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A commercial service organization that for DNA Methylation available for bisulfite treatment of DNA for methylation-specific analysis, histone, chromatin, and small RNA analysis and for the next era of DNA methylation detection and analysis.

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• SciCrunch
• 14 years ago - by Anonymous

An integrated software framework for efficient management, analysis and visualization of large amounts of gene expression data across replicated experiments., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 13 years ago - by Anonymous

Gene sequences and genomes of Bombus terrestris, Bombus impatiens, Apis mellifera and three of its pathogens, that are discoverable and analyzed via genome browsers, blast search, and apollo annotation tool. The genomes of two additional species, Apis dorsata and A. florea are currently under analysis and will soon be incorporated.BeeBase is an archive and will not be updated. The most up-to-date bee genome data is now available through the navigation bar on the HGD Home page.

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• SciCrunch
• 14 years ago - by Anonymous

Community built zebrafish RNAi platform that contains plasmids, successfully targeted genes and shRNA sequences, and a forum for discussion. This is a true community platform with users who add data, modify entiries, request features and share using the discussion board.

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• SciCrunch
• 14 years ago - by Anonymous

Longitudinal datasets of demographic, social, medical and economic information from a rural demographic in northern KwaZulu-Natal, South Africa where HIV prevalence is extremely high. The data may be filtered by demographics, years, or by individuals questionnaires. The datasets may be used by other researchers but the Africa Centre requests notification that anyone contact them when downloading their data. The datasets are provided in three formats: Stata11 .dta; tables in a MS-Access .accdb database; and worksheets in a MS-Excel .xlsx workbook. Datasets are generated approximately every six months containing information spanning the whole period of surveillance from 1/1/2000 to present.

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• SciCrunch
• 14 years ago - by Anonymous

A longitudinal, epidemiologic study to identify the common risk factors or characteristics that contribute to cardiovascular disease by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms or suffered a heart attack or stroke. Since that time the FHS has studied three generations of participants resulting in biological specimens and data from nearly 15,000 participants. Since 1994, two groups from minority populations, including related individuals have been added to the FHS. FHS welcomes proposals from outside investigators for data and biospecimens. The researchers recruited 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Since 1948, the subjects have continued to return to the study every two years for a detailed medical history, physical examination, and laboratory tests, and in 1971, the Study enrolled a second generation - 5,124 of the original participants'''' adult children and their spouses - to participate in similar examinations. In 1994, the need to establish a new study reflecting a more diverse community of Framingham was recognized, and the first Omni cohort of the Framingham Heart Study was enrolled. In April 2002 the Study entered a new phase, the enrollment of a third generation of participants, the grandchildren of the Original Cohort. In 2003, a second group of Omni participants was enrolled. Over the years, careful monitoring of the Framingham Study population has led to the identification of major CVD risk factors, as well as valuable information on the effects of these factors such as blood pressure, blood triglyceride and cholesterol levels, age, gender, and psychosocial issues. Risk factors for other physiological conditions such as dementia have been and continue to be investigated. In addition, the relationships between physical traits and genetic patterns are being studied. FHS clinical and research data is stored in the dbGaP and NHLBI Repository repositories and may be accessed by application. Please check the following repositories before applying for data through FHS. Investigators seeking data that is not available through dbGaP or BioLINCC or seeking biological specimens may submit a proposal through the FHS web-based research application. The FHS data repository may be accessed through this FHS website, under the For Researchers link, then Description of Data, in order to determine if and how the desired data is stored. Proposals may involve the use of existing data, the collection of new data, either directly from participants or from previously collected samples, images, or other materials (e.g., medical records). The FHS Repository also has biological specimens available for genetic and non-genetic research proposals. Specimens include urine, blood and blood products, as well as DNA.

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• SciCrunch
• 14 years ago - by Anonymous

A 20 year, 20,000 person, open longitudinal epidemiological study of a cohort town. GAZEL was not constructed to answer a specific question rather it was designed to help analyze a wide range of scientific problems and is accessible to the community of researchers specializing in epidemiology. Translation is not available for all pages. The GAZEL cohort, set up in 1989 by Inserm Unit 88 (subsequently Unit 687), in cooperation with several departments of ��lectricit�� de France-Gaz de France (EDF-GDF), was a public utility firm in France involved in production, transmission and distribution of energy. GAZEL initially included 20 624 volunteers working at EDF-GDF (15 010 men and 5614 women), aged from 35 to 50 years. In accordance with its purpose as a scientific research platform, the GAZEL cohort is permanently open to epidemiologic research teams. Today, more than 50 projects on very diversified themes have been set up in GAZEL by some 20 teams, French, belonging to different bodies, and foreign (Germany, Belgium, Canada, Great Britain, Sweden, Finland, and USA).

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• SciCrunch
• 14 years ago - by Anonymous

Software application allowing to simulate population genetics datasets. It allows generating genetic data for haploid, diploid, and haplodiploid organisms under a variety of mating systems. It includes various migration and mutation models. Output can be generated for the FSTAT, GENEPOP, and ARLEQUIN genetic analysis packages. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 14 years ago - by Anonymous

An Antibody supplier

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• SciCrunch
• 14 years ago - by Anonymous

A database and associated tools for investigating the genetic basis of neurodisability. It combines phenotype information from patients with neurodevelopmental and behavioral problems with clinical genetic data, and displays this information on the human genome map. Basic access to genetic information (deletions, duplications) relating to participants with neurodevelopmental disorders is provided without an account; access to the full dataset requires an account. The genetic information that is available to view comprises potentially pathogenic copy number variation across the genome, detected by array comparative genome hybridization (aCGH) using a customized 44K oligonucleotide array.

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• SciCrunch
• 14 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 27, 2015. Infrastructure for more effective scientific communication by leveraging the open APIs of GitHub, Amazon Web Services, and Synapse, clearScience demonstrates how scientists can easily transition from exploring dataexecuting scienceand providing the scientific community all the resources and artifacts to recreate analyses. By capturing the complete lifecycle of a project, reproducibility becomes a byproduct rather than a burden of publication. Further, we provide for forking an analysis, allowing anyone to explore and elaborate on "published" work. If the goal of biomedical research is to deliver results that will ultimately alleviate suffering and minimize harm to patients, being able to transparently share, reproduce, and build off of one another's work is critical to scientific progress. clearScience represents one compelling model for facilitating this progress.

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• SciCrunch
• 14 years ago - by Anonymous

An ontology for neuroimaging or medical imaging studies based on DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. Detailed description from web: Our aim is the design of a common semantic model providing a unified view on all data and tools to be shared between NeuroLOG partners. For this purpose, we built a multi-layered and multi-components formal ontology. We chose a design framework that structures the ontology at different levels of abstraction while respecting common conceptualization choices. At the highest level is a top-level ontology that includes abstract concepts and relationships valid across domains. We adopted DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. We then added Core ontologies, which provide generic, basic and minimal concepts and relations in a specific domain. By minimal we mean that core ontologies should include only the most reusable and widely applicable categories. These kinds of ontologies are essential for sharing intended meaning between different domains. We adopted I& DA (Information and Discourse Acts), a core ontology initially built for classifying documents as a function of their content.We use it to model medical images, which we consider as types of documents. Participant Roles is the core ontology we use to describe the modes of image participation in data processing. I& DA and Participant Roles are built according to DOLCE ontological commitments. On the basis of these two layers, we constructed our Domain ontology dedicated to conceptualizing a specific domain, in this case neuroimaging. Obviously, large domains such as neuroimaging can be divided into sub-domains for the sake of modularization.

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• SciCrunch
• 14 years ago - by Anonymous

A practical implementation of a novel compression approach that can rapidly compress FASTA files containing genomic sequence data in a loss-less fashion.

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• SciCrunch
• 13 years ago - by Anonymous

Data set of spine morphology parameters assembled from published literature published in Stepanyants, A. and Escobar, G., Statistical traces of long-term memories stored in strengths and patterns of synaptic connections, J Neuroscience, 31(21): 75797590 (2011). Data set is available as a pdf under the Resources tab.

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• SciCrunch
• 14 years ago - by Anonymous

An ImageJ plugin for morphological data mining, three-dimensional modeling and image stitching, registration, editing and annotation. Two independent modalities exist: either XML-based projects, working directly with the file system, or database-based projects, working on top of a local or remote PostgreSQL database. What can you do with it? * Semantic segmentation editor: order segmentations in tree hierarchies, whose template is exportable for reuse in other, comparable projects. * Model, visualize and export 3D. * Work from your laptop on your huge, remote image storage. * Work with an endless number of images, limited only by the hard drive capacity. Dozens of formats supported thanks to LOCI Bioformats and ImageJ. * Import stacks and even entire grids (montages) of images, automatically stitch them together and homogenize their histograms for best montaging quality. * Add layers conveniently. A layer represents, for example, one 50 nm section (for TEM) or a confocal section. Each layer has its own Z coordinate and thickness, and contains images, labels, areas, nodes of 3d skeletons, profiles... * Insert layer sets into layers: so your electron microscopy serial sections can live inside your optical microscopy sections. * Run any ImageJ plugin on any image. * Measure everything: areas, volumes, pixel intensities, etc. using both built-in data structures and segmentation types, and standard ImageJ ROIs. And with double dissectors! * Visualize RGB color channels changing the opacity of each on the fly, non-destructively. * Annotate images non-destructively with floating text labels, which you can rotate/scale on the fly and display in any color. * Montage/register/stitch/blend images manually with transparencies, semiautomatically, or fully automatically within and across sections, with translation, rigid, similarity and affine models with automatically extracted SIFT features. * Correct the lens distortion present in the images, like those generated in transmission electron microscopy. * Add alpha masks to images using ROIs, for example to split images in two or more parts, or to remove the borders of an image or collection of images. * Model neuronal arbors with 3D skeletons (with areas or radiuses), and synapses with connectors. * Undo all steps. And much more...

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• SciCrunch
• 14 years ago - by Anonymous

A large multi-site pediatric MRI and genetics data resource to facilitate studies of the genomic landscape of the developing human brain. It includes information about the developing mental and emotional functions of the children to understand the genetic basis of individual differences in brain structure and connectivity, cognition, and personality. Investigators on the project are studying 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined. Investigators interested in the effects of a particular gene will be able to search the database for any brain areas or connections between areas that differ as a function of variation in a particular gene, and also to determine if the genes appear to affect the course of brain development at some point during childhood. A data exploration tool has been created for mapping and analyzing MRI data sets collected for PING and related developmental studies. Approved investigators will be able to view raw image sets and derived 3D brain maps of MRI and DTI data, conduct hypothesis testing, and graph brain area measures as they change across the time course of development. PING Cores * Coordinating Core: Functions include project management, screening of participants and maintaining the database * Neuroimaging Core: applying a standardized high-resolution structural MRI protocol involving 3-D T1-weighted scans, a T2-weighted volume, and a set of diffusion-weighted scans with multiple b values and diffusion directions, scans to estimate MRI relaxation rates, and gradient echo EPI scans for resting state fMRI. Importantly, adaptive motion compensation, using ����??PROMO����??, a novel real-time motion correction algorithm will be used. Specific PING protocols for each scanner manufacturer: ** PING MRI Protocol - GE ** PING MRI Protocol - Philips ** PING MRI Protocol - Siemens * Assessment Core: Cognitive assessments for the PING project are conducted using the NIH Toolbox for Cognition. * Genomics Core: functions as a central repository for receipt of saliva samples collected for each study participant. Once received, samples are catalogued, maintained, and DNA is extracted using state-of-the-field laboratory techniques. Ultimately, genome-wide genotyping is performed on the extracted DNA using the Illumina Human660W-Quad BeadChip. PING involves 10 sites throughout the country including UCSD, University of Hawaii, Scripps Genomics, UCLA, UC Davis, Kennedy Krieger Institute/Johns Hopkins, Sacker Institute/Cornell University, University of Massachusetts, Massachusetts General Hospital/Harvard, and Yale. Families who may want to participate in the study, or others who want to know more about it, may email questions to ping (at) ucsd.edu.

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• SciCrunch
• 13 years ago - by Anonymous

An Antibody supplier

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• SciCrunch
• 14 years ago - by Anonymous

A network to improve measurement of biological risk for late life health outcomes in large representative samples of populations. Activities of the network include designing and carrying out a series of focused meetings, interactive activities, workshops, and pilot projects to harmonize and develop measurement of biological risk in populations. This project will improve the methods of measuring health used in populations and improve comparability of results over time and across studies, which is important for monitoring population health. Biological risk represents objective measurement of major dimensions of population health. The level of risk can indicate the health of the population, need for health care treatment in a population, and the effectiveness of that treatment in controlling risk or delaying disease progression, and death. The measurement of biological risk in large populations often requires adoption of methods not used in laboratory settings. The overarching goal of the network is to promote interdisciplinary research that clarifies the biological paths to health outcomes that can be measured or monitored in population surveys. The network will address the following questions: * What array of biological markers can be included reliably and validly in population studies in order to better monitor health and predict health outcomes at the older ages? * What are the best methods of collecting biological risk information under a variety of circumstances? * What are the best methods for processing the biological risk information collected? * What methods of harmonization will allow us to compare biological risk across studies? * What are the best approaches to measurement of cumulative biological risk or dimensions of biological risk for a variety of health outcomes in a variety of settings? * What are the best approaches in including indicators of genetic risk for complex diseases and conditions into data from population-based surveys? * How do we best capture indicators of life-long social, psychological and economic conditions along with lifelong biological risk to explain later life health outcomes? * What particular ethical issues are posed by our linking of biological data to extensive social, psychological, and economic information? A dataset of descriptions of Selected Population Studies with Biomarkers is available.

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• SciCrunch
• 14 years ago - by Anonymous

A longitudinal, population-based study of health and social factors, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health and illness, medication use, cognitive function, emotional health, sensory function, health behaviors, social connectedness, sexuality, and relationship quality. NSHAP provides policy makers, health providers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The study contributes to finding new ways to improve health as people age. In 2005 and 2006, NORC and Principal Investigators at the University of Chicago conducted the first wave of NSHAP, completing more than 3,000 interviews with a nationally representative sample of adults aged 57 to 85. In 2010 and 2011, nearly 3,400 interviews were completed for Wave 2 with these Wave 1 Respondents, Wave 1 Non-Interviewed Respondents, and their spouses or cohabiting romantic partners. The second wave of NSHAP is essential to understanding how social and biological characteristics change. NSHAP, by eliciting a variety of information from respondents over time, provides data that will allow researchers in a number of fields to examine how specific factors may or may not affect each other across the life course. For both waves, data collection included three measurements: in-home interviews, biomeasures, and leave-behind respondent-administered questionnaires. The face-to-face interviews and biomeasure collection took place in respondents'''' homes. NSHAP uses a national area probability sample of community residing adults born between 1920 and 1947 (aged 57 to 85 at the time of the Wave 1 interview), which includes an oversampling of African-Americans and Hispanics. The NSHAP sample is built on the foundation of the national household screening carried out by the Health and Retirement Study (HRS) in 2004. Through a collaborative agreement, HRS identified households for the NSHAP eligible population. A sample of 4,400 people was selected from the screened households. NSHAP made one selection per household. Ninety-two percent of the persons selected for the NSHAP interview were eligible. For Wave 2 in 2010 and 2011, NSHAP returned to Wave 1 Respondents and eligible non-interviewed respondents from Wave 1 (Wave 1 Non-Interviewed Respondents). NSHAP also extended the Wave 2 sample to include the cohabiting spouses and romantic partners of Wave 1 Respondents and Wave 1 Non-Interviewed Respondents. Partners were considered to be eligible to participate in NSHAP if they resided in the household with the Wave 1 Respondent/Wave 1 Non-Interviewed Respondent at the time of the Wave 2 interview and were at least 18 years of age. Wave I biomeasures: height; weight; waist circumference; blood pressure; smell; taste; vision; touch; respondent-administered vaginal swabs; oral mucosal transudate (OMT) for HIV-1 antibody screening; saliva; ����??get up and go����??; and blood spots. Technological advances in biomeasure collection methods have decreased respondent burden and increased ease of collection, storage, and yield of various biomeasures for the second wave of NSHAP. Wave II biomeasures: anthropometrics, including height, hip and waist circumference, and weight; cardiovascular function, including blood pressure, heart rate variability, and pulse; 2 of the 3 components of the short physical performance battery (SPPB) including chair stands and a timed walk; sensory function including smell; and actigraphy. In addition, we collect dried blood spots, microtainer blood, passive drool and salivettes, urine, and respondent-administered vaginal swabs, each of which are analyzed using multiple assays for a variety of measures and rationales. Furthermore, we assess respondents����?? cognition using the Montreal Cognitive Assessment (MoCA). Data Availability: NSHAP data made available to the public does not contain any identifiable respondent information and uses code numbers instead of names for all data. De-identified data from the 2005 and 2006 interviews are available to researchers through the National Archive of Computerized Data on Aging, located within Inter-University Consortium for Political and Social Research (ICPSR). Data from the Wave 2 interviews in 2010 and 2011 will be available in the summer of 2012. * Dates of Study: 2005-2006, 2010-2011 * Study Features: Biospecimens, Anthropometric Measures * Sample Size: ** Wave 1: 3,005 ** Wave 2: 3,377 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/20541

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• SciCrunch
• 14 years ago - by Anonymous

Software application to simulate sequences drawn from a population under the Wright-Fisher neutral model. The purpose of this program is to simulate sequences on the whole genome scale within practical time. (entry from Genetic Analysis Software)

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• SciCrunch
• 14 years ago - by Anonymous