The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Extraction of Heavy Metals from Soil Affected by Landfill Leachate through Constructed Wetlands: A Phytoremediation Approach to Rejuvenating the Contaminated Environment

Water is one of the most essential elements of life. The water shortage is becoming a lurid issue in many regions, with over a billion people without passable water for drinking purposes. The leachate from landfill sites is a major problem and poses a threat to aquatic ecosystems and public health. To overcome this situation, either to remove contaminants or to reduce the amount of contamination, constructed wetlands using phytoremediation can be considered the best solution. This green low-cost technology uses plants to remove heavy metals from soil and water. The objective of this report is to study the removal of specific heavy metals such as Zinc (Zn), Nickel (Ni), Chromium (Cr), Cadmium (Cd), Iron (Fe) and Lead (Pb) from landfill leachate by using two laboratory scaled wetlands. These wetlands were filled with soil and planted with Typha Latifolia. One system was operated without recirculation and the other with effluent recirculation with an interval of one day. The influent and effluent physicochemical parameters were analyzed after 30 days and the concentrations of the heavy metals were observed. The wide variation is seen in the case of Nickel, Lead, Chromium, Cadmium, Zinc and Iron. The percentage of removal with recirculation and without recirculation is 100% for Cadmium and Iron, in the case of Nickel, Lead, Chromium and Zinc the percentage difference between recirculation and without recirculation was found to be 1.6, 2.4, 0, 0. Since the removal efficiency for Cadmium and Iron is predominant this study indicates that this technology gives good removal of heavy metals and has a scope for its effective analysis since it has low working and conservation costs; it is comparatively a step toward a sustainable economy

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Not AvailableThe present study describes prevalence of peste des petits ruminants (PPR) virus infection in goats in various parts of North-East (NE) India by screening of suspected serum samples collected during outbreak investigation and random samples during 2013 to 2014 survey. A total of 391 serum samples (318 random and 73 outbreak/suspected) were collected from 28 districts in 7 states (Meghalaya, Assam, Manipur, Nagaland, Arunachal Pradesh, Tripura and Mizoram) of NE India. Serum samples were screened for PPRV-specific antibodies by using PPR monoclonal-antibody based competitive ELISA. Analysis of 391 serum samples indicates that an overall seroprevalence of 17.90 [CI 95 14.40–22.00] in goats (45.2 in suspected [CI 95 34.32–56.58] and 11.63 in random [CI 95 8.56–15.63] samples) in NE India. As expected prevalence was high in outbreaks vis-à-vis random samples. The random survey results (11.63 ) has specific implication in epidemiological perspectives, since it highlights the exact PPR prevalence under natural situations, where the subclinical, in apparent or nonlethal or recovery of infection was suspected in goats, as samples were collected from unvaccinated animals. It also warrants appropriate control measures against PPR in NE region to prevent spread of infection besides widespread presence of the disease in rest of India.Not Availabl

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Not AvailableIn this study, epidemiological investigation of the three outbreaks of peste des petits ruminants (PPR) in goats and sheep flocks with high morbidity and considerable mortality were recorded at Tumkur District, Karnataka, India during 2012-2013. Clinical samples were collected from the affected flocks/villages for laboratory investigation along with epidemiological parameters. The PPR virus (PPRV) antigen and nucleic acid was detected in the infected tissues/swab materials by sandwich enzyme-linked immunosorbent assay (ELISA) and reverse transcription-polymerase chain reaction techniques (RT-PCR) and PPRV-specific antibodies were detected by competitive ELISA and indirect ELISA in serum samples. The PPR virus was isolated successfully in Vero cells after initial adaptation, and its identity was confirmed by RT-PCR assays. The significant epidemiological parameters observed were: young animals aged between 6 month to one year were severely affected than adult animals, which showed only few symptoms pertaining to PPR; changing pattern of disease in term of severity of gross lesions was observed in affected animals; source of infection was the introduction or purchase of animals from other source; etc. Given the importance of PPR and high productivity losses in small ruminants, outbreaks of the disease need to be carefully monitored, in spite of vaccination of the sheep and goats is undergoing under the National control programme on PPR.Not Availabl

The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19