Leveraging Tiled Display for Big Data Visualization Using D3.js

Data visualization has proven effective at detecting patterns and drawing inferences from raw data by transforming it into visual representations. As data grows large, visualizing it faces two major challenges: 1) limited resolution i.e. a screen is limited to a few million pixels but the data can have a billion data points, and 2) computational load i.e. processing of this data becomes computationally challenging for a single node system. This work addresses both of these issues for efficient big data visualization. In the developed system, a High Pixel Density and Large Format display was used enabling the display of fine details on the screen when visualizing data. Apache Spark and Hadoop used in the system allow the computation to be done on a cluster. The system is demonstrated using a global wind flow simulation. The Global Surface Summary of the Day dataset is processed and visualized using web browsers with Data-Driven Documents (D3).js code. We conducted both a performance evaluation and a user study to measure the performance and effectiveness of the system. It was seen that the system was most efficient when visualizing data using streamed bitmap images rather than streamed raw data. The system only rendered images at 6-10 Frames Per Second (FPS) and did not meet our target of rendering images at 30 FPS. The results of the user study concluded that the system is effective and easy to use for data visualization. The outcome of our experiment suggests that the current state of Google Chrome may not be as powerful as required to perform heavy 2D data visualization on the web and still needs more development for visualizing data of large magnitude

Economic analysis of mushroom enterprise in Chitwan district, Nepal

The majority of the population (66%) in-country “Nepal” are engaged in agriculture. However, domestic production finds it difficult to meet the annual demand of the people. Hence, people are moving from subsistence agriculture to embrace mushroom farming. This study focuses on economic analysis and analysis of the present status of mushroom farming and enterprise in this country. The study was conducted in the land area of Kalika Municipality and Bharatpur Metropolitan City. 30 mushroom farmers with two huts and at least three years of experience were selected from the study area. The primary data were collected through face-to-face interviews with the farmers, focus group discussion (FGD) and key informant interviews (KII). The secondary data was collected through various published articles and documents. The data analysis was done using basic statistics and a regression function. The benefit-cost ratio is 2.54 and a high gross margin is NRs.490,876.65 per kattha per year. The return to scale (RTS) is 0.80. Five marketing channels are present among which wholesalers and local collectors contributed the highest percentage of the share. However, the dominance of the intermediaries, timely unavailability of inputs, price fluctuation, disease and pest infestation were the major constraints. Disease and pest control, formation of the producer organization, improvised cultivation practices, timely and affordable availability of quality can be the major solution measures. Whereas, suitable climatic conditions, high productivity and growing market demand are the strengths of mushroom production in this study area. Mushroom farming is found to be a profitable business concerning competitive and comparative markets.

Predictors of infection in viral-hepatitis related acute liver failure

<p><b>Objective:</b> Infections are common and associated with complications and mortality in acute liver failure (ALF). The temporal relationship between ammonia and infection in ALF patients is unclear. We aimed to evaluate the predictors of infection and its relationship with arterial ammonia levels.</p> <p><b>Materials and methods:</b> Consecutive ALF patients hospitalized between January 2004 and December 2015, without signs of infection at/within 48 h of admission, were included. Occurrence of infection after 48 h was documented and ammonia levels were estimated for five consecutive days. Multivariate logistic regression analysis was used to assess factors associated with development of infection. Generalized estimating equations (GEE) were used to evaluate five-day time trend of ammonia in patients with and without infection.</p> <p><b>Results:</b> Of 540 consecutive patients, 120 were infected at admission/within 48 h and were excluded. Of the rest 420 patients, 144 (34.3%) developed infection after 48 h and 276 (65.7%) remained non-infected. Infected patients had higher mortality than non-infected patients (61.8% vs 40.0%, <i>p</i> < .001). On multivariate analysis, presence of cerebral edema(HR 2.049; 95%CI, 1.30–3.23), ammonia level on day 3 of admission (HR 1.006; 95%CI, 1.003–1.008), and model for end stage liver disease (MELD) score (HR 1.051; 95%CI, 1.026–1.078) were associated with development of infection. GEE showed group difference in serial ammonia values between infected and non-infected patients indicating lack of ammonia decline in infected patients.</p> <p><b>Conclusions:</b> Cerebral edema, elevated ammonia on day 3, and higher MELD score predict the development of infection in ALF. Ammonia persists at high levels in infected patients, and elevated ammonia on day 3 is associated with complications and death.</p

Comparison of Dynamic Changes Among Various Prognostic Scores in Viral Hepatitis-Related Acute Liver Failure

Introduction and aim. Multiple prognostic scores are available for acute liver failure (ALF). Our objective was to compare the dynamicity of model for end stage liver disease (MELD), MELD-sodium, acute liver failure early dynamic model (ALFED), chronic liver failure (CLIF)-consortium ACLF score and King’s College Hospital Criteria (KCH) for predicting outcome in ALF.Materials and methods. All consecutive patients with ALF at a tertiary care centre in India were included. MELD, MELD-Na, ALFED, CLIF-C ACLF scores and KCH criteria were calculated at admission and day 3 of admission. Area under receiver operator characteristic curves (AUROC) were compared with DeLong method. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratio (LR) and diagnostic accuracy (DA) were reported.Results. Of the 115 patients included in the study, 73 (63.5%) died. The discrimination of mortality with baseline values of prognostic scores (MELD, MELD-Na, ALFED, CLIF-C ACLF and KCH) was modest (AUROC: 0.65-0.77). The AUROC increased on day 3 for all scores, except KCH criteria. On day 3 of admission, ALFED score had the highest AUROC 0.95, followed by CLIF-C ACLF 0.88, MELD 0.81, MELD-Na 0.77 and KCH 0.52. The AUROC for ALFED was significantly higher than MELD, MELD-Na and KCH (P 4 on day 3 had the best sensitivity (87.1%), specificity (89.5%), PPV (93.8%), NPV (79.1%), LR positive (8.3) and DA (87.9%) for predicting mortality.Conclusions. Dynamic assessment of prognostic scores better predicts outcome. ALFED model performs better than MELD, MELD, MELD-Na, CLIF-C ACLF scores and KCH criteria for predicting outcome in viral hepatitis-related ALF

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset