ul> <p><strong>Tracking videos<br></strong></p> <p>Landmark Overlay and Landmark Plot videos were produced with <code>plot_detections</code> function call (<a href="https://py-feat.org/pages/api.html?highlight=plot_detections#feat.data.Fex.plot_detections">described here</a>). This function generated invidual images for each frame, which were then compiled into a video using the imageio library (<a href="https://pypi.org/project/imageio/">described here</a>).</p> <p>AU Activation videos were produced with plot_face function call (<a href="https://py-feat.org/pages/api.html?highlight=plot_face#feat.plotting.plot_face">described here</a>). This function also generated invidual images for each frame, which were then compiled into a video using the imageio library. Some frames could not be correctly generated by Py-Feat, producing only the AU heatmap but failing to plot/locate facial landmarks. These frames were dropped prior to compositing the output video. Drop rate was approximately 10% of all frames, in each video. Dropped frames were distributed evenly across the video timeline (i.e. no apparent clustering).</p> <p><strong>License information</strong></p> <p>The RAVDESS Facial expression and landmark tracking data set is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>.</p> <p><strong>How to cite the RAVDESS Facial Tracking data set</strong></p> <p><em>Academic citation </em><br>If you use the RAVDESS Facial Tracking data set in an academic publication, please cite both references: </p> <ol> <li>Liao, Z., Livingstone, SR., & Russo, FA. (2024). RAVDESS Facial expression and landmark tracking (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.13243600</li> <li>Livingstone SR, Russo FA (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</li> </ol> <p><em>All other attributions </em><br>If you use the RAVDESS Facial expression and landmark tracking dataset in a form other than an academic publication, such as in a blog post, data science project or competition, school project, or non-commercial product, please use the following attribution: "<a href="../records/13243600">RAVDESS Facial expression and landmark tracking</a>" by Liao, Livingstone, & Russo is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>.</p> <p><strong>Related Data sets</strong></p> <ul> <li>The Ryerson Audio-Visual Database of Emotional Speech and Song [<a href="../record/1188976">Zenodo project page</a>].</li> </ul> <p> </p>
Aberrant gene expression prediction benchmark based on GTEx v8
<p>This repository contains the aberrant gene expression prediction benchmark data as well as the necessary expected gene expression across tissues and tissue-specific isoform contribution scores for AbExp prediction.<br> </p> <p>The aberrant gene expression prediction benchmark data (aberrant_expression_prediction_benchmark.parquet) contains the following columns:</p> <ul> <li>individual: GTEx individual</li> <li>gene: Ensembl gene identifier</li> <li>tissue: GTEx tissue</li> <li>tissue_type: GTEx tissue type</li> <li>mu: OUTRIDER-estimated expected gene expression</li> <li>theta: OUTRIDER-estimated gene dispersion</li> <li>counts: Raw gene expression count</li> <li>normalized_counts: OUTRIDER-normalized gene expression count</li> <li>l2fc: log2 fold change between observed and expected gene expression count</li> <li>zscore: z-score of gene expression, obtained by quantile-mapping the OUTRIDER-estimated distribution to the standard normal distribution</li> <li>nominal_pvalue: OUTRIDER-estimated <em>p</em>-value of being an expression outlier</li> <li>FDR: FDR-adjusted <em>p</em>-value of being an expression outlier</li> <li>is_in_benchmark: Whether this observation is part of the aberrant gene expression prediction benchmark</li> <li>is_underexpressed_outlier: Whether this observation is an underexpression outlier at FDR < 5%. This is the benchmark prediction label.</li> </ul> <p><br>The isoform proportions table (gtex_v8_isoform_proportions.tsv) contains the following columns:</p> <ul> <li>gene: Ensembl gene identifier</li> <li>tissue_type: GTEx tissue type</li> <li>tissue: GTEx tissue</li> <li>transcript: Ensembl transcript identifier</li> <li>mean_transcript_proportions: mean transcript proportions across individuals in GTEx v8</li> <li>median_transcript_proportions: median transcript proportions across individuals in GTEx v8</li> <li>sd_transcript_proportions: standard deviation of transcript proportions across individuals in GTEx v8</li> </ul> <p><br>The expected gene expression table (gtex_v8_expected_expression.tsv) contains the following columns:</p> <ul> <li>gene: Ensembl gene identifier</li> <li>tissue_type: GTEx tissue type</li> <li>tissue: GTEx tissue</li> <li>gene_is_expressed: Whether the gene is expressed in the tissue</li> <li>median_expression: median OUTRIDER-estimated expected gene expression (mu) across individuals</li> <li>expression_dispersion: OUTRIDER-estimated gene dispersion (theta)</li> </ul>
Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer
<p><strong>Exposure expression quantitative trait loci (e2QTL)</strong></p> <p>The analysis of e2QTL allows for the identification of context-specific eQTL effects (Kim-Hellmuth et al. (2017), PMID: 28814792). To evaluate how inter-individual genetic variability influences the regulation of DNA damage-induced apoptosis, we performed e2QTL analysis of CD8+ T cells from 461 healthy European participants stimulated with high doses of 5 different carcinogens. These include Methyl-methanesulfonate (MMS), tert-butyl-hydroperoxide (TBOOH), benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE), 4-hydroxycyclophosphamide (HC) and UVC radiation.</p> <p><code>eQTL_DNA_damage_induced_apoptosis.csv:</code> eQTL data. FastQTL was used to analyze cis-eQTL within a 1 MB window of a gene’s transcription start site. Filtering and normalization of expression data was performed as described by the Genotype Tissue Expression (GTEx) project (The GTEx Consortium (2015), PMID: 25954001) including 60 PEER factors, top 3 genotype PCs and sex as covariates. Genotypes were filtered by PHWE > 10-6 and MAF > 5 %. Adjusted p-values were generated using 1,000 to 10,000 permutations. Variant IDs (CHR:POS:REF:ALT) are based on GRCh38.</p> <p><code>e2QTL_DNA_damage_induced_apoptosis.csv:</code> e2QTL data. The most significant variant for each analyzed gene was determined based on eQTL data to calculate e2QTL in a z-test that were corrected for multiple testing using Bonferroni correction as described by Kim-Hellmuth et al. (2017). Variant IDs (CHR:POS:REF:ALT) are based on GRCh38.</p> <p> </p>
Per-gene per-strain data: expression divergence between strains and alleles in F1s in wild C. elegans
<p>This dataset comprises p<span>er-gene per-strain data (used to perform all analyses and generate all figures), including regulatory pattern and inheritance mode classifications and underlying statistical differential expression results</span>.</p> <p>This is supplemental data for the linked preprint/publication describing insights derived from comparing gene expression (RNA-seq) between seven wild <em>C. elegans</em> strains and the laboratory reference strain N2, as well as the allelic expression of the wild and N2 alleles in F1s of crosses between all these wild strains and the reference strain.</p> <p>The PDF file <code>column_names_descriptions_worm_ase_data_pergene_perstrain.pdf</code> and excel spreadsheet <code>column_names_descriptions_worm_ase_data_pergene_perstrain.xlsx</code> serve as READMEs for the data file by providing details of the data held in each column of the data file <code>worm_ase_data_pergene_perstrain.txt.gz</code></p> <p>If you use this dataset (we hope someone does!), please cite the latest version of the accompanying preprint/publication.</p> <p>To query each gene in a user-friendly, visual format, see our shiny app <a href="https://wildworm.biosci.gatech.edu/ase/" target="_blank" rel="noopener">https://wildworm.biosci.gatech.edu/ase/</a></p>
Raw data for Figures in: LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions, Ferraioli et al., Nucleic Acids Res. 2024
<p>These datasets represent raw data for the preparation of Figures in:</p> <p><span>Ferraioli S, Sarigol F, Prakash C, Filipczak D, <strong>Foisner R</strong>, Naetar N. (2024) </span>LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions<span>. <em>Nucleic Acids Res.</em>2024 Sep 4:gkae752. doi: 10.1093/nar/gkae752.</span></p>
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Synthesis and biological evaluation of a radiolabeled PET probe for visualization of in vivo -fucosidase expression - esi
<p>Supplementary data for paper titled: <strong>Synthesis and biological evaluation of a radiolabeled PET probe for visualization of <em>in vivo</em> </strong><strong>a</strong><strong>-fucosidase expression</strong></p>
Limma-voom differential expression results for GTEx CVD and MD analyses
<p>Supplementary File 5 for the paper entitled "Exploring the Impact of Cerebrovascular Disease and Major Depression on Non-diseased Human Tissue Transcriptomes" (doi: 10.3389/fgene.2021.696836).</p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Dataset for "Diets supplemented with Saccharina latissima influence the expression of genes related to lipid metabolism and oxidative stress modulating rainbow trout (Oncorhynchus mykiss) fillet composition" (doi.org/10.1016/j.fct.2020.111332)
<p>Dataset corresponding to the following article:</p> <p>Ferreira, M., Larsen, B.K., Granby, K., Cunha, S.C., Monteiro, C., Fernandes, J.O., Nunes, M.L., Marques, A., Dias, J., Cunha, I., Castro, L.F.C., Valente, L.M.P., 2020. Diets supplemented with <em>Saccharina latissima </em>influence the expression of genes related to lipid metabolism and oxidative stress modulating rainbow trout (<em>Oncorhynchus mykiss</em>) fillet composition. Food Chem. Toxicol. 140, 111332. <a href="https://doi.org/10.1016/j.fct.2020.111332">https://doi.org/10.1016/j.fct.2020.111332</a></p>
Dataset of "Knowledge-based Sense Disambiguation of Multiword Expressions in Requirements Documents"
<p>This is the dataset used in the paper "Knowledge-based Sense Disambiguation of Multiword Expressions in Requirements Documents" at AIRE'21</p> <p> </p> <p>In this paper, we explore the use of a multiword expression detection in combination with a knowledge-based word sense disambiguation to disambiguate expressions in requirements documents.</p> <p>The dataset comprises a gold standard for multiword expression detection and sense disambiguation for Wikipedia and WordNet 3.1.</p> <p>It covers 18 projects: CM1, EBT and GANTT as well as the 15 projects of the NFR dataset.</p> <p> </p> <p><strong>File format</strong></p> <p>We use a tab-separated version of the DiMSUM file format and extended it with sense information.</p> <p>The nine original DiMSUM tab-separated columns:</p> <p>1. token offset</p> <p>2. word</p> <p>3. lowercase lemma</p> <p>4. POS</p> <p>5. MWE tag</p> <p>6. offset of parent token (i.e. previous token in the same MWE), if applicable</p> <p>7. strength level encoded in the tag, if applicable. Currently not used</p> <p>8. supersense label, Currently not used</p> <p>9. sentence ID</p> <p> </p> <p>and the two further columns for sense information:</p> <p>10. Wikipedia article name</p> <p>11. WordNet 3.1 synset</p> <p> </p> <p>The last two columns might end with .1 or .0 indicating that the sense is a fully applicable or partial sense of a multiword expression.</p> <p><strong>Attribution (of datasets used)</strong></p> <p>The NFR Dataset can be attributed to Jane Cleland-Huang.<br> Jane Cleland-Huang, Sepideh Mazrouee, Huang Liguo, & Dan Port. (2007). nfr [Data set]. Zenodo. Available: <a href="http://doi.org/10.5281/zenodo.268542">http://doi.org/10.5281/zenodo.268542</a><br> </p> <p>The CM1, EBT and GANTT datasets were retrieved from the Center of Excellence for Software & Systems Traceability (CoEST) <a href="https://doi.org/10.5281/zenodo.3309669">http://coest.org/</a></p>
Medial amygdala ERα expression influences monogamous behavior of male prairie voles in the field
<p>Formation of long-term pair-bonds is a complex process, involving multiple neural circuits, and is context- and experience-dependent. While laboratory studies using prairie voles have identified the involvement of several neural mechanisms, efforts to translate these findings into predictable field outcomes have been ambiguous at best. Here we test the hypothesis that inhibition of estrogen receptor alpha (ERα) in the medial amygdala of male prairie voles would significantly increase the expression of social monogamy in the field. Prairie vole populations of equal sex ratio were established in outdoor enclosures with males bred to overexpress ERα and display reduced prosocial behavior. Medial amygdala ERα expression was knocked down in half the males per population. Knockdown-males displayed a greater degree of social monogamy in five of the eight indices assessed. This study demonstrates the robust nature of ERα in playing a critical role in the expression of male social monogamy in a field setting.</p>
Full expression and splicing QTL summary statistics for three human spinal cord segments
<p>This dataset is part of the manuscript: <strong>"Integrative genetic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes</strong>" by Jack Humphrey, et al.</p> <p>The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and either gene expression or splicing phenotypes generated from RNA-seq of post-mortem spinal cord sections. All QTLs were mapped with TensorQTL.</p> <p>Nominal association files are coordinate sorted, bgzip-compressed and tabix-indexed tab-separated variable files. Top association files are gzip-compressed tab-separated variable files.</p> <p>Sample sizes for each dataset are as follows:</p> <p>Cervical spinal cord n = 216.</p> <p>Lumbar spinal cord n = 197.</p> <p>Thoracic spinal cord n = 68.</p> <p>Description of files:<br> CervicalSpinalCord_expression_peer30_gene.cis_qtl.txt.gz - <strong>Top per-gene </strong>eQTL summary statistics from cervical spinal cord <br> CervicalSpinalCord_expression_peer30_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> eQTL summary statistics from cervical spinal cord <br> CervicalSpinalCord_splicing_peer15_cluster.cis_qtl.txt.gz - <strong>Top per-cluster </strong>sQTL summary statistics from cervical spinal cord<br> CervicalSpinalCord_splicing_peer15_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> sQTL summary statistics from cervical spinal cord<br> LumbarSpinalCord_expression_peer30_gene.cis_qtl.txt.gz - <strong>Top per-gene </strong>eQTL summary statistics from lumbar spinal cord<br> LumbarSpinalCord_expression_peer30_gene.cis_qtl_nominal_tabixed.tsv.gz -<strong> Full nominal</strong> eQTL summary statistics from lumbar spinal cord<br> LumbarSpinalCord_splicing_peer10_cluster.cis_qtl.txt.gz - <strong>Top per-cluster </strong>sQTL summary statistics from lumbar spinal cord<br> LumbarSpinalCord_splicing_peer10_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> sQTL summary statistics from lumbar spinal cord<br> ThoracicSpinalCord_expression_peer10_gene.cis_qtl.txt.gz - <strong>Top per-gene </strong>eQTL summary statistics from thoracic spinal cord<br> ThoracicSpinalCord_expression_peer10_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> eQTL summary statistics from thoracic spinal cord<br> ThoracicSpinalCord_splicing_peer5_cluster.cis_qtl.txt.gz - <strong>Top per-cluster </strong>sQTL summary statistics from thoracic spinal cord<br> ThoracicSpinalCord_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> sQTL summary statistics from thoracic spinal cord</p> <p>als_snps_maf0.01_alleles.tsv.gz<strong> - Allele information</strong> for all SNPs tested in the eQTL and sQTL analysis</p> <p>Table columns are formatted as follows:</p> <p>Nominal QTL results include all SNP-gene pairs tested using either a 1Mb window from each side of the transcription start site (TSS) of the gene (eQTLs) or 100kb either side of the middle of the splicing cluster (sQTLs). Table columns are formatted as follows:</p> <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30) for eQTL, or the ID of the splice junction for sQTLs, splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30),separated by a colon</li> <li>variant_id - SNP tested for association (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>maf - minor allele frequency in cohort</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> </ul> <p>Permuted QTL results include only the top SNP-gene association for each gene (eQTL) or cluster (sQTL). Table columns are formatted as follows:</p> <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30), or the coordinates of the cluster, formatted as coordinate, splicing cluster id, and gene Ensembl id (GENCODE v30), separated by a colon</li> <li>num_var - total number of variants tested in <em>cis</em></li> <li>beta_shape1 - first parameter value of the fitted beta distribution</li> <li>beta_shape2 - second parameter value of the fitted beta distribution</li> <li>true_df - effective degrees of freedom the beta distribution approximation</li> <li>pval_true_df - empirical <em>P</em>-value for the beta distribution approximation</li> <li>variant_id - ID of the top variant (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>maf -minor allele frequency in MiGA cohort</li> <li>ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not)</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>pval_perm - first permutation <em>P</em>-value directly obtained from the permutations with the direct method</li> <li>pval_beta - second permutation <em>P</em>-value obtained via beta approximation. This is the one to use for downstream analysis</li> <li>qval - Storey q-value derived from pval_beta (FDR adjusted)</li> <li>pval_nominal_threshold - nominal <em>P</em>-value threshold for calling a variant-gene pair significant for the gene</li> </ul> <p>Allele Information for each variant:</p> <ul> <li>CHROM - chromosome position of the variant</li> <li>POS - position of the variant in the chromosome</li> <li>REF - reference allele (GRCh38)</li> <li>ALT - alternative allele (this is the <strong>effect</strong> allele in the eQTL and sQTL analysis)</li> <li>ID - variant id (rsid or chr:position:ref:alt)</li> </ul>
Elucidating gene expression adaptation of phylogenetically divergent coral holobionts under heat stress
<p>As coral reefs struggle to survive under climate change, it is crucial to know whether they have the capacity to withstand changing conditions, particularly increasing seawater temperatures. Thermal tolerance requires the integrative response of the different components of the coral holobiont (coral host, algal photosymbiont, and associated microbiome). Here, using a controlled thermal stress experiment across three divergent Caribbean coral species, we attempt to dissect holobiont member metatranscriptome responses from coral taxa with different sensitivities to heat stress and use phylogenetic ANOVA to study the evolution of gene expression adaptation. We show that coral response to heat stress is a complex trait derived from multiple interactions among holobiont members. We identify host and photosymbiont genes that exhibit lineage-specific expression level adaptation and uncover potential roles for bacterial associates in supplementing the metabolic needs of the coral-photosymbiont duo during heat stress. Our results stress the importance of integrative and comparative approaches across a wide range of species to better understand coral survival under the predicted rise in sea surface temperatures.</p>
Figure 1 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 1 Mite growth curve of Dermatophagoides pteronyssinus (a) andD. farinae (b) in the semisynthetic culture medium. Data showing the kinetics of the mites' growth in semi-synthetic culture medium are expressed as arithmetic mean ± standard error of triplicates, at each time of growth.
Figure 3 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 3 Kinetics of Der 1 and Der 2 major allergen levels during the growth ofDermatophagoides pteronyssinus (a) andD. farinae (b) in the semi-synthetic culture medium. Data showing the kinetics
Figure 2 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 2 SDS-PAGE IgE-immunoblotting of extracts fromDermatophagoides pteronyssinus (a) and D. farinae (b) mites along their growth. Mw: Molecular weight marker. Numbers on lines indicate
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