A Heuristic Approach to Creating Technological Fair Use Guidelines in Higher Education

Higher education has experienced challenges defining and implementing copyright compliance. Confusion among faculty and staff appears to be common regarding copyright and fair use. The original copyright doctrine was drafted over 200 years ago, which predates practically all technological advances that have and will continue to occur. Change is slow and onerous with most legislation; there is not much possibility the small amendments made to the law will be able to keep pace with the continual technological evolution. Further, judges are citing precedents in court rulings of copyright disputes that were made using the best interpretation of the law, even though those earlier adjudicators had nothing concrete upon which to base decisions. The cycle of loose interpretations further exacerbates the copyright and fair use problem involving technology. Moreover, this concern has been magnified due to the digital nature of lesson delivery most learning institutions are adopting today. The rapid, widespread move toward online learning methods creates an entire set of copyright and fair use circumstances that extend beyond the traditional, face-to-face pedagogical issues. Invariably, schools will be left to attempt to decide what will be considered legal and safe, often by trial and error, until clearer, universally accepted guidelines can be created. A group consensus for best practice was achieved over three rounds of surveying with the help of a Delphi panel highly experienced in copyright laws. Opinions converged early during the process, where proper fair use assessment was one of the major themes appearing during the first round. Respondents also agreed future educators will undoubtedly continue to struggle with fully understanding the intricacies of fair use. An overall consensus reached for many questions was sufficient for answering the proposed research questions and drafting a list of recommendations for technological fair use. The outcome should add to the existing knowledge base, given the limited number of studies that have been conducted regarding the complexities of copyright topics in distance and online education. Recommendations for further investigations encourages researchers to continue where this effort ends to remain current and compliant with the ubiquitous changes in technologies

Behavioral and Emotional Dyscontrol Following Traumatic Brain Injury: A Systematic Review of Neuroimaging and Electrophysiological Correlates.

BACKGROUND: Behavioral and emotional dyscontrol commonly occur following traumatic brain injury (TBI). Neuroimaging and electrophysiological correlates of dyscontrol have not been systematically summarized in the literature to date. OBJECTIVE: To complete a systematic review of the literature examining neuroimaging and electrophysiological findings related to behavioral and emotional dyscontrol due to TBI. METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant literature search was conducted in PubMed (MEDLINE), PsycINFO, EMBASE, and Scopus databases prior to May 2019. The database query yielded 4392 unique articles. These articles were narrowed based on specific inclusion criteria (e.g., clear TBI definition, statistical analysis of the relationship between neuroimaging and dyscontrol). RESULTS: A final cohort of 24 articles resulted, comprising findings from 1552 patients with TBI. Studies included civilian (n = 12), military (n = 10), and sport (n = 2) samples with significant variation in the severity of TBI incorporated. Global and region-based structural imaging was more frequently used to study dyscontrol than functional imaging or diffusion tensor imaging. The prefrontal cortex was the most common neuroanatomical region associated with behavioral and emotional dyscontrol, followed by other frontal and temporal lobe findings. CONCLUSIONS: Frontal and temporal lesions are most strongly implicated in the development of postinjury dyscontrol symptoms although they are also the most frequently investigated regions of the brain for these symptom categories. Future studies can make valuable contributions to the field by (1) emphasizing consistent definitions of behavioral and emotional dyscontrol, (2) assessing premorbid dyscontrol symptoms in subjects, (3) utilizing functional or structural connectivity-based imaging techniques, or (4) restricting analyses to more focused brain regions

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)