11943 results for "division 2 coins fc 26 Visit Buyfc26coins.com Kupiłem(a) duży pakiet monet FC 26 i dostałem(a) dodatkowy rabat. Świetnie!.fXzt"

in 13.27 milliseconds.

• scRNAseq of human primary nasal epithelium differentiated at air-liquid interface exposed to SARS-CoV2

  The nasal epithelium is a plausible entry point for SARS-CoV-2, a site of pathogenesis and transmission, and may initiate the host response to SARS-CoV-2. Antiviral interferon responses are critical to outcome of SARS-CoV-2. Yet little is known about the interaction between SARS-CoV-2 and innate immunity in this tissue. Here we applied single-cell RNA sequencing and proteomics to a primary cell model of human primary nasal epithelium differentiated at air-liquid interface.

  Study EGAS00001005633

• DAC for Early detection of ovarian cancer using cell-free DNA fragmentomes and protein biomarkers

  Ovarian cancer is a leading cause of death from gynecological malignancies worldwide. No effective screening methods exist resulting in tumor detection at advanced stages where treatment is less effective. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker (CA-125 and HE4) analyses to evaluate 591 women, mostly post-menopausal, from the European Union or the United States with ovarian cancer, benign adnexal masses, or without ovarian lesions. Using a machine learning model that incorporated multi-analyte fragmentome data and protein measurements, we evaluated the detection of ovarian cancer in two clinical settings, detection of ovarian cancers among a healthy population, and classification of adnexal masses as benign or malignant. The combined approach detected ovarian cancer with specificity >99% and sensitivity of 72%, 69%, 87%, and 100% for stages I–IV, respectively (AUC=0.96, 95% CI: 0.94-0.99), including 90% of high grade serous ovarian cancers. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100% of ovarian cancers for stages I–IV (p=0.001, two-sided test of equal proportions). Additionally, our approach differentiated benign masses from ovarian cancers with high accuracy (AUC of 0.88, 95% CI=0.83-0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation.

  Dac EGAC50000000332

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? TThe most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• ARRA - NHLBI Lung Cohorts Sequencing Project: Genetic modifiers of

  The major goal of this project is to apply second generation resequencing technology to identify disease causing variants influencing pediatric and adult lung diseases in a collection of two longitudinal population cohorts of cystic fibrosis patients that have been well characterized for a comprehensive set of clinical traits. In Phase I, exome sequencing was performed on 43 cystic fibrosis patients with early Pa infection and 48 cystic fibrosis patients with late Pa infection to identify variants influencing the time to onset of Pa infection. In Phase II, additional exomes were added to the study, to reach a total of 91 individuals with early Pa infection and 96 with late Pa infection. The majority of the 340 subjects of Phase II do not have a Pa infection phenotype, but instead have a pulmonary function phenotype (121 severe vs. 124 mild impairment) as determined by the survival corrected Kulich FEV percentile of Corey et al. A small minority have intermediate phenotypes and/or show severe decline in lung function during childhood.

  Study phs000254

• SNP array files, IDAT files, from 34 members of a Family with a high prevalence of psychosis

  We investigated the genetic causes of major mental disorders (MMDs) including schizophrenia, bipolar disorder I, major depressive disorder, and attention deficit hyperactive disorder, in a large family pedigree from Alpujarras, South of Spain, a region with a high prevalence of psychotic disorders. We applied a systematic genomic approach based on karyotyping (N=4), genotyping by genome-wide SNP array (N=33), whole-exome sequencing (N=4), and whole-genome sequencing (N=12). We performed genome-wide linkage analysis, family-based association analysis, and polygenic risk score estimates. Significant linkage was obtained at chromosome 9 (9q33.1-33.2, LOD score= 4.11), a suggestive region that contains five candidate genes ASTN2, BRINP1, C5, TLR4, and TRIM32, previously associated to MMDs. Comprehensive analysis associated the MMD phenotype with genes of the immune system with dual brain functions. Moreover, the psychotic phenotype was enriched for genes involved in synapsis. These results should be considered once studying the genetics of psychiatric disorders in other families, especially the ones from the same region, since founder effects may be related to the high prevalence.

  Study EGAS00001004592

• Research Program on Genes, Environment and Health (RPGEH)

  The Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) is a resource developed to facilitate research on genetic and environmental factors on common diseases and healthy aging. The RPGEH resource links biospecimens, health surveys, and comprehensive electronic medical records on broadly consented adult members of Kaiser Permanente Medical Care Plan, Northern California Region (KPNC). KPNC is an integrated health care delivery system with a membership of approximately 3.3 million people in northern California. The membership of KPNC is representative of the general population in the 14 county area in which facilities are located, although extremes of income are underrepresented. At the end of 2013, the RPGEH resource included: (1) demographic and behavioral surveys from over 430,000 participants; (2) biospecimens (DNA, serum, plasma, and/or saliva) from over 204,000 participants, including over 13,000 pregnant women; (3) genome-wide genotype data (70 billion SNP genotypes) on over 100,000 participants, including the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort; and (4) the longitudinal electronic medical records of the participants. The RPGEH was developed beginning in 2005 at the Division of Research of Kaiser Permanente Northern California by Catherine Schaefer (Director), Neil Risch (Co-Director), Lisa Croen, Eric Jorgenson, Lawrence Kushi, Charles Quesenberry, Sarah Rowell, Carol Somkin, Stephen Van den Eeden, Larry Walter, and Rachel Whitmer. Funding of the RPGEH was provided to C. Schaefer (PI) and N. Risch (co-PI) by the Wayne and Gladys Valley Foundation, The Ellison Medical Foundation, the Robert Wood Johnson Foundation, Kaiser Permanente Northern California, and the Kaiser Permanente National and Regional Community Benefit Programs. The GERA cohort was funded by a grant from NIH to RPGEH and UCSF (RC2 AG036607; C. Schaefer and N. Risch, PIs). At the time of the award of the RC2 project in late 2009, the RPGEH had established a cohort of about 140,000 individuals who had answered a detailed survey, provided saliva samples for extraction of DNA, and given broad consent for the use of their data in studies of health and disease. Survey and Cohort Recruitment. Initially, the RPGEH developed electronic disease registries to enable identification of phenotypes, using algorithms applied to EMR data. In 2007, the RPGEH mailed a four page survey to 1.9 million adult (≥ 18 years old) members of KPNC who had been members for two years or more, to obtain data on demographic and behavioral factors complementary to the clinical data in the EMR. The survey materials included a cover letter introducing the RPGEH, a two page list of Frequently Asked Questions, and the survey, which included questions on demographic factors such as education, race-ethnicity, income and marital status, dietary factors, physical activity, smoking, and alcohol consumption, as well as reproductive history and reproductive health. Members whose electronic medical records indicated a preference for written communications in Chinese or Spanish received survey materials both in English and a Chinese or Spanish translation. Approximately 400,000 completed surveys were returned. Saliva Sample Collection. Beginning in July 2008, respondents to the survey were asked to sign and return a consent form and authorization for use and disclosure of protected health information. The consent form authorized broad use of biospecimens, survey data, and data from participants' electronic health records for use in studies of genetic and environmental influences on health and disease. Respondents who returned completed consent forms were mailed (Oragene) saliva collection kits; more than 132,000 saliva samples were collected in two years. Completed saliva kits were scanned and archived in a temporary biorepository at the KPNC Division of Research. In late 2009, the RPGEH began collection of saliva samples from the California Men's Health Study (CMHS), a cohort that had been previously assembled in 2002-2003 and had been excluded from the RPGEH survey mailing with the intent of later adding CMHS participants to the assembled RPGEH cohort. The CMHS was developed to facilitate research on prostate cancer and other conditions in older men; the study protocol is described in Enger, et al., 2006. It enrolled and surveyed more than 40,000 men in KPNC, ages 45-69 years, who were members of KPNC during 2002-2003. CMHS men completed two mailed surveys with demographic and behavioral data similar to that of the RPGEH. The data on analogous variables were reconciled and integrated with the data derived from the RPGEH cohort for use in the RPGEH resource. By 2011, RPGEH collected approximately 15,400 saliva samples from men participating in the CMHS. RPGEH Access and Collaborations Website and Procedures. The RPGEH maintains a web portal for inquiries and applications for collaboration and access to data. The url is: https://rpgehportal.kaiser.org/. RPGEH has an application process and an Access Review Committee that reviews applications for collaboration and use. For more information, please contact RPGEH through the website.

  Study phs000788

• Persistence of circulating tumor DNA in breast cancer patients during neoadjuvant treatment is a significant predictor of poor tumor response

  Purpose:Accurate assessment of response during neoadjuvant systemic treatment (NST) poses a major clinical challenge. Therefore, a minimally-invasive assessment of tumor response based on cell-free circulating tumor DNA (ctDNA) may be beneficial to guide treatment decisions.Experimental Design: We profiled 93 genes in tissue from 193 early breast cancer patients. Patient-specific assays were designed to track ctDNA during NST from 145 patients with available plasma. ctDNA presence and levels were correlated with complete pathological response (pCR) and residual cancer burden (RCB) as well as with clinicopathologic characteristics of the tumor to identify potential proxies for ctDNA release. Moreover, we tested the predictive value of driver events identified in the tumor.Results:At baseline, ctDNA could be detected in 63/145 (43.4%) patients and persisted in 25/63 (39.7%) patients at mid-therapy (MT) and 15/63 (23.8%) patients at the end of treatment. ctDNA detection at MT was significantly associated with higher RCB (OR 0.062, 95% CI 0.01-0.48, P=0.0077). Out of 31 patients with detectable ctDNA at MT, 30 patients (96.8%) were non-responders (RCB II, n=8; RCB III, n=22) and only one patient responded to the treatment (RCB I). Considering all 145 patients with baseline (BL) plasma, none of the patients with RCB 0 and only 6.7% of patients with RCB I had ctDNA detectable at MT, while 30.6% and 29.6% of patients with RCB II/III, respectively, had a positive ctDNA result. Conclusion:Overall, our results demonstrate that the detection and persistence of ctDNA at mid-therapy may have the potential to negatively predict response to neoadjuvant treatment and identify patients who will not achieve pCR or be classified with RCB II/III.

  Study EGAS00001005798

• Jackson Heart Study (JHS-BioLINCC)

  Accessing Data: Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.Related Studies: Other Jackson Heart Study data available include: Imaging studies (JHS-Imaging), Genetics and genomics (The Jackson Heart Study, phs000286.v7.p2), and Collaborative Cohort of Cohorts for COVID-19 Research (C4R-JHS, phs002907.v1.p1): Jackson Heart Study (phs002907.v1.p1).Available Data: Data available for request include Jackson Heart Study visit 1-3 examination cycles, collated annual follow-up communication data through 2016, and follow-up for mortality, heart disease, and stroke events through 2014.Objectives: The objectives of the Jackson Heart Study are to: 1) investigate the associations of biological, psychosocial, and behavioral factors with the incidence atherosclerotic events and health outcomes in an African American cohort; and 2) increase access to and the participation of African American populations and scientists in biomedical research and professions.Background: It has long been recognized that African Americans share a disproportionate burden of deleterious health outcomes including diabetes, hypertension, kidney disease and early onset of cardiovascular disease. The Jackson Heart Study was initiated in 2000 to explore potential mechanisms and mediators of health outcomes in a large African American cohort. In addition, the JHS conducts a variety of community education and outreach activities to promote healthy lifestyles to reduce disease risk burden and student training programs to promote and support public health research.Participants: African American men and women, age 35-84 at entry. Of the 5306 cohort members enrolled in the study, the data repository contains data from 3,883 that provided informed consent to share their data with investigators not affiliated with the study.Design: Participants were enrolled in the study from 2000-2004 from urban and rural areas of three counties (Hinds, Madison and Rankin) in the Jackson, MS metropolitan statistical area. Participants were enrolled from each of 4 recruitment pools: a random sample component (17%), volunteer component (30%), currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study (31%), and secondary family members (22%). Recruitment was limited to non-institutionalized adult African Americans age 35-84 years, except in the family cohort where those age ≥21 years were eligible. The final cohort of 5,306 participants includes 6.59% of all African American residents aged 35-84 (N=76,426, US Census 2000). Data collection at the baseline exam included a medical history, physical examination, blood/urine analytes and interview questions on areas such as: physical activity; stress, coping and spirituality; racism and discrimination; socioeconomic status; and health care access.The current release of the Jackson Heart Study includes data collected at baseline, exam 2 (2005-2008), and exam 3 (2009-2013). Annual follow-up and surveillance of clinical cardiovascular events is ongoing.

  Study phs003740

• Genetic Studies of Chronic Kidney Disease (CKD)

  The Genetic Studies of Chronic Kidney Disease (CKD) is an ongoing genetic research and biobanking protocol which aims to elucidate the genetic basis CKD. The study recruits patients seen at the Columbia University Medical Center (CUMC) Nephrology Division for the evaluation and care of CKD. All patients clinically diagnosed with CKD are eligible to participate; there are no inclusion or exclusion criteria related to age, sex/gender, and/or race/ethnicity. In addition to office visits, patients are recruited in contexts including perinatal care and nonprofit events for patients with various forms of CKD; recruitment tools include bilingual informational brochures, flyers, and consent forms. All individuals provide informed consent; for minors, signature of the parent/guardian is required. Consent includes: use of biosamples given for genetic studies, and the request to be re-contacted if medically actionable genetic findings are identified. In the case of the latter, such research-level findings are first validated in a clinically certified (CLIA) laboratory environment, using a second sample given by the individual at the time of re-contact; following CLIA validation, the individual is once again re-contacted, and, assuming he or she still desires return of these results, they can then be returned. Following the above consent process to participate in the study, individuals provide a single biosample (generally, a venous blood sample), from which DNA is extracted using standard protocols and used for genetic studies, such as exome sequencing. Basic clinical and demographic data, including sex/gender, age, and race/ethnicity are also collected. All data collected is stored in a secure computerized database, with patient samples anonymized using numerical identifiers; the relation of this identifier to patients' names is kept in a locked computer file in a centralized database. Electronic data complies with all HIPAA and other privacy and security regulations, and resides on a firewall server maintained by the CUMC IT department.

  Study phs001828

• Cellular and Molecular Characterization of Renal Cell Carcinoma

  We are performing a comprehensive cellular and molecular characterization of renal cell carcinoma. This work includes an initial cohort of patients whose tumors (and adjacent normal tissue) underwent single cell analysis. Single-cell RNA sequencing (scRNA-seq) and TCR sequencing (scTCR-seq) analysis of untreated clear cell renal cell carcinoma tumor and adjacent normal tissue was performed across disease stages. This initial cohort includes 13 patients: 4 with early stage disease (stage I/II), 4 with locally advanced disease (stage III), and 5 with advanced/metastatic disease (stage IV). Whole exome sequencing on tumor and normal tissue was also performed. 

  Study phs002252