Modeling Big Medical Survival Data Using Decision Tree Analysis with Apache Spark

In many medical studies, an outcome of interest is not only whether an event occurred, but when an event occurred; and an example of this is Alzheimer’s disease (AD). Identifying patients with Mild Cognitive Impairment (MCI) who are likely to develop Alzheimer’s disease (AD) is highly important for AD treatment. Previous studies suggest that not all MCI patients will convert to AD. Massive amounts of data from longitudinal and extensive studies on thousands of Alzheimer’s patients have been generated. Building a computational model that can predict conversion form MCI to AD can be highly beneficial for early intervention and treatment planning for AD. This work presents a big data model that contains machine-learning techniques to determine the level of AD in a participant and predict the time of conversion to AD. The proposed framework considers one of the widely used screening assessment for detecting cognitive impairment called Montreal Cognitive Assessment (MoCA). MoCA data set was collected from different centers and integrated into our large data framework storage using a Hadoop Data File System (HDFS); the data was then analyzed using an Apache Spark framework. The accuracy of the proposed framework was compared with a semi-parametric Cox survival analysis model

A new estimate of carbon for Bangladesh forest ecosystems with their spatial distribution and REDD+ implications

In tropical developing countries, reducing emissions from deforestation and forest degradation (REDD+) is becoming an important mechanism for conserving forests and protecting biodiversity. A key prerequisite for any successful REDD+ project, however, is obtaining baseline estimates of carbon in forest ecosystems. Using available published data, we provide here a new and more reliable estimate of carbon in Bangladesh forest ecosystems, along with their geo-spatial distribution. Our study reveals great variability in carbon density in different forests and higher carbon stock in the mangrove ecosystems, followed by in hill forests and in inland Sal (Shorea robusta) forests in the country. Due to its coverage, degraded nature, and diverse stakeholder engagement, the hill forests of Bangladesh can be used to obtain maximum REDD+ benefits. Further research on carbon and biodiversity in under-represented forest ecosystems using a commonly accepted protocol is essential for the establishment of successful REDD+ projects and for the protection of the country’s degraded forests and for addressing declining levels of biodiversity

Study of Spot Urine Protein Creatinine Ratio as an Index of Quantitative Proteinuria

BACK GROUND : Quantitating protein in the urine has become more than just a diagnostic test. It can be even used for prognostic purposes and to assess the effect of therapy. Commonly used method to measure protein is 24 hours urine collection, which is time consuming, cumbersome and often inaccurate. AIM : Aim of the study was to compare spot urine protein- creatinine ratio with 24 hours urine protein as an index of quantitative proteinuria. METHOD : 68 patients with proteinuria with varying degrees of renal dysfunction were included in this study. 24 hour urine protein estimation was done and protein creatinine ratio was calculated from a spot urine sample and compared. Study group was divided into 4 groups according to creatinine clearance and degree of proteinuria. Correlation between 24 hours urine protein and spot urine protein creatinine ratio, among the groups was statistically analyzed. RESULTS : There was significant correlation between 24 hours urine protein and spot urine protein-Creatinine ratio (r = 0.975) ( P < 0.05). However least correlation was in patients with end stage renal disease having nephrotic range proteinuria ( r = 0.868) (P < 0.05). CONCLUSION : Spot urine protein creatinine ratio is a useful index for quantification of proteinuria, which is easy to perform, inexpensive and less time consuming method

Malaysian automobile industry and green supply chain management

The automotive industry is one of the main producers of industrial wastes affecting the natural environment. The purpose of this study is to identify the most important barriers to the Malaysian automotive industry. Researching 145 companies in Malaysia’s automotive supply chain industry collected the data. The data were examined using Problem Conflict Index (PCI) to determine the most important critical barriers that put automotive companies in a difficult position to implement green supply chain management (GSCM). The results of this study report that the number one barrier in the automotive sector is "market competition and uncertainty" with a PCI of 298. The second problem is “Lack of Implementing Green Practice” with the PCI of 297. Like these two barriers, cost implications, unawareness of customers, lack of corporate social responsibility, lack of globalization, lack of technical assistance from the government have been identified as top-level barriers and lack of the government’s willingness to invest, reduced involvement in environmentally related conferences. The elimination of these barriers will help to apply the GSCM in the Malaysia automobile industry

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy