The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

A Framework for the Design of a Mobile-Based Alert System for Outpatient Adherence in Nigeria

Nigeria ranks among the countries with the highest child and maternal mortality rate. Chronic diseases are the most common contributors to the diseases burden in Nigeria most especially Malaria, Tuberculosis (TB) and HIV/AIDS. Adherence to long-term therapy in out-patient condition is required to reduce and curb the prevalence of these diseases. Poor adherence to long-term therapies severely compromises the effectiveness of treatment; making this a critical issue in population health both from the perspective of quality of life and of health economics. This work introduces a m-technology based system that will provide an easy way of complying with drug regimen. It will make use of the Short Messaging Service (SMS) of mobile phones to provide reminders at dosing times. It will design architecture for mobile health interventions and develop a prototype SMS-based system to improve out-patient adherence. This system will be deployed over a period of time at selected hospitals and chronic disease management centers in selected states in Nigeria, and the adherence rates measured via health outcomes and evaluated. This would provide a significant positive return on investment through primary prevention (of risk factors) and secondary prevention of adverse health outcomes. It will also inform predictions of future population health outcomes predicted by treatment efficacy data. Keywords: out-patient, m-technology, adherence, chronic diseases, Nigeria, SM

A Randomized, Controlled Clinical Study to Assess the Effect of Anodal and Cathodal Electrical Stimulation on Periwound Skin Blood Flow and Pressure Ulcer Size Reduction in Persons with Neurological Injuries

The use of electrical stimulation (ES) should be considered for treating nonhealing pressure ulcers (PUs), but optimal ES wound treatment protocols have yet to be established. A randomized, controlled, double-blind clinical study was conducted to evaluate the effects of cathodal and anodal high-voltage monophasic pulsed current (HVMPC) on periwound skin blood flow (PSBF) and size reduction of Stage 2 to Stage 4 PUs of at least 4 weeks’ duration.Persons \u3e18 years of age, hospitalized with neurological injuries, at high risk for PU development (Norton scale \u3c14 \u3epoints; Waterlow scale \u3e15 points), and with at least 1 Stage 2 to Stage 4 PU were eligible to participate in the study. Persons with necrotic wounds, osteomyelitis, electronic or metal implants in the PU area, PUs in need of surgical intervention, acute wound inflammation, diabetes (HBA1c \u3e7%), diabetic neuropathy, cancer, and/or allergies to standard wound treatments were excluded. Patients were randomly assigned to 1 of 3 groups: anodal (AG), cathodal (CG), or placebo (PG) ES. All groups received individualized PU prevention and standard wound care. In the PG, sham ES was applied; the AG and CG were treated with anodal and cathodal HVMPC, respectively (154 μs 100 Hz; 360 µC/second; 1.08 C/day), 50 minutes per day, 5 days per week, for a maximum of 8 weeks. PSBF was measured using laser Doppler flowmetry at baseline, week 2, and week 4, and wound surface area measurements were obtained and analyzed using a digitizer connected to a personal computer. Data analysis utilized the maximum-likelihood chi-squared test, the analysis of variance Kruskal-Wallis test, the Kruskal-Wallis post-hoc test, and Spearman’s rank order correlation. Nonlinear approximation based on exponential function was used to calculate treatment time needed to reduce the wound area by 50%. In all tests, the level of significance was set at P ≤.05. Of the 61 participating patients, 20 were in the AG (mean age 53.2 ± 13.82 years), 21 in the CG (mean age 55.67 ± 17.83 years), and 20 in the PG (mean age 52.5 ± 13.18 years). PUs (baseline size range 1.01 cm2 to 59.57 cm2; duration 4 to 48 weeks) were most frequently located in the sacral region (73.77%) and classified as Stage 3 (62.29%). PSBF at week 2 was significantly higher in the AG and CG than in the PG (P P = .0391 and P = .0024, respectively). In both ES groups, PSBF at week 4 and percent wound surface area reductions between weeks 4 and 8 were positively correlated, but only the AG correlation was statistically significant (P = .049). In this study, both ES modalities improved blood flow and wound area reduction rate. Studies examining optimal ES treatment times for healing to occur, the effect of comorbidities and baseline wound variables on ES outcomes, and the nature of the relationship between blood flow and healing are necessary

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

The Workforce Needs of New Jersey's Pharmaceutical and Medical Technology Industry

This report is based on an online survey conducted in spring 2006 of pharmaceutical and medical technology companies in New Jersey. It identifies the current and future workforce needs of the pharmaceutical and medical technology industry in New Jersey

Research Advances: January 2014

The VA has a comprehensive research agenda to help the newest generation of Veterans -- those returning from operations Enduring Freedom, Iraqi Freedom, and New Dawn. In addition to exploring new treatments for traumatic brain injury and other complex blast-related injuries, VA researchers are examining ways to improve the delivery of health care services for these Veterans and promote their reintegration back into their families, communities, and workplaces.This publication reviews recent advances in research about Veterans' health and well-being