155,443 research outputs found
Enrichment and characterization of a bacteria consortium capable of heterotrophic nitrification and aerobic denitrification at low temperature
No full textNitrogen removal in wastewater treatment plants is usually severely inhibited under cold temperature. The present study proposes bioaugmentation using psychrotolerant heterotrophic nitrification-aerobic denitrification consortium to enhance nitrogen removal at low temperature. A functional consortium has been successfully enriched by stepped increase in DO concentration. Using this consortium, the specific removal rates of ammonia and nitrate at 10 degrees C reached as high as 3.1 mg N/(g SS h) and 9.6 mg N/ (g SS h), respectively. PCR-DGGE and clone library analysis both indicated a significant reduction in bacterial diversity during enrichment. Phylogenetic analysis based on nearly full-length 16S rRNA genes showed that Alphaproteobacteria. Deltaproteobacteria and particularly Bacteroidetes declined while Gammaproteobacteria (all clustered into Pseudomonas sp.) and Betaproteobacteria (mainly Rhodoferax ferrireducens) became dominant in the enriched consortium. It is likely that Pseudomonas spp. played a major role in nitrification and denitrification, while R. ferrireducens and its relatives utilized nitrate as both electron acceptor and nitrogen source. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.</p
PROV-N: The Provenance Notation
No full textPara dar ejemplos del modelo de datos PROV, se presenta la notación PROV (PROV-N) que está dirigida al uso humano, PROV-N permite serializaciones de instancias PROV para que se creen de una manera compacta. PROV-N facilita la asignación del modelo de datos PROV a una sintaxis concreta, y se utiliza como base para una semántica formal de PROV. El propósito de este documento es definir la notación PROV-N.W3
Defining neurocovid: an interdisciplinary analysis of computational approaches to sample prioritisation and disease definition in the neurocov consortium
No full textIntroduction and rationale:
NeuroCovid is a novel disease characterized by persistent fatigue, memory impairment, cognitive dysfunction and neurodegeneration from four months post COVID-19 infection. However, its biomedical definition, diagnostic criteria, and underlying mechanisms remain elusive. To address this gap, this work is situated at the intersection of molecular biology and sociology of science; its object of investigation is the development of a medical nosology for NeuroCovid. Using the NeuroCOV consortium as a case study - a large-scale European initiative employing neuropsychological tests, multi-omics, and patient-derived hiPSCs and organoid models on a multinational cohort - we examine how social and experimental decisions shape this emerging disease category.
Methods:
Occupying a dual role within NeuroCOV as molecular biologist and social scientist, I adopt an auto-ethnographic approach to critically analyze the research process from within. I focus here on the computational prioritisation strategy that selects patients’ samples for downstream reprogramming. This strategy integrates transcriptomic data from peripheral blood mononuclear cells (PBMCs) and neuropsychological assessments collected from NeuroCOV participants.
Results:
Preliminary findings describe NeuroCOV’s emerging sample selection strategy and its epistemological implications. This involves applying single-cell interpretable tensor decomposition (scITD) to PBMC transcriptomics from over 200 participants to identify patterns of coordinated gene expression across cell types. Participants are clustered based on these patterns, and clusters are linked to neuropsychological assessments via machine learning to guide sample prioritisation for hiPSC reprogramming. The resulting approach heralds epistemic parity between molecular and behavioral data in defining NeuroCovid.
Conclusion:
This work highlights how, within NeuroCOV, sample selection is pivotal in shaping which biological profiles will define NeuroCovid’s signature. By integrating omic data with neuropsychological assessments, and leveraging AI-based tools to guide prioritisation, this approach fosters an integrative nosology where molecular and behavioural data jointly co-define the emerging disease entity of NeuroCovid
CICE-Consortium/CICE: CICE Version 6.5.0
No full text<p>CICE6.5.0 updates CICE6.4.2 from September 2023 and includes Icepack1.4.0.</p><p>This release updates the C-grid solver to make it more robust. It also includes the first updates in Icepack from the E3SM integration. Some Icepack interfaces have changed. Other important changes include the frazil coupling implementation, addition of the 5-band snicar shortwave feature, and an update of the JRA55 tx1 grid forcing. Finally, the cicedynB directory has been removed and renamed to cicedyn.</p><p><strong>Important NOTES</strong></p><ul><li>This release improves the robustness of the C-grid solver and can be considered the first fully supported release of the C-grid feature.</li><li>Icepack was updated with non-backward-compatible interface changes. More details can be seen in the Icepack1.4.0 release notes.</li><li>CICE tx1 grid JRA55 forcing from prior versions was incorrect, this has been fixed.</li></ul><p><strong>Major Changes</strong></p><ul><li>Update areafact calculation remapping advection and set l_fixed_area to true for C grid solutions (<strong>This changes answers slightly with remap advection and improves the C-grid solutions</strong>) <a href="https://github.com/CICE-Consortium/CICE/pull/849">#849</a></li><li>Update Icepack to version with E3SM initial integration modification. Update CICE to be compatible. (<strong>This changes temperature values on restart files where ice does not exist but is bit-for-bit otherwise. This also changes some Icepack intefaces</strong>) <a href="https://github.com/CICE-Consortium/CICE/pull/879">#879</a><ul><li>Update snicar shortwave/aerosol optics implementation, add snicar tests</li><li>Add Tf argument to several Icepack subroutine interfaces</li><li>Cleanup/remove several static Icepack subroutine arguments</li><li>Update snow physics control</li><li>Update CICE driver and coupling layers as needed</li></ul></li><li>Update the tfrz_options settings for some configurations (<strong>This changes answers for those configurations</strong>) <a href="https://github.com/CICE-Consortium/CICE/pull/883">#883</a></li></ul><p><strong>Bug fixes</strong></p><ul><li>Update the JRA55 tx1 atm forcing dataset <a href="https://github.com/CICE-Consortium/CICE/pull/876">#876</a></li><li>Fix bug in lateral melt in Icepack (<strong>This changes all results</strong>) <a href="https://github.com/CICE-Consortium/CICE/pull/902">#902</a></li><li>Fix error in the computation of N and E grid values when TLON, TLAT, ANGLET are on the CICE grid file. <a href="https://github.com/CICE-Consortium/CICE/pull/899">#899</a></li><li>Fix the mesh check method in the nuopc/cmeps coupling layer <a href="https://github.com/CICE-Consortium/CICE/pull/873">#873</a></li></ul><p><strong>Enhancements</strong></p><ul><li>Add cpl_frazil and update use of update_ocn_f consistent with upgrades to Icepack <a href="https://github.com/CICE-Consortium/CICE/pull/889">#889</a></li><li>Add atm_data_version namelist to control JRA55 atm forcing file versioning <a href="https://github.com/CICE-Consortium/CICE/pull/876">#876</a></li><li>Update computation of intermediate quantities for the seabed stress (<strong>This changes answers for C-grid with seabed stress turned on</strong>) <a href="https://github.com/CICE-Consortium/CICE/pull/893">#893</a></li><li>Change nuopc/cmeps coupling layer to use CESM style field names <a href="https://github.com/CICE-Consortium/CICE/pull/869">#869</a></li><li>Add single channel test cases <a href="https://github.com/CICE-Consortium/CICE/pull/875">#875</a></li><li>Port to Perlmutter <a href="https://github.com/CICE-Consortium/CICE/pull/882">#882</a></li><li>Add CPP NO_SNICARHC to turn off hardcoded 5-band dEdd tables in Icepack. By default this is on. <a href="https://github.com/CICE-Consortium/CICE/pull/886">#886</a></li><li>Remove the cicedynB link, provided temporarily for backwards compatibility when the directory was renamed to cicedyn. <a href="https://github.com/CICE-Consortium/CICE/pull/887">#887</a></li><li>Update input data path on Derecho <a href="https://github.com/CICE-Consortium/CICE/pull/890">#890</a></li><li>Update Icepack <a href="https://github.com/CICE-Consortium/CICE/pull/879">#879</a>, <a href="https://github.com/CICE-Consortium/CICE/pull/886">#886</a>, <a href="https://github.com/CICE-Consortium/CICE/pull/902">#902</a>, <a href="https://github.com/CICE-Consortium/CICE/pull/903">#903</a></li></ul><p><strong>Documentation</strong></p><ul><li>Update version <a href="https://github.com/CICE-Consortium/CICE/pull/901">#901</a></li><li>Update documentation <a href="https://github.com/CICE-Consortium/CICE/pull/849">#849</a>, <a href="https://github.com/CICE-Consortium/CICE/pull/879">#879</a>, <a href="https://github.com/CICE-Consortium/CICE/pull/888">#888</a>, <a href="https://github.com/CICE-Consortium/CICE/pull/898">#898</a></li></ul>
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
No full textThe Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way
Patterns of E-Journal Use within the Anatolian University Library Consortium
No full textWith the establishment of Anatolian University Library Consortium (ANKOS), the number of accessible databases and usage of electronic journals has increased rapidly. Due to the diversity of the universities, differences in usage for various subject collections are observed. In this study, a comparison between the research activity in Turkey and electronic journal usage through the Anatolian University Library Consortium has been carried out. The data of the total and subject- based full-text article usage indicate a strong correlation between the number of published articles and their usage. Additionally, a rank analysis was conducted to establish similarities and differences between each institution’s usage with the aggregated consortium usage
Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes
No full textOnline Correction for: https://doi.org/10.1038/s41586-020-2493-4 | Erratum for https://bura.brunel.ac.uk/handle/2438/21299In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article : 'Expanded encyclopaedias of DNA elements in the human and mouse genomes'.https://www.nature.com/articles/s41586-021-04226-3https://www.nature.com/articles/s41586-021-04226-
Needs assessment to strengthen capacity in water and sanitation research in Africa:experiences of the African SNOWS consortium
No full textDespite its contribution to global disease burden, diarrhoeal disease is still a relatively neglected area for research funding, especially in low-income country settings. The SNOWS consortium (Scientists Networked for Outcomes from Water and Sanitation) is funded by the Wellcome Trust under an initiative to build the necessary research skills in Africa. This paper focuses on the research training needs of the consortium as identified during the first three years of the project
Author Correction: Perspectives on ENCODE (Nature, (2020), 583, 7818, (693-698), 10.1038/s41586-020-2449-8)
No full textThe Original Article (https://doi.org/10.1038/s41586-020-2449-8) was published on 29 July 2020.Copyright © The Authors 2022. In this Article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online
Consortium Approach to E-Resource Sharing - A Case Study
No full textIncrease in journals costs, depleting library budgets and drastic cuts in number of journals has lead to 'Journal Crisis'. As a result, library professionals are facing a big challenge to cope with this situation. The urgent need of the hour is that library professionals should come together for active resource sharing, and as a result of which consortium practice has emerged in the library arena. Various consortia models have emerged in India in variety of forms depending upon sources of funding and participants affiliations. The different models identified are Open Consortia; Closed Group Consortia; Institute Headquarters Funding; Centrally Funding; Shared Budgets and National Models
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