Developing and Advancing a Cyberinfrastructure to Gain Insights into Research Investments: An Organizing Research Framework

Developing and Advancing a Cyberinfrastructure to Gain Insights into Research Investments: An Organizing Research Framework Although the National Science Foundation (NSF) funds approximately 24% of basic research conducted in America’s colleges and universities (NSF), there is little we know about how NSF-­‐funding decisions have resulted in the current research landscape. This gap was the impetus for a project –Deep Insights Anytime, Anywhere (DIA2)— that begins to address this problem by focusing on NSF investments in undergraduate STEM education research, and how education innovations make an impact and diffuse throughout the STEM education community. The project team has designed an information portal (http://www.dia2.org) to allow researchers and scientists to browse and search public data from NSF to understand what research has taken place in specific areas and to find collaborators. There are many challenges in developing and using such a cyberinfrastructure, but also many potential advantages for practitioners and researchers. In this paper we will specifically discuss the research opportunities provided by DIA2 and present the research framework guiding the DIA2 project—a description of the three major themes/areas of research for the study. It summarizes the research questions and research activities corresponding to each of the themes, presents next steps, and based on our findings, highlights the value of DIA2 to members of the STEM education community. These concentrated efforts can help us better understand the domain of STEM research. Reference NSF. About the National Science Foundation. Retrieved October 14, 2014, from http://nsf.gov/about

Understanding the information needs of public health practitioners: A literature review to inform design of an interactive digital knowledge management system

AbstractThe need for rapid access to information to support critical decisions in public health cannot be disputed; however, development of such systems requires an understanding of the actual information needs of public health professionals. This paper reports the results of a literature review focused on the information needs of public health professionals. The authors reviewed the public health literature to answer the following questions: (1) What are the information needs of public health professionals? (2) In what ways are those needs being met? (3) What are the barriers to meeting those needs? (4) What is the role of the Internet in meeting information needs? The review was undertaken in order to develop system requirements to inform the design and development of an interactive digital knowledge management system. The goal of the system is to support the collection, management, and retrieval of public health documents, data, learning objects, and tools.Method:The search method extended beyond traditional information resources, such as bibliographic databases, tables of contents (TOC), and bibliographies, to include information resources public health practitioners routinely use or have need to use—for example, grey literature, government reports, Internet-based publications, and meeting abstracts.Results:Although few formal studies of information needs and information-seeking behaviors of public health professionals have been reported, the literature consistently indicated a critical need for comprehensive, coordinated, and accessible information to meet the needs of the public health workforce. Major barriers to information access include time, resource reliability, trustworthiness/credibility of information, and “information overload”.Conclusions:Utilizing a novel search method that included the diversity of information resources public health practitioners use, has produced a richer and more useful picture of the information needs of the public health workforce than other literature reviews. There is a critical need for public health digital knowledge management systems designed to reflect the diversity of public health activities, to enable human communications, and to provide multiple access points to critical information resources. Public health librarians and other information specialists can serve a significant role in helping public health professionals meet their information needs through the development of evidence-based decision support systems, human-mediated expert searching and training in the use information retrieval systems

Suppression of Tumor Necrosis Factor Production by Alcohol in Lipopolysaccharide-Stimulated Culture

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66388/1/j.1530-0277.1994.tb00917.x.pd

In vivo genome editing improves muscle function in a mouse model of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a devastating disease affecting about 1 out of 5000 male births and caused by mutations in the dystrophin gene. Genome editing has the potential to restore expression of a modified dystrophin gene from the native locus to modulate disease progression. In this study, adeno-associated virus was used to deliver the CRISPR/Cas9 system to the mdx mouse model of DMD to remove the mutated exon 23 from the dystrophin gene. This includes local and systemic delivery to adult mice and systemic delivery to neonatal mice. Exon 23 deletion by CRISPR/Cas9 resulted in expression of the modified dystrophin gene, partial recovery of functional dystrophin protein in skeletal myofibers and cardiac muscle, improvement of muscle biochemistry, and significant enhancement of muscle force. This work establishes CRISPR/Cas9-based genome editing as a potential therapy to treat DMD

Effect of temperature and time delay in centrifugation on stability of select biomarkers of nutrition and non-communicable diseases in blood samples

Introduction: Preanalytical conditions are critical for blood sample integrity and poses challenge in surveys involving biochemical measurements. A cross sectional study was conducted to assess the stability of select biomarkers at conditions that mimic field situations in surveys. Material and methods: Blood from 420 volunteers was exposed to 2 – 8 °C, room temperature (RT), 22 – 30 °C and > 30 °C for 30 min, 6 hours, 12 hours and 24 hours prior to centrifugation. After different exposures, whole blood (N = 35) was used to assess stability of haemoglobin, HbA1c and erythrocyte folate; serum (N = 35) for assessing stability of ferritin, C-reactive protein (CRP), vitamins B12, A and D, zinc, soluble transferrin receptor (sTfR), total cholesterol, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), tryglicerides, albumin, total protein and creatinine; and plasma (N = 35) was used for glucose. The mean % deviation of the analytes was compared with the total change limit (TCL), computed from analytical and intra-individual imprecision. Values that were within the TCL were deemed to be stable. Result: Creatinine (mean % deviation 14.6, TCL 5.9), haemoglobin (16.4%, TCL 4.4) and folate (33.6%, TCL 22.6) were unstable after 12 hours at 22- 30°C, a temperature at which other analytes were stable. Creatinine was unstable even at RT for 12 hours (mean % deviation: 10.4). Albumin, CRP, glucose, cholesterol, LDL, triglycerides, vitamins B12 and A, sTfR and HbA1c were stable at all studied conditions. Conclusion: All analytes other than creatinine, folate and haemoglobin can be reliably estimated in blood samples exposed to 22-30°C for 12 hours in community-based studies

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK.

BACKGROUND: A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. METHODS: This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. FINDINGS: Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0-75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4-97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8-80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3-4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. INTERPRETATION: ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, Bill & Melinda Gates Foundation, Lemann Foundation, Rede D'Or, Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca