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12,799 results for “immunity”
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </p>
Peripheral MC1R activation modulates immune responses and confers neuroprotection in a mouse model of Parkinson's disease
<p>Raw data sets for the manuscripts</p> <p>This work was supported by NIH grants R01NS102735 and R01NS110879, the Farmer Family Foundation Initiative for Parkinson’s Disease Research and the MJFF and ASAP [ASAP-000312].</p>
Antagonism between viral infection and innate immunity at the single-cell level -- Immunostaining Imaging Dataset
<p>This dataset accompanies the article "Antagonism between viral infection and innate immunity at the single-cell level", at the time of submission available as a <a href="https://doi.org/10.1101/2022.11.18.517110">preprint</a>.</p>
Induced immune reaction in the acorn worm, Saccoglossus kowalevskii, informs the evolution of antiviral immunity
<p>The data present in this repository reflect intermediate and processed data presented in the manuscript, <em>Induced immune reaction in the acorn worm, Saccoglossus kowalevskii, informs the evolution of antiviral immunity. </em>This manuscript is still under review; as such, this page will be updated upon publication.</p> <p> </p> <p><strong>Manuscript Abstract:</strong></p> <p>Evolutionary perspectives on the deployment of immune factors following infection have been shaped by studies on a limited number of biomedical model systems with a heavy emphasis on vertebrate species. Though their contributions to contemporary immunology cannot be understated, a broader phylogenetic perspective is needed to understand the evolution of immune systems across Metazoa. In our study, we leverage differential gene expression analyses to identify genes implicated in the antiviral immune response of the acorn worm hemichordate, <em>Saccoglossus kowalevskii</em>, and place them in the context of immunity evolution within deuterostomes – the animal clade composed of chordates, hemichordates, and echinoderms. Following acute exposure to the synthetic viral dsRNA analog, poly(I:C), we show that <em>S. kowalevskii </em>responds by regulating the transcription of genes associated with canonical innate immunity signaling pathways (e.g., NF-κB and IRF signaling) and metabolic processes (e.g., lipid metabolism), as well as many genes without clear evidence of orthology with those of model species. Aggregated across all experimental time point contrasts, we identify 423 genes that are differentially expressed in response to poly(I:C). We also identify 147 genes with altered temporal patterns of expression in response to immune challenge. By characterizing the molecular toolkit involved in hemichordate antiviral immunity, our findings provide vital evolutionary context for understanding the origins of immune systems within Deuterostomia.</p> <p> </p> <p><strong>Repository contents:</strong></p> <p>### Processed Data ###</p> <ul> <li><em>Full_DESeq2_matrix.csv </em>--> DESeq2 results for each contrast (e.g., 2hpi treatment vs. control)</li> <li><em>MaSigPro.Clusters.csv</em> --> Mean expression for each gene placed within a pDEG cluster</li> <li><em>MaSigPro.SigGenes.TreatmentvsControl.Robj</em> --> T.fit() R-object output from MaSigPro pipeline. This can be opened in R using the load() function.</li> </ul> <p>### Homology Assessment ###</p> <ul> <li><em>Orthofinder.tar.gz</em> --> OrthoFinder results</li> <li><em>Skowalevskii_Genome_Annotation.SPHuman_and_HOG.csv</em> --> Assignment of IDs to Skow1.1 genes conforming to "PANTHER-Human" and "HOG" output described in the main text of the paper</li> <li><em>Skowalevskii_Genome_Annotation.SPPANTHER.csv </em>--> Assignment of IDs to Skow1.1 genes conforming to "PANTHER-SwissProt" output described in the main text of the paper</li> </ul> <p>### Functional Annotation ###</p> <ul> <li><em>Skow.HMMER_Pfam.domtblout.tsv</em> --> Pfam annotation of the Skow1.1 genome assembly in HMMER's domblout format</li> <li><em>Skow.KofamKOALA.detail.tsv</em> --> KO annotation of the Skow1.1 genome assembly using KofamKOALA (detailed output)</li> <li><em>Skow.KofamKOALA.detail.tsv </em>--> KO annotation of the Skow1.1 genome assembly using KofamKOALA (mapper output)</li> <li><em>SkowAnnotations.GO.tsv</em> --> GO annotation of the Skow1.1 genome assembly</li> <li><em>SkowAnnotations.PF.tsv</em> --> PF annotation of the Skow1.1 genome assembly</li> <li><em>SkowAnnotations.PP.tsv</em> --> PP annotation of the Skow1.1 genome assembly</li> </ul> <p>### Enrichment Data ###</p> <ul> <li><em>DESeqEnrichments.tsv</em> --> Pearson's chi-squared enrichment calculations for every annotation present in the Skow1.1 genome assembly for genes resolved as significantly differentially expressed by DESeq2.</li> <li><em>MaSigProEnrichments.tsv</em> --> Pearson's chi-squared enrichment calculations for every annotation present in the Skow1.1 genome assembly for genes resolved as significantly differentially expressed by MaSigPro.</li> </ul>
Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)
<p># Title<br> Immunization Agenda 2030 (IA2030) 1st Movement Full Learning Cycle (FLC 2022) – “How are you doing?” Value Creation Stories Survey (Version 1.0)</p> <p># Research audience<br> Education researchers interested in the application of the “value creation stories” (VCS) conceptual framework elaborated by Etienne Wenger et al. in the study of communities of practice and other types of digital communities.</p> <p># Credits</p> <p>## Author<br> The Geneva Learning Foundation<br> 18 avenue Louis Casaï<br> CH-1209 Geneva, Switzerland<br> research@learning.foundation</p> <p>### Principal Investigator and corresponding author<br> Reda Sadki, The Geneva Learning Foundation (TGLF)<br> reda@learning.foundation</p> <p>## Project partners<br> Bridges to Development<br> University of South Australia Centre for Change and Complexity in Learning (C3L)</p> <p>## Roles and responsibilities<br> - Design: The Geneva Learning Foundation<br> - Implementation (sample collection): The Geneva Learning Foundation<br> - Processing: The Geneva Learning Foundation, Bridges for Development, Centre for Complexity and Change in Learning (C3L)<br> - Anonymization: The Geneva Learning Foundation and Bridges for Development<br> - Data cleaning: Bridges to Development<br> - Submission: The Geneva Learning Foundation</p> <p>## Funding sources or sponsorship that supported the data collection<br> Wellcome, Bill & Melinda Gates Foundation (BMGF)</p> <p>## Recommended citation<br> The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: 10.5281/zenodo.7763922</p> <p># Description of the sample</p> <p>## File list:</p> <p>This file is IA2030_FLC_2022_Value_Creation_Stories.README.md</p> <p>IA2030-EN_FLC_2022_Value_Creation_Stories-questions_mapping.csv : List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023. </p> <p>IA2030-EN_FLC_2022_Value_Creation_Stories.csv : Dataset Response of participants that replied in English. (n: 2101, obs:5601) - Version 1: Geneva Learning Foundation, 31 March 2023. </p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories-questions_mapping.csv: List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023.</p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories-Google_translation.csv: Dataset Response of participants that replied in French translated to English using “Google Translate” (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023.</p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories.csv: Dataset Response of participants that replied in French (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023. Relationship between files: The questions codes data set are the same code as the column variables and can be connected.</p> <p>## Relationship between files<br> The questions codes data set are the same code as the column variables and can be connected.</p> <p>## Related data sets<br> This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC). The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities. </p> <p>Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit [insights@learning.foundation](insights@learning.foundation)</p> <p>## Other publicly accessible locations of the data<br> The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/</p> <p>## 1. Purpose and Objectives</p> <p>### Primary goal of the survey:<br> This survey had two goals in the context of TGLF’s IA2030 Movement Full Learning Cycle programme (2022): <br> 1. Provide an asynchronous mechanism for support between peers (participants) and from the TGLF team; and<br> 2. collect and measure programme participants’ value creation stories (VCS) during the programme.</p> <p>Martin de Laat’s “value creation stories” (VCS) has been used primarily in small-scale, qualitative studies of communities of practice, online forums, and education activities.</p> <p>This data set includes both quantitative (Likert) and qualitative (open text) responses to the VCS questions, collected over a period of four months (7 March – 20 June 2022) from a cohort that began with 6,185 participants on the start date.</p> <p>## 2. Population and Sample</p> <p>The target population were participants of the Geneva Learning Foundation’s Movement for Immunization Agenda 2030 (IA2030) learning programme. The initial cohort admitted to the programme was 6,185 individuals from 99 countries. Only participants who were formally admitted to the programme received the invitation to complete the survey.</p> <p>Programme participants were free to choose if and when to report (self-selection), and their responses were not checked against any other measures (self-reporting).</p> <p>### Languages: French and English</p> <p>## 3. Survey Design and Methods</p> <p>Data collection period: 7 March 2022 – 20 June 2022</p> <p>Between 7 March and 20 June 2023, participants in the Geneva Learning Foundation’s “Immunization Agenda 2030” (IA2030) Movement Full Learning Cycle (FLC) were asked to respond to a questionnaire titled “How are you doing?”.</p> <p>Participants received a personalized email with the request to share feedback about their experience during the week. The link to share feedback was also included in other reminder and information emails sent in response to participant needs.</p> <p>The first survey was launched on the 11 of March 2022 and the last at 17 of March 2022, totalizing 15 requests. Participants could answer the survey at any time and as many times that they wished.</p> <p> <br> The group of 6,185 participants grew over the course of the Cycle, as additional participants were able to join the initiative throughout the four-month period.</p> <p>### Software- or Instrument-specific information needed to interpret the data<br> - Automated translation of French data was performed using [Google Translate](https://translate.google.com/?sl=en&tl=fr&op=docs)<br> - Methods used for removing or anonymizing personal identifiers or sensitive information:<br> - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site [Miracle Salad](https://www.miraclesalad.com/webtools/md5.php.).Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.*Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.**<br> - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.) The macro did not account for typos, in case any country information is found please contact: [research@learning.foundation](research@learning.foundation)</p> <p>### Data collection start and end dates:<br> 7 March 2023 until 20 June 2023</p> <p>#### Events or circumstances during data collection that may have influenced results:<br> No requests for responses were sent during TGLF’s “Term break” between 16-30 April 2022.</p> <p>## 4. Data Processing and Cleaning</p> <p>- Incomplete or inconsistent responses: Not cleaned, as respondents were able to opt out of specific sections of survey or skip questions.<br> - Data transformations or imputations: None<br> - Treatment of outliers or extreme values: None</p> <p>## 5. Variables and Measures</p> <p>The survey included Likert scale questions and qualitative open texts based the conceptual framework for Value Creation Stories (VCS) developed by Wenger et. al. (2011). There are no derived or calculated variables. Items are Likert scale, multiple choice, and open text.</p> <p>## 6. Data Quality and Reliability<br> All the responses done before or after the FLC period (7 March – 20 June 2022) were excluded of the sample.</p> <p>## 7. Data Privacy and Anonymization</p> <p>### Methods used for removing or anonymizing personal identifiers or sensitive information:<br> - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site https://www.miraclesalad.com/webtools/md5.php. Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.<br> - Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information. <br> - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.)</p> <p>## 8. Data Availability and Accessibility<br> This data set is made available on Zenodo.org in the Zenodo community “Movement for Immunization Agenda 2030 (IA2030)”<br> https://zenodo.org/communities/ia2030/</p> <p>Requests for additional information should be addressed to research@learning.foundation.</p> <p>This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC).</p> <p>The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities.</p> <p>### Other publicly accessible locations of the data<br> The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/</p> <p>### Related data sets<br> Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit insights@learning.foundation</p> <p>## 10. Ethical Considerations</p> <p>### Ethical guidelines followed during data collection:<br> TGLF’s research abides by the principles of the Cantonal Commission for Research Ethics (CCER), the Federal Law on Research on Human Beings (RS 810.30), Swiss Human Research Act (HRA) and the Ordinance on Organisational Aspects of the Human Research Act (HRA Organisation Ordinance, OrgO-HRA)</p> <p>### Informed consent and participant rights information:<br> In order to join TGLF’s IA2030 Full Learning Cycle programme, participants had to confirm their agreement to use of their responses “for research, learning, evaluation, communication, and advocacy, in line with the Foundation’s mission”.</p> <p>Participants were able to opt out of the VCS questions by selecting “No” when asked “Could we ask you five questions about your participation?”. They were informed these questions were asked in order to “share your feedback in the next weekly Assembly”, the weekly synchronous meeting for programme participants. The rationale for sharing such feedback was also explained; “Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.”</p> <p>Data protection and confidentiality<br> Consent was requested during the application and submitted of action plan period for sharing data, in line with the Geneva Learning Foundation’s data protection and confidentiality policy.</p> <p># Copyright and license<br> The Geneva Learning Foundation © 2022. This data set and all associated files are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)</p> <p>© The Geneva Learning Foundation 2023</p> <p>Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International; https://creativecommons.org/licenses/by-nc-sa/4.0/.</p> <p>Under the terms of this license, you may copy, redistribute and adapt the data set for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this data set, there should be no suggestion that the Foundation endorses any specific organization, products or services. The use of the Foundation logo is not permitted. If you use the data set, then you must license your work under the same or equivalent Creative Commons license. If you create a translation of this data set, you should add the following disclaimer along with the suggested citation: “This translation was not created by the Geneva Learning Foundation. The Foundation is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition.”</p> <p>Any mediation relating to disputes arising under the license shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization.</p> <p>General disclaimers. The designations employed and the presentation of the data set do not imply the expression of any opinion whatsoever on the part of the Foundation concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement.</p> <p>The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the Foundation in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.</p> <p>All reasonable precautions have been taken by the Foundation to verify the information contained in this data set. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the Foundation be liable for damages arising from its use.</p> <p>This data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation.</p> <p>Version 1.0 (31 March 2023): reviewed internally; reviewed externally. </p> <p># References<br> Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum.</p> <p>Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum.</p> <p>Victoria J. Marsick, Rachel Fichter, Karen E. Watkins, 2022. From Work-based Learning to Learning-based Work: Exploring the Changing Relationship between Learning and Work, in: The SAGE Handbook of Learning and Work. SAGE Publications.</p> <p>Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Serv Res 22, 736. https://doi.org/10.1186/s12913-022-08138-4</p> <p>Watkins, K.E., Kim, K., 2019. Measuring the Impact of the WHO Scholar Programme Courses for Immunization (2016-2018) (Evaluation report). University of Georgia at Athens, Athens, United States.</p> <p>Watkins, K.E., Bhattarai, A., 2019. Analysis of the Impact Accelerator Launch Pad Individual Acceleration Reports in July 2019. University of Georgia at Athens, Athens, United States.</p> <p># Questionnaire</p> <p>## Hello {{fname}} {{lname}}. How are you doing in the Movement for Immunization Agenda 2030?</p> <p>## Do you need help? Do you want to share your experience? We would like to know how you are doing.<br> - I am doing fine.<br> - I have a problem and need help.<br> - I want to share my experience.</p> <p>## Tell us more about what you want to share. Be specific and detailed so that we can understand. Share your lessons learned, successes, and challenges.</p> <p>## Did you complete your action for the week? If you did it, how did it turn out? What did you learn in the process? Did anything surprise you? What will you do next? If you did not complete your action, what will you do differently next week? This is a good way to write your thoughts if you did not get to speak in the last session. You can also record an audio message in the IA2030 Movement Dialogue https://t.me/+-PwJxPpyWfQ0ZjRk or share an idea or practice https://accelerator.wazoku.com/ccc/learning in the Ideas Engine.</p> <p>## What do you need help with?<br> - I do not know what I am supposed to do<br> - I need help with my IA2030 challenge<br> - I want to catch up<br> - I have poor connectivity<br> - I have a problem with technology<br> - Something else</p> <p>## Tell us more about the problem you are facing.<br> What have you tried to solve this problem? Where did you get stuck? The more information you provide, the better colleagues will be able to help you.</p> <p>## Have you tried taking time to read and follow the instructions? </p> <p>Click here https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022 to access the video tutorials and slide decks on www.learning.foundation https://www.learning.foundation/login. </p> <p>Use your email email to log in. Don’t remember your password?</p> <p>Click here to recover it https://www.learning.foundation/password/new. </p> <p>Take the time to read the instructions – and then follow them step-by-step. Do not forget to come back to finish this questionnaire. </p> <p>## Do not suffer in silence. It sounds like you should ask for help from your Movement colleagues. </p> <p>Click here to connect with colleagues https://t.me/IA2030 in the IA2030 Movement Telegram channel.<br> - When you join Telegram, please introduce yourself and explain the problem that you are facing. Your colleagues can only help you if you describe the issue and explain what you have already done to solve it.<br> - We encourage you to share your challenge in the next short session where we share experience and problem-solve. Click here to register https://us02web.zoom.us/j/86171141804, and then come back to finish this questionnaire.<br> - Surely, someone will be able to help you. But you do have to register https://us02web.zoom.us/j/86171141804 and actually show up at the right time!<br> - Poor connectivity? Click here to listen https://podcasts.google.com/feed/aHR0cHM6Ly9saXN0ZW5ib3guYXBwL2YvODRTTFI0eTY5X05h to our low-bandwidth podcast. And then come back to finish this questionnaire.<br> - You can listen to most sessions in our podcast. This is audio-only, like listening to radio on demand.</p> <p>## Could we ask you five questions about your participation?<br> We will share your feedback in the next weekly Assembly. Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.</p> <p>Yes<br> No</p> <p>## Participation changed me as a professional<br> (change in skills, attitudes, identity, self-confidence, feelings, etc.).</p> <p>## Can you explain how participation changed you as a professional?</p> <p>## Participation affected my social connections<br> (change in the number, quality, frequency, emotions, etc.)</p> <p>## Can you explain how participation affected your social connections?</p> <p>## Participation helped my professional practice<br> (get new ideas, insights, materials, procedures, etc.)</p> <p>## Can you explain how participation helped your professional practice?</p> <p>## Participation changed my ability to influence my world as a professional<br> (enhance my voice, contribution, status, recognition, etc.)</p> <p>## Can you explain how participation changed your ability to influence your world as a professional?</p> <p>## Participation made me see my world differently<br> (change in perspective, new understandings of the situation, redefine success, etc.)</p> <p>## Can you explain how participation made you see your world differently?</p> <p>## Do you remain committed to the Movement for Immunization Agenda 2030?<br> You remain a Member even if you are not actively participating.<br> - Yes, and I am actively participating<br> - Yes, but I am not actively participating<br> - No, I wish to leave the Movement</p> <p>## We are sorry to see you go. Could you let us know what went wrong? What could we have done better to support you?<br> - (Or just hit RETURN to skip.)</p> <p>## What is the email you are using?<br> We need your email to follow up and respond to what you shared with us. Do not forget to press the SUBMIT button.</p> <p>## URL redirection upon completion:<br> https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022</p> <p>## Thank you [fname] [lname] for sharing your feedback.<br> We will share your feedback in the next weekly Assembly. Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.</p> <p>Click here to check http://cal.ae/eudusmw the IA2030 Movement calendar so you do not miss upcoming event</p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2<strong> </strong>lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.</p>
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
Dataset for "Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"
<p>Datasets for preprint (https://doi.org/10.1101/2020.07.10.190785) entitled:</p> <p>"Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"</p> <p>Contains:</p> <p>- All data files for scripts quoted in the preprint and deposited at https://github.com/krasileva-group/hvNLR</p> <p>- Clade Membership Tables</p> <p>- Clade Alignment Files</p> <p>- Clade Trees</p> <p>- Excel files for Figure S1, Figure S2, and Table 1</p>
Lactate Increases Stemness of CD8+ T Cells to Augment Anti-Tumor Immunity
<p>The immunological role of lactate in antitumor immunity is not well understood. In this study, we report lactate treatment significantly augments antitumor efficacy of immune checkpoint blockade or T cell vaccine therapy in multiple tumor models. Single cell transcriptomics and flow cytometry analysis revealed an increased subpopulation of stem-like TCF-1-expressing CD8<sup>+</sup> T cells upon lactate treatment.</p>
Immune repertoire profiling reveals that clonally expanded B and T cells infiltrating diseased human kidneys can also be tracked in the blood
<p>Recent advances in high-throughput sequencing allow for the competitive analysis of the human B and T cell immune repertoire. In this study we compared Immunoglobulin and T cell receptor repertoires of lymphocytes found in kidney and blood samples of 10 patients with various renal diseases based on next-generation sequencing data.</p>
Genetic architecture of immune cell DNA methylation in the rhesus macaque
<p><strong>Complete model outputs from rhesus macaque (<em>Macaca mulatta</em>) whole blood meQTL and eQTL analyses in article, "Genetic architecture of immune cell DNA methylation in the rhesus macaque". </strong></p> <p><strong><em>cis</em> meQTL model output (SNP-CpG associations):</strong> </p> <ol> <li>IMAGE_573_meqtl_model_res_wPVE.txt: <ul> <li>Model results from IMAGE meQTL mapping including all genome, chromatin state annotations, and PVE estimates</li> </ul> </li> <li>pqlseq_allimagesnps_res_converged_wpve.txt: <ul> <li>Model results from PQLseq meQTL mapping including PVE estimates </li> </ul> </li> </ol> <p><strong><em>cis</em> eQTL model output (SNP-gene associations): </strong></p> <ol> <li>eqtl_res_sva5_gemma_172samples_qvalue.txt: <ul> <li>Model results from GEMMA eQTL mapping </li> </ul> </li> </ol> <p> </p>
Fig. 1 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 1. Analysis of the population of the potato ladybird beetle with the species-specific PCR-markers of the gene COI mtDNA. А – species-specific marker for H. vigintioctopunctata, 400 b.p.; Б – species-specific marker for H. vigintioctomaculata, 406 b.p.; М – marker of the lengths of fragments 100 b.p. ladder; 1–3 – Primorsky krai: Chuguevsky district; 4–6 – Amurskaya oblast; 7–17 – Primorsky krai: Timiryazevsky.
Fig. 3 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 3. Sinergetic activity of the protainases of trypsin type (in an insect) and trypsin inhibitors (in a plant) in the course of feeding on different potato varieties.
ATAC-seq dataset: Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors
<p>The dataset encompasses raw sequencing reads, identified peaks, and regions of differential accessibility derived from ATAC-seq experiments conducted under various immune activation conditions. For additional technical details regarding data collection, please refer to the published source at https://doi.org/10.1093/jxb/erab373.</p>
Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease
<p><span><span><span><span><span><span><span><span><span><span><span>Some patients hospitalized with acute COVID-19 suffer respiratory symptoms that persist for many months. We delineated the immune-proteomic landscape in the airway and peripheral blood of healthy controls and post-COVID-19 patients 3 to 6 months after hospital discharge. Post-COVID-19 patients showed abnormal airway (but not plasma) proteomes, with elevated concentration of proteins associated with apoptosis, tissue repair and epithelial injury versus healthy individuals. Increased numbers of cytotoxic lymphocytes were observed in individuals with greater airway dysfunction, while increased B cell numbers and altered monocyte subsets were associated with more widespread lung abnormalities. 1 year follow-up of some post-COVID-19 patients indicated that these abnormalities resolved over time. In summary, COVID-19 causes a prolonged change to the airway immune landscape in those with persistent lung disease, with evidence of cell death and tissue repair linked to ongoing activation of cytotoxic T cells. </span></span></span></span></span></span></span></span></span></span></span></p>
Immune disease variants modulate gene expression in regulatory CD4+ T cells
<p>We mapped genetic regulation (QTL) of gene expression and chromatin activity in Tregs and we identified 133 colocalizing loci with immune disease variants.<br> For the time being, the preprint DOI: <a href="https://doi.org/10.1101/654632">10.1101/654632</a></p>
Tumor-Immune Microenvironment Revealed by Imaging Mass Cytometry in a Metastatic Sarcomatoid Urothelial Carcinoma with a Prolonged Response to Pembrolizumab - IMC data
<blockquote> <p>Sarcomatoid urothelial carcinoma (SUC) is a rare subtype of urothelial carcinoma (UC), that typically presents at an advanced stage compared to more common variants of UC. Locally advanced and metastatic UC have a poor long-term survival following progression on first-line platinum-based chemotherapy. Antibodies directed against the programmed cell death 1 protein (PD-1) or its ligand (PD-L1) are now approved to be used in these scenarios. The need for reliable biomarkers for treatment stratification is still under research. Here we present a novel case report of the first Image Mass Cytometry (IMC) analysis done in SUC to investigate the immune cell repertoire and PD-L1 expression in a patient who presented with metastatic SUC and experienced a prolonged response to the anti-PD1 immune checkpoint inhibitor pembrolizumab after progression on first line chemotherapy. This case report provides an important platform for translating these findings to a larger cohort of UC and UC variants.</p> </blockquote> <p>We make available TIFF files containing imaging mass cytometry data for 4 regions of interest of a sample of metastatic sarcomatoid urothelial carcinoma. The order of the axis in the image stacks is "CYX". The CSV files indicate the identity of the channels.</p>
Differential effects of early or late exposure to prenatal maternal immune activation on mouse embryonic neurodevelopment
<p>Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental and psychiatric disorders. MIA-induced deficits in adolescent and adult offspring have been well characterized, however, less is known about the effects of MIA-exposure on embryo development. To address this gap, we collected high-resolution ex vivo magnetic resonance imaging (MRI) of C57BL/6 mouse embryos (gestational day [GD]18) who were prenatally exposed to MIA either early (GD9) or late (GD17) in gestation. We further examined hippocampal neuroanatomy using electron microscopy and identified differential effects due to MIA-timing. </p> <p>The data published here was collected and analyzed for the following publication available as a preprint on BioRxiv (https://www.biorxiv.org/content/10.1101/2021.07.14.452084v2). Briefly, We identify striking neuroanatomical changes in the embryo brain, particularly in the late exposed offspring. An increase in apoptotic cell density was observed in the GD9 exposed offspring, while an increase in the density of dark neurons and glia, putative markers for increased neuroinflammation and oxidative stress, was observed in GD17 exposed offspring, particularly in females. Overall, our findings integrate imaging techniques across different scales to identify differential impact of MIA-timing on the earliest stages of neurodevelopment.</p> <p>In this dataset, you will find a total of <strong>187 preprocessed structural MRIs </strong>(in MINC format) of whole embryos at gestational day 18. These embryos were exposed to poly I:C or vehicle control (0.9% sterile saline) at GD9 or 17. A multi-channel 7.0-T MRI scanner with a 40 cm diameter bore (Varian Inc., Palo Alto, CA), with a custom-built 16-coil solenoid array was used to acquire T2-weighted, gadolinium enhanced structural images at 40 μm3 resolution images from 16 samples concurrently (3D fast spin echo sequence using a cylindrical k-space acquisition; TR/TE=350/12 ms, echo train length=6, two averages, field-of-view 20 mm x 20 mm x 25 mm, matrix size=504 x 504 x 630).</p> <p>An N4 correction for B1 bias field inhomogeneities and denoising using non-local means (minc_anlm) was applied to the T2-weighted images, and the background was set to zero using minc tools. The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>The dorsal hippocampus was selected as a region of interest in which the total number and density of total cells, dark neurons, dark glia, apoptotic cells were assessed (as presented in our manuscript). This is available in the <strong>EM_raw_data_per_slice.csv</strong> and <strong>EM_average_per_mouse.csv </strong>files. </p> <p>Included in this data set are the <strong>structural MRIs in MINC format</strong>, corresponding demographics information (<strong>demographics.csv</strong>), electron microscopy data from the dorsal hippocampus (<strong>EM_raw_data_per_slice.csv</strong> and <strong>EM_average_per_mouse.csv)</strong> data, and a <strong>readme.txt</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles.The raw (not-preprocessed) MINC files, as well as MINC files cropped to the head of the embryos available upon request to the authors. </p> <p>Finally, the authors would like to acknowledge the funding bodies that supported the completion of this work including the Canadian Institute for Health Research, the Fonds de Recherche du Québec en Santé, and the Healthy Brains for Healthy Lives at McGill University.</p>
Comparative host transcriptomics as a tool to identify candidate biomarkers for immune reactions in leprosy: A meta-analysis study
<p>The dataset consists of R source code for the individual dataset analysis of the studies and their meta-analysis. It also contains supplementary tables and figure.</p>
Longitudinal structural MRI, MRS, and behavioral data for mice prenatally exposed to maternal immune activation at gestational day 9
<p>Previous evidence from our lab (https://cobralab.ca/) and others suggest that prenatal exposure to maternal immune activation (MIA) can impact trajectories of neurodevelopment as measured through brain anatomy and behavior in mice. Yet, there are still open questions regarding the alterations to developmental trajectories, as well as the impact on brain chemistry, that this data set seeks to explore. The dataset presented here includes magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) data from two timepoints, adolescence (postnatal day [PND 35]) and young adulthood (PND 60) in C57BL/6J mice prenatally exposed either to poly I:C (POL) inducing maternal immune activation (MIA) or saline (SAL) at gestational day (GD) 9. The dataset also includes three behaviors acquired after each scanning session with 2 days of rest between the scans and each behavior: open field test, social novel object preference test, and prepule inhibition. Finally, the data also include cytokine assays acquired from a separate sample of pregnant mice and a test-retest of MRS acquired from a voxel in the anterior cingulate area. </p> <p>The data here published were collected and analyzed for a paper under review, available as a preprint where more details can be found here: https://www.preprints.org/manuscript/202203.0136/v1. In brief, using whole-brain, voxelwise analysis techniques (deformation-based morphometry) we found MIA subtly altered developmental trajectories, reducing volume relative to SAL offspring in the hippocampus and the anterior, right caudate putamen, and increasing volume in the posterior, left caudate putamen and cerebellum. Additionally, there was a trending decrease of myo-inositol and GABA in MIA offspring at PND 60 compared to SAL controls. Finally, there was a trending decrease in ratio of distance travelled in the anxiogenic center zone of an open field compared to the outer areas at PND 35 for MIA offspring. </p> <p>In this dataset you will find a total of <strong>80 preprocessed structural MRIs</strong> in minc format acquired at postnatal day ~35 and ~60 in mice exposed to 5mg/kg poly I:C or vehicle control (0.9% sterile saline) at GD9. The images are included in CUPO_MIA_mncs.zip. These are T1-weighted structural images with two averages; repetition time (TR)/echo time (TE) = 21.55 ms/5.13 ms, matrix size = 260 x 158 x 210, voxel dimensions =&thinsp;70 µm isotropic, flip angle =&thinsp;20°, 23 min total using 5% isoflurane for induction, 1.5% for maintenance of anesthesia during the scan on a cryogenically-cooled surface coil. T1-weighted scans were preprocessed by stripping native coordinates, flipping left-right to maintain fidelity, denoising, correcting inhomogeneities in the bias field using the N4 algorithm, and registering in LSQ6 alignment (i.e. 6 degrees of freedom are allowed for imagine alignment: translations and rotations along x, y, and z dimensions). The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>Behavioural tests were performed following the postnatal day 35 and 60 scans in all animals with a 2 day rest period. These include: open field test, three chambered social approach, and prepulse inhibition. The data for all of these tests is presented in individual .csv spreadsheet and includes data for both the timepoints evaluated. Additionally, cytokine panels were collected from an independent cohort of 7 dams. <strong>MRS </strong>data are included in two formats: 1) preprocessed quantifications from LCModel software in csvs, and 2) raw data with press and press_w (respectively water supressed and unsupressed acquisitions) for analysis. The raw data were released in upload version 1.1.0. MRS was acquired from a 1.2 x 2.6 x 2.5 mm3 voxel in the ACA with a Point Resolved Spectroscopy sequence (PRESS; TR/TE=3000/8.5 ms, 256 averages). Within the raw_data.zip,</p> <p>Included in this data set are the structural MRIs in MINC format, the behavioural .csv data, the MRS data (csvs and raw files), and a <strong>README</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles. DICOMS are also available for the structural MRI data, as are the raw (not-preprocessed) MINC files, available upon request to the authors. </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.