What's new in dkNET 2.0
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What's new in dkNET 2.0 (March 17, 2015) We are pleased to announce the release of dkNET 2.0! The new site is mobile and tablet friendly, so you can keep up to date with dkNET wherever you are. In addition to the new look and feel, we’ve added new data and features to make it easier to search for and access research resources: * A new community funding section has been added to let you search for pilot grants from NIDDK supported centers. Please let us know if you have an opportunity you’d like advertised. * We’ve consolidated the dkNET website and search portal into one site: dknet.org. Learn about the dkNET project through the ABOUT page in the top menu. * Do you have a database, software tool or service of interest to the dkNET community? Register it into the Resource Catalog. Want to see what resources are available for kidney? Search the Registry. * New data sources have been added to the Community Resources section. * New search and exploration features have been added to the More Resources section.
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Research Data Alliance (RDA) Webinar: What do you really know about that anti...
The blessing and the curse: handshaking between general and specialist data r...
The blessing and the curse: handshaking between general and specialist data r...
NISO Working Group Connection Live! Research Data Metrics Landscape: An Updat...
NISO Working Group Connection Live! Research Data Metrics Landscape: An Updat...
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Petten ces 2014 presentation
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ONS Local presents: Census 2021, products and analysis
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CRMS
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2. A walkthrough showed the login process, plan creation, guidance features, template selection, sharing abilities, and export options.
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2012 10 ciard (GCARD2 Uruguay)
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Similar to What's new in dkNET 2.0 (20)
ONS Local presents: Census 2021, products and analysis
ONS Local presents: Census 2021, products and analysis
Community Systems Presents: Four Ways To Market Your Community's Commercial ...
Community Systems Presents: Four Ways To Market Your Community's Commercial ...
Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards
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More from dkNET
dkNET Webinar: The 4DN Data Portal - Data, Resources and Tools to Help Elucid...
Presenter: Andrew Schroeder, PhD. Project Manager & Senior Data Curator, 4D Nucleome Data Coordination and Integration Center (4DN-DCIC), Park Lab, Department of Biomedical Informatics, Harvard Medical School
Abstract
The Common Fund 4D Nucleome program, currently in its 9th year, is a consortium of researchers that aims to understand the principles behind the three-dimensional organization of the nucleus and how this organization can change over time to affect a variety of cellular processes. The 4DN Data Portal (data.4dnucleome.org) is an expanding resource hosting data generated by the 4DN Network and other reference nucleomics data sets. The portal provides tools for search, exploration, visualization, and download. An overview of the data portal, highlighting available data, how it can be found, visualized and used for analyses will be presented.
The top 3 key questions that the 4DN data portal can answer:
1. Are there significant sites of long-range chromatin contacts near my gene or region of interest?
2. What omics datasets are available for my tissue of interest?
3. Are there imaging datasets available that are relevant to my tissue of interest?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours: NIH Data Management and Sharing Mandate 05/03/2024
Presenter: Jeffrey Grethe, PhD, Principal Investigator of NIDDK Information Network (dkNET), Center for Research in Biological Systems, University of California San Diego
For all proposals submitted on/after January 25 2023, NIH requires the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine
Abstract
Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge.
Resource link: https://github.com/Texera/texera
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Unlocking the Power of FAIR Data Sharing with ImmPort 04/12/2024
Presenter: Sanchita Bhattacharya, ImmPort Science Program Lead, Bakar Computational Health Sciences Institute UCSF
Abstract
The Immunology Database and Analysis Portal (ImmPort, https://www.immport.org/home) is a domain-specific data repository for immunology-related data which is funded by the National Institutes of Health, National Institute of Allergy and Infectious Diseases, and Division of Allergy, Immunology, and Transplantation. ImmPort has been making scientific data Findable, Accessible, Interoperable, and Reusable (FAIR) for over 20 years. ImmPort data sets encompass over 7 million experimental results across 160 diseases and conditions, including data related to diabetes, kidney and liver transplantation, celiac disease, and many more conditions. In this webinar, participants will learn about data management and sharing through ImmPort, as well as finding and leveraging data sets of interest for research.
The top 3 key questions that the ImmPort can answer:
1. How can researchers share data through ImmPort to comply with the NIH Data Management and Sharing policy?
2. How does ImmPort support FAIR data and why is this powerful for research?
3. What scientific data does ImmPort house that would be of interest to NIDDK researchers?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Tabula Sapiens 03/22/2024
Presenter: Angela Oliveira Pisco , PhD
Abstract
Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference...Full abstract: https://dknet.org/about/blog/2726
Resource link: https://tabula-sapiens-portal.ds.czbiohub.org
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The Multi-Omic Response to Exercise Training Across Rat Tissue...
Presenter: Malene Lindholm, PhD, Instructor, Department of Medicine, Stanford University
Abstract
The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to map the molecular responses to exercise and training to elucidate how exercise improves health and prevents disease. The first MoTrPAC data provides an extensive temporal map of the dynamic multi-omic response to endurance training across multiple rat tissues. All results can be viewed, interrogated, and downloaded in a user-friendly, publicly accessible data portal (https://motrpac-data.org). The MoTrPAC data compendium includes transcriptomics, proteomics, metabolomics, phosphoproteomics, acetylproteomics, ubiquitylproteomics, DNA methylation, chromatin accessibility, and multiplexed immunoassay data. This compilation constitutes of 211 datasets across 19 tissues, 25 molecular assays, and 4 training time points in adult male and female rats. Over 35,000 analytes were found to be differentially regulated in response to endurance training, with many displaying sexual dimorphism. We observed a male-specific recruitment of immune cells to adipose tissues and an anticorrelated transcriptional response in the adrenal gland related to the stress response. Temporal multi-omic and multi-tissue integration demonstrated similar temporal responses in the heart and skeletal muscle, reflecting a concerted adaptation of mitochondrial biogenesis and metabolism. Integrative multi-omic network analysis revealed connections between the heat shock-mediated stress response and mitochondrial biogenesis. Training increased phospholipids and decreased triacylglycerols in the liver, and there were extensive changes to mitochondrial protein acetylation. Many changes were relevant for human health conditions, such as non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular wellness, and tissue damage and repair. Altogether, this MoTrPAC resource provides an unprecedented view of the effects of exercise across an organism, revealing mechanistic details of how exercise impacts mammalian health. The MoTrPAC data hub is the primary online resource to disseminate this large-scale multi-omics data.
The top 3 questions that the MoTrPAC resource can answer:
1. What is the multi-omic response to endurance exercise across different tissues?
2. What are the top signaling pathways affected in response to exercise and do they differ between males and females?
3. How can the MoTrPAC data hub be utilized to interrogate all the MoTrPAC findings?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
Presenter: Pieter Dorrestein, PhD, Professor, Skaggs School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology and Pediatrics, University of California San Diego
Abstract
In the analysis of organs, volatilome, or biofluids, the microbiome influences 15-70% of detectable mass spectrometry molecules. Typically, only 10% of human untargeted metabolomics data can be assigned a molecular structure, with merely 1-2% traceable to microbial origins. Human microbiomes contribute metabolites through the microbial metabolism of host-derived substances, digestion of food and beverage molecules, and de novo assembly using proteins encoded by genetic elements. Despite the significance of microbiome-derived metabolites to human health, there is no centralized knowledge base for community access. To address this, the "Collaborative Microbial Metabolite Center" (CMMC) leverages expertise in mass spectrometry, microbiome innovation, and the GNPS ecosystem to built a knowledgebase. It aims to create a user-accessible microbiome resource, enrich bioactivity knowledge, and facilitate data deposition. The CMMC includes the construction of a knowledge base, MicrobeMASST tool, and health phenotype enrichment workflows, the construction and use will be discussed in this presentation. The use of this ecosystem will be exemplified by the discovery of 20,000 bile acids, many of which were shown to be of microbial origin and linked to diet and IBD.
The top 3 key questions that this resource can answer:
1. How can we leverage the 1000’s of public metabolomics studies to discover microbial metabolites and their organ distributions as well as their phenotypic, including health, associations?
2. If one has an unknown molecule, how can one assess what microbes make a molecule without known structure?
3. How can one contribute to the expansion of the knowledgebase on microbial metabolites?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: An Encyclopedia of the Adipose Tissue Secretome to Identify Me...
Presenter: Paul Cohen, MD, PhD, Albert Resnick, M.D. Associate Professor, Rockefeller University
Abstract
White and brown adipocytes not only play a central role in energy storage and combustion but are also dynamic secretory cells that secrete signaling molecules linking levels of energy stores to vital physiological systems. Disruption of the signaling properties of adipocytes, as occurs in obesity, contributes to insulin resistance, type 2 diabetes, and other metabolic disorders. Fat cells have been estimated to secrete over 1,000 polypeptides and microproteins and an even larger number of small molecule metabolites. The great majority of the adipocyte secretome has not been defined or characterized. A major obstacle has been the lack of suitable technologies to quantitatively identify circulating proteins and metabolites, determine their cellular origin, and elucidate their function. Building on key innovations in chemical biology and mass spectrometry, our team is generating an encyclopedia of the white and brown adipocyte secretome in mouse models and humans. Our work has the potential to identify new secreted mediators with roles in obesity, type 2 diabetes, and metabolic diseases, provide a crucial resource for researchers and clinicians, and lead to new biomarkers and therapies.
The top 3 key questions that this resource can answer:
1. What techniques can be used to characterize the secretome of a cell type in vitro and in vivo?
2. What is the full complement of proteins and metabolites secreted by different kinds of adipocytes?
3. How should one prioritize uncharacterized secreted mediators for functional study?
Resource link: https://secrepedia.org/
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: A Single Cell Atlas of Human and Mouse White Adipose Tissue 11...
Presenter: Margo Emont, PhD. Instructor, Beth Israel Deaconess Medical Center/Harvard Medical School
Abstract
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defense. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.
The top 3 key questions that this resource can answer:
1. How specific is my gene of interest to a particular cell type in adipose tissue?
2. Is the gene/pathway that I am studying in mouse adipose tissue also present in human adipose tissue (and is it regulated similarly in low vs high body weight)?
3. What are the changes in gene expression in a specific cell type at low vs high body weight?
Resource link:
https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue#study-summary
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The National Sleep Research Resource (NSRR) - Opportunities fo...
Presenter: Susan Redline, MD, MPH, Peter C. Farrell Professor of Sleep Medicine, Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract
Experimental, clinical and epidemiological studies have identified multiple inter-relationships of sleep with glucose regulation and metabolic disease. In one meta-analysis, after overweight and family history of diabetes, the next 7 top risk factors for incident diabetes were measures of sleep health. These included poor sleep quality, insomnia, short or extremely long sleep duration, and sleep apnea; each sleep problem was associated with incident diabetes with relative risks ranging from 1.38 to 1.74. A mechanism linking sleep apnea with diabetes is through the effects of intermittent hypoxemia on insulin sensitivity. However, studies using neurophysiological markers of sleep in healthy adults showed that selective reduction of slow wave sleep reduced glucose tolerance by 23%, thus additionally suggesting the importance neurophysiological mechanisms during sleep in glucose regulation. In support of this, longitudinal epidemiological studies demonstrated that higher proportions of slow wave sleep (N3) were protective for the development of type 2 diabetes. Recent animal and human studies also point to the effects of sleep micro-architecture—specifically the coupling of slow waves and spindles- on short-term and long-term glucose regulation, possibly through the effects on signaling between the hippocampus and hypothalamus, and changes in autonomic nervous system output. Experimental data also demonstrate a prominent role of the circadian system in regulating glucose and lipid levels. In support of those studies, epidemiological associations have identified significant associations between actigraphy-based measures of sleep irregularity (a marker of circadian disruption) with incident metabolic dysfunction and hypertension. This rich data implicating sleep disturbances as drivers of metabolic disease, coupled with data indicating a high prevalence of sleep and circadian disorders in the population, suggest novel opportunities to target sleep and circadian pathways for preventing or treating metabolic dysfunction, as well as key knowledge gaps.
The National Sleep Research Resource (NSRR; sleepdata.org) provides a large and growing repository of well-annotated polysomnograms (PSGs), actigraphy studies, and questionnaires, some associated with clinical and biochemical data relevant to understanding the links between sleep and circadian disorders with metabolic disease. Notably, the NSRR includes over 50,000 PSGs, which concurrently include multiple physiological signals with high temporal resolution, allowing generation of thousands of variables summarizing dynamic physiological changes and “cross-talk” between physiological systems...(Please see https://dknet.org/about/blog/2674 for full abstract)
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Abstract
The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.
Join us for a webinar to introduce the following resources & topics:
1. An overview of dkNET
2. How Resource Reports benefit you
3. Biomed Resource Watch
3.1 Navigating Biomed Resource Watch
3.2 How to Submit a Claim
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Wenting Wu, PhD. Research Assistant Professor, Center for Diabetes and Metabolic Diseases, Department of Medical and Molecular Genetics, Associate Director of Data and Analytics Core for Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine
Abstract
Type 1 diabetes (T1D) is an immune-mediated disease that results in insulin insufficiency and affects 0.3% of the population, including both children and adults. To support clinical trial efforts, there is an urgent need to develop reliable biomarkers capable of predicting T1D risk and guiding therapeutic interventions. Recently, whole blood bulk RNA sequencing has been used to guide T1D clinical trial design and assess response to disease modifying interventions. While the use of bulk RNA sequencing is cost-effective, these datasets provide limited information about cell specific gene expression changes. Here, we aimed to apply computational strategies to deconvolute cell type composition using cell specific gene expression references. Single-cell RNA sequencing (scRNA-seq) was conducted to profile peripheral blood mononuclear cells obtained from youth within recent T1D onset and age- and sex-matched controls and identified 31 distinct cell clusters. Using this pre-defined reference dataset, we ran computational algorithms CIBERSORTx and other deconvolution methods simultaneously to deconvolute cell proportions using public clinical trial data. We focused our initial analysis on data from the TN-20 Rituximab trial, which tested the anti-CD20 monoclonal antibody rituximab vs placebo in recent onset T1D. This talk will introduce recent advances of scRNA-seq techniques and computational deconvolution methods and demonstrate that how we apply different deconvolution approaches for secondary analysis of existing clinical trial data, in the purpose of linking cell specific immune signatures associated with drug responder status.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Gluco...
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Joon Ha, PhD. Associate Professor, Department of Mathematics, Howard University, Washington DC.
Abstract
The most common form of diabetes, type 2 diabetes (T2D) is a failure of insulin-secreting pancreatic beta-cells to increase insulin to the level required to maintain normal blood glucose. Thus, identifying beta-cell function and insulin sensitivity in those who are at high risk is crucial to preventing and delaying the disease. Hyper-glycemic clamp and euglycemic hyper- insulinemic clamp are considered to be gold standard measures for these quantities. However, these two methods demand highly skilled labor and thus are cost-prohibitive. Glucose challenge tests have been used to estimate beta-cell function and insulin sensitivity. The product of beta-cell function and insulin sensitivity, termed the disposition index (DI), is of great value because it measures beta-cell function relative to insulin requirements. However, glucose challenge tests are expensive and time-consuming and therefore impractical to implement in large-scale clinical studies. To address this challenge, we developed a model disposition index (mDI estimated without insulin) that does not require insulin measurements during an oral glucose tolerance test (OGTT) (Ha et al., Diabetes 2021 (70) suppl. 1). mDI outperforms the conventional oral disposition index (oDI) at predicting progression to diabetes.
To further increase access and refine the assessments of beta-cell function, we are adapting our model to calculate a model disposition index using continuous glucose monitoring (CGM). CGM has been in the spotlight of diabetes management and has revolutionized the field of medicine as they are approved for glucose monitoring and clinical decision-making in patients with diabetes. CGM devices are relatively inexpensive compared to oral glucose challenge tests, accessible, and simple to use, especially in remote or free-living environments. The CGM device continuously measures interstitial glucose every 5 minutes and provides glucose profiles for 7-14 days. Thus, there are numerous data points compared to glucose challenge tests, but the abundant data points have not previously been used for estimating metabolic parameters. We compared mDI to two widely used CGM-derived metabolic parameters for assessing metabolic status and risk, mean glucose and glycemic excursion. Both mean glucose and glycemic excursion correlated strongly with mDI. The new approach promises to be cost- effective and easy to perform and therefore implementable in large-scale clinical studies. As for specific clinical applications, estimated model parameters during OGTTs identified ethnic differences in common pathways to T2D between Pima Indians and Koreans.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
This document summarizes an online meeting about resources available from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to support researchers in implementing the new 2023 NIH data management and sharing policy. It discusses the NIDDK Central Repository, which acquires, maintains and distributes data and biospecimens from NIDDK-funded clinical studies. Eligibility and requirements for submitting resources to the repository are also covered.
dkNET Webinar: Postpartum Glucose Screening Among Homeless Women with Gestati...
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Rie Sakai-Bizmark, PhD. Assistant Professor, The Lundquist Institute at Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA
Abstract
Women with gestational diabetes mellitus (GDM) are at high risk of developing glucose intolerance after delivery. In the long term, women with GDM have a nearly 10-fold higher risk of developing type 2 diabetes mellitus (T2D) than women without GDM. The American Diabetes Association (ADA) and the American College of Obstetrics and Gynecology (ACOG) recommend that women with GDM undergo a 75-g oral glucose tolerance test (OGTT) between four and 12 weeks postpartum, and periodically thereafter. However, postpartum glucose screening (PGS) rate is historically low despite of various interventions to improve such rate. We hypothesized that PGS rate is lower among postpartum homeless women than their housed counterparts, and that interventions to improve PGS rate among postpartum homeless women with GDM should be tailored to their unique circumstances. The Japanese Society of Diabetes and Pregnancy (JSDP) modified the method to perform PGS with random plasma glucose (RPG) and glycated hemoglobin (HbA1c), which are simple and less invasive, to reduce the risk of COVID-19 infection by shortening the time spent in the hospital. RPG or HbA1c test do not require fasting. Therefore, homeless women who utilized care for other reasons could have the test as PGS. Given the barriers faced by homeless individuals, we hypothesize that RPG and HbA1c at healthcare utilizations during the postpartum period could be one of the strategies to identify high-risk individuals early because 1] healthcare utilizations are an opportunity for healthcare providers and social workers to educate homeless patients on GDM and their insurance eligibility and coverage for the screening, and 2] the physical barriers to health care access, which are often cited as a reason for the low PGS rate, are removed.
This proposed study will use administrative data from five states (AZ, CO, NC, NJ, and OR), which collectively include 9.3% of the US female homeless population. Each state will provide detailed, linked, multi-level, anonymized data for postpartum homeless women from four sources: 1] Medicaid claims; 2] Homeless Management Information System (HMIS); 3] birth records; and 4] the American Hospital Association (AHA) database to obtain hospital characteristics. With data from 2013 to 2020, an estimated sample size of 24,000 homeless women who delivered babies and 3,290 postpartum homeless women with GDM will be included.
First, we will estimate rates of GDM and PGS among homeless women. Second, we will estimate the cost-effectiveness of performing RPG and HbA1c tests...[Full abstract: https://dknet.org/about/blog/2581]
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies Analyzed with Linear Mixed Models
Presenter:
Kylie K. Harrall, MS, Research Instructor, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus
Abstract
Planning a reproducible study requires selecting a sample size which will ensure appropriate statistical power. Free point-and-click software (Kreidler et al., Journal of Statistical Software, 2013, 10.18637/jss.v054.i10) makes it easy to select a sample size for clustered and longitudinal designs with linear mixed models. The software, a suite of training modules, and reference materials are freely available online (www.SampleSizeShop.org ). The software interface and training materials are aimed at biomedical scientists, included those funded by NIDDK. We give examples of study designs for which the software will compute power and sample size, including a study with clustering, a study with longitudinal repeated measures, and a study with multiple outcomes, where heterogeneity of response among subgroups is of interest.
The top 3 key questions that the Sample Size Shop can answer:
1. What free, online, point-and-click, wizard-style, NIH-funded, validated, published power and sample size software provides calculations for studies with clusters, longitudinal studies, and longitudinal studies with clusters?
2. Can GLIMMPSE (www.SampleSizeShop.org) compute power and sample size for randomized controlled clinical trials and observational studies funded by NIDDK?
3. Why use validated power and sample size software instead of writing simulations?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...
Abstract
AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.
The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.
The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.
The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:
1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?
2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?
3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?
Presenters:
Dr. Felix Breden, Scientific Director, iReceptor
Dr. Brian Corrie, Technical Director, iReceptor
Dr. Kira Neller, Bioinformatics Director, iReceptor
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources (https://dknet.org/rin/research-data-management) that could help.
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar "The Mission and Progress of the(sugar)science: Helping Scienti...
Abstract
The(sugar)science was launched two years ago with the aim of helping scientists who study type 1 diabetes (T1D) and related interdisciplinary fields connect globally. We also wanted to create a digital space where trainees in the field can be supported, celebrated and connected to future positions. As part of our mission, our all volunteer team created the State of the Science series (2021. 2022), connecting global thought leaders around T1D research topics for discussion with a larger scientific audience. The second State of Science series was led by women scientists following the ADA publication which highlighted the paucity of women scientists in the leadership positions in the field.
To encourage the scientific community at large to dive into pre-existing data and pull out novel hypotheses that pertain to T1D, we created and together with dkNET, hosted D-Challenge 2021 and 2022. These competitions awarded $40K and $50K respectively to those who mined data and developed the most creative and testable hypothesis as judged by scientific experts in the field. These teams were also able to have an audience with the JDRFT1D Fund as part of a "pitch polish" which facilitated their interaction with venture capital.
To date, we have hosted over 200 interviews with T1D focused scientists in academia and industry and have an audience of 35K. Our reach on social media continues to grow and our metrics indicate a robust following. We share opportunities for positions in the field, engage and support trainees and together, our young scientific team published a paper, Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes, PLOS, February 2022.
We are currently engaged in the build of a T1D TCR Repository. We connected the AIRR data commons community with top TCR scientists in the field to begin this community based venture. It has the possibility to be incredibly instructive in defining the prodrome , which will further inform the field as it pertains to understanding the etiology of T1D.
Current team members that will join the discussion today will be Neha Mejety, Johns Hopkins University undergraduate and Tiffany Richardson, doctoral degree candidate at VUMC Diabetes.
The top 3 key questions that the(sugar)science can answer:
1. How can I find scientists to collaborate with in Type 1 diabetes research?
2. Where can I learn about Type 1 diabetes trending topics?
3. Where can I find forums to discuss novel ideas with scientists or key opinion leaders and find opportunities for Type 1 diabetes research ?
Presenters:
Monica Westley, PhD, Founder, the(sugar)science
Tiffany Richardson
Neha Majety
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What's new in dkNET 2.0
- 1. What’s new in dkNET 2.0 http://dknet.org
- 2. What’s New? • New look and feel • Mobile and tablet friendly • New data • New features For more information on how to use dkNET 2.0, see our tutorial
- 3. • Consolidated web page and data portal – To learn about dkNET, click the About menu New Look and Feel
- 4. Mobile and Tablet Friendly • dkNET will reconfigure itself for your phone or tablet • Use dkNET wherever you are!
- 5. New Data! • New data sources have been added to community resources – Software tools – Clinical trial data • NIDDK Central Repository New
- 6. Add your data or software resource to the dkNET Registry • Do you have a tool or data set that you’d like to share through dkNET? • Register your resource with dkNET
- 7. Community Funding Opportunities • dkNET has created a database of community- based funding opportunities for pilot projects and training
- 8. New Features • Select multiple facets – Select table view for any source – Expand a facet – Select desired facets – Click: Perform search – Facets by in situ hybridization OR transgenic Helpful Hints: If you don’t see the selection box, try expanding the window horizontally If you don’t see the “perform search” box, expand the window vertically or scroll down
- 9. See More Data • dkNET indexes more data than we can easily display in a single window • To see more of the data in Table View, select Options: See more columns • Select Options: Fullscreen to see the full table in a larger format Helpful Hint: Hidden columns are searched, so if you return what appears to be a false positive result, try revealing hidden columns
- 10. See who else is using data… • dkNET is built on the SciCrunch platform • SciCrunch connects different communities based on the data they use • Each community organizes data differently • Using shared data resources helps to share expertise across communities • Explore other data portals through the More Resources tab