ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data

Background: Research studies exploring the determinants of disease require sufficient statistical power to detect meaningful effects. Sample size is often increased through centralized pooling of disparately located datasets, though ethical, privacy and data ownership issues can often hamper this process. Methods that facilitate the sharing of research data that are sympathetic with these issues and which allow flexible and detailed statistical analyses are therefore in critical need. We have created a software platform for the Virtual Pooling and Analysis of Research data (ViPAR), which employs free and open source methods to provide researchers with a web-based platform to analyse datasets housed in disparate locations. Methods: Database federation permits controlled access to remotely located datasets from a central location. The Secure Shell protocol allows data to be securely exchanged between devices over an insecure network. ViPAR combines these free technologies into a solution that facilitates 'virtual pooling' where data can be temporarily pooled into computer memory and made available for analysis without the need for permanent central storage. Results: Within the ViPAR infrastructure, remote sites manage their own harmonized research dataset in a database hosted at their site, while a central server hosts the data federation component and a secure analysis portal. When an analysis is initiated, requested data are retrieved from each remote site and virtually pooled at the central site. The data are then analysed by statistical software and, on completion, results of the analysis are returned to the user and the virtually pooled data are removed from memory. Conclusions: ViPAR is a secure, flexible and powerful analysis platform built on open source technology that is currently in use by large international consortia, and is made publicly available at [http://bioinformatics.childhealthresearch.org.au/software/vipar/]

Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to 300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m 2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

202109 bchyVersion of RecordPublishe

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

201901 bcrcVersion of RecordPublishe

Effect of cover management factor in quantification of soil loss: case study of Sungai Akah subwatershed, Baram River basin Sarawak, Malaysia

© 2017 Informa UK Limited, trading as Taylor & Francis Group The present study evaluates the effectiveness and suitability of cover management factors (C factor) generated through different techniques like land use/land cover-based arbitrary value (C LULC ), Normalised Different Vegetation Index-based methods C NDVI1 and C NDVI2 and Modified Soil Adjusted Vegetation Index 2-based method (C MSAVI2 ). The C factors generated using these four methods were tested in the calculation and assessment of annual average soil loss from an upland forested subwatershed in the Baram river basin using the Revised Universal Soil Loss Equation (RUSLE). The four cover management factor maps generated by this analysis show some variation among the results. The LULC method uses a single arbitrary value for each LULC type mapped in the subwatershed. The other three methods show a range of C values within each mapped LULC type. The effects of these variations were tested in the RUSLE by keeping the factors such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS) constant. The maximum annual average soil loss is 1191 t. ha -1 . y -1 based on the C LULC . Soil losses estimated with other three methods are very different compared to those estimated with the C LULC method. The highest calculated soil loss values were 1832, 1674 and 1608 t. ha -1 . y -1 in the study area based, respectively, on C NDVI1 , C NDVI2 and C MSAVI2 C factors. These maximum values represent the worst pixel scenario values of soil loss in the region. The statistical analysis performed indicates different relationship between the parameters and suggests the acceptance of the methodology based on C NDVI2 for the study area, instead of a single value method such as C LULC . Among the other two methods, the C MSAVI2 was found to be more consistent than the C NDVI1 method, but both methods lead to over-prediction of annual soil loss rate and therefore need to be reconsidered before applied in the RUSLE