Semantic-Preserving Transformations for Stream Program Orchestration on Multicore Architectures

Because the demand for high performance with big data processing and distributed computing is increasing, the stream programming paradigm has been revisited for its abundance of parallelism in virtue of independent actors that communicate via data channels. The synchronous data-flow (SDF) programming model is frequently adopted with stream programming languages for its convenience to express stream programs as a set of nodes connected by data channels. Static data-rates of SDF programming model enable program transformations that greatly improve the performance of SDF programs on multicore architectures. The major application domain is for SDF programs are digital signal processing, audio, video, graphics kernels, networking, and security. This thesis makes the following three contributions that improve the performance of SDF programs: First, a new intermediate representation (IR) called LaminarIR is introduced. LaminarIR replaces FIFO queues with direct memory accesses to reduce the data communication overhead and explicates data dependencies between producer and consumer nodes. We provide transformations and their formal semantics to convert conventional, FIFO-queue based program representations to LaminarIR. Second, a compiler framework to perform sound and semantics-preserving program transformations from FIFO semantics to LaminarIR. We employ static program analysis to resolve token positions in FIFO queues and replace them by direct memory accesses. Third, a communication-cost-aware program orchestration method to establish a foundation of LaminarIR parallelization on multicore architectures. The LaminarIR framework, which consists of the aforementioned contributions together with the benchmarks that we used with the experimental evaluation, has been open-sourced to advocate further research on improving the performance of stream programming languages

Change of Computed Tomography-Based Body Composition after Adrenalectomy in Patients with Pheochromocytoma

Despite the potential biological importance of the sympathetic nervous system on fat and skeletal muscle metabolism in animal and in vitro studies, its relevance in humans remains undetermined. To clarify the influence of catecholamine excess on human body composition, we performed a retrospective longitudinal cohort study including 313 consecutive patients with histologically confirmed pheochromocytoma who underwent repeat abdominal computed tomography (CT) scans before and after adrenalectomy. Changes in CT-determined visceral fat area (VFA), subcutaneous fat area (SFA), skeletal muscle area (SMA), and skeletal muscle index (SMI) were measured at the level of the third lumbar vertebra. The mean age of all patients was 50.6 ± 13.6 years, and 171/313 (54.6%) were women. The median follow-up duration for repeat CTs was 25.0 months. VFA and SFA were 14.5% and 15.8% higher, respectively (both p p < 0.001); however, the prevalence of sarcopenia was unchanged. This study provides important clinical evidence that sympathetic hyperactivity can contribute to lipolysis in visceral and subcutaneous adipose tissues, but its impact on human skeletal muscle is unclear

Work-Related Risk Factors by Severity for Acute Pesticide Poisoning Among Male Farmers in South Korea

The objective of this study was to explore work-related risk factors of acute occupational pesticide poisoning among male farmers according to the severity of the poisoning. A nationwide sampling survey of male farmers was conducted in South Korea in 2011. A total of 1,958 male farmers were interviewed. Severity of occupational pesticide poisoning in 2010 was evaluated according to symptoms, types of treatment, and number of pesticide poisoning incidents per individual. A multinomial logistic regression model was used to estimate the odds ratio with 95% confidence intervals for risk factors of acute occupational pesticide poisoning. We found that the risk of acute occupational pesticide poisoning increased with lifetime days of pesticide application (OR = 1.74; 95% CI = 1.32–2.29), working a farm of three or more acres in size (OR = 1.49), not wearing personal protective equipment such as gloves (OR = 1.29) or masks (OR = 1.39). Those who engaged in inappropriate work behaviors such as not following pesticide label instructions (OR = 1.61), applying the pesticide in full sun (OR = 1.48), and applying the pesticide upwind (OR = 1.54) had a significantly increased risk of pesticide poisoning. There was no significant risk difference by type of farming. In addition, the magnitude of these risk factors did not differ significantly by severity of acute pesticide poisoning. In fact, our findings suggest that work-related risk factors contributed to the development of acute occupational pesticide poisoning without relation to its severity. Therefore, prevention strategies for reducing occupational pesticide poisoning, regardless of severity, should be recommended to all types of farming and the level of poisoning severity

Work-Related Risk Factors by Severity for Acute Pesticide Poisoning Among Male Farmers in South Korea

The objective of this study was to explore work-related risk factors of acute occupational pesticide poisoning among male farmers according to the severity of the poisoning. A nationwide sampling survey of male farmers was conducted in South Korea in 2011. A total of 1,958 male farmers were interviewed. Severity of occupational pesticide poisoning in 2010 was evaluated according to symptoms, types of treatment, and number of pesticide poisoning incidents per individual. A multinomial logistic regression model was used to estimate the odds ratio with 95% confidence intervals for risk factors of acute occupational pesticide poisoning. We found that the risk of acute occupational pesticide poisoning increased with lifetime days of pesticide application (OR = 1.74; 95% CI = 1.32–2.29), working a farm of three or more acres in size (OR = 1.49), not wearing personal protective equipment such as gloves (OR = 1.29) or masks (OR = 1.39). Those who engaged in inappropriate work behaviors such as not following pesticide label instructions (OR = 1.61), applying the pesticide in full sun (OR = 1.48), and applying the pesticide upwind (OR = 1.54) had a significantly increased risk of pesticide poisoning. There was no significant risk difference by type of farming. In addition, the magnitude of these risk factors did not differ significantly by severity of acute pesticide poisoning. In fact, our findings suggest that work-related risk factors contributed to the development of acute occupational pesticide poisoning without relation to its severity. Therefore, prevention strategies for reducing occupational pesticide poisoning, regardless of severity, should be recommended to all types of farming and the level of poisoning severity

Development of an algorithm to automatically compress a CT image to visually lossless threshold

Abstract Background To develop an algorithm to predict the visually lossless thresholds (VLTs) of CT images solely using the original images by exploiting the image features and DICOM header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity metrics. Methods Five radiologists independently determined the VLT for 206 body CT images for JPEG2000 compression using QUEST procedure. The images were divided into training (n = 103) and testing (n = 103) sets. Using the training set, a multiple linear regression (MLR) model was constructed regarding the image features and DICOM header information as independent variables and regarding the VLTs determined with median value of the radiologists’ responses (VLT rad ) as dependent variable, after determining an optimal subset of independent variables by backward stepwise selection in a cross-validation scheme. The performance was evaluated on the testing set by measuring absolute differences and intra-class correlation (ICC) coefficient between the VLT rad and the VLTs predicted by the model (VLT model ). The performance of the model was also compared two metrics, peak signal-to-noise ratio (PSNR) and high-dynamic range visual difference predictor (HDRVDP). The time for computing VLTs between MLR model, PSNR, and HDRVDP were compared using the repeated ANOVA with a post-hoc analysis. P < 0.05 was considered to indicate a statistically significant difference. Results The means of absolute differences with the VLT rad were 0.58 (95% CI, 0.48, 0.67), 0.73 (0.61, 0.85), and 0.68 (0.58, 0.79), for the MLR model, PSNR, and HDRVDP, respectively, showing significant difference between them (p < 0.01). The ICC coefficients of MLR model, PSNR, and HDRVDP were 0.88 (95% CI, 0.81, 0.95), 0.85 (0.79, 0.91), and 0.84 (0.77, 0.91). The computing times for calculating VLT per image were 1.5 ± 0.1 s, 3.9 ± 0.3 s, and 68.2 ± 1.4 s, for MLR metric, PSNR, and HDRVDP, respectively. Conclusions The proposed MLR model directly predicting the VLT of a given CT image showed competitive performance to those of image fidelity metrics with less computational expenses. The model would be promising to be used for adaptive compression of CT images

The Impact of Myosteatosis Percentage on Short-Term Mortality in Patients with Septic Shock

The impact of myosteatosis on septic patients has not been fully revealed. The aim of the study was to evaluate the impact of the myosteatosis area and percentage on the 28-day mortality in patients with septic shock. We conducted a single center, retrospective study from a prospectively collected registry of adult patients with septic shock who presented to the emergency department and performed abdominal computed tomography (CT) from May 2016 to May 2020. The myosteatosis area defined as the sum of low attenuation muscle area and intramuscular adipose tissue at the level of the third lumbar vertebra was measured by CT. Myosteatosis percentages were calculated by dividing the myosteatosis area by the total abdominal muscle area. Of the 896 patients, 28-day mortality was 16.3%, and the abnormal myosteatosis area was commonly detected (81.7%). Among variables of body compositions, non-survivors had relatively lower normal attenuation muscle area, higher low attenuation muscle area, and higher myosteatosis area and percentage than that of survivors. Trends of myosteatosis according to age group were different between the male and female groups. In subgroup analysis with male patients, the multivariate model showed that the myosteatosis percentage (adjusted OR 1.02 [95% CI 1.01–1.03]) was an independent risk factor for 28-day mortality. However, this association was not evident in the female group. Myosteatosis was common and high myosteatosis percentage was associated with short-term mortality in patients with septic shock. Our results implied that abnormal fatty disposition in muscle could impact on increased mortality, and this effect was more prominent in male patients

Artificial Intelligence Mortality Prediction Model for Gastric Cancer Surgery Based on Body Morphometry, Nutritional, and Surgical Information: Feasibility Study

The objective of this study is to develop a mortality prediction model for patients undergoing gastric cancer surgery based on body morphometry, nutritional, and surgical information. Using a prospectively built gastric surgery registry from the Asan Medical Center (AMC), 621 gastric cancer patients, who were treated with surgery with no recurrence of cancer, were selected for the development of the prediction model. Input features (i.e., body morphometry, nutritional, surgical, and clinicopathologic information) were selected in the collected data based on the XGBoost analysis results and experts’ opinions. A convolutional neural network (CNN) framework was developed to predict the mortality of patients undergoing gastric cancer surgery. Internal validation was performed in split datasets of the AMC, whereas external validation was performed in patients in the Ajou University Hospital. Fifteen features were selected for the prediction of survival probability based on the XGBoost analysis results and experts’ suggestions. Accuracy, F1 score, and area under the curve of our CNN model were 0.900, 0.909, and 0.900 in the internal validation set and 0.879, 0.882, and 0.881 in the external validation set, respectively. Our developed CNN model was published on a website where anyone could predict mortality using individual patients’ data. Our CNN model provides substantially good performance in predicting mortality in patients undergoing surgery for gastric cancer, mainly based on body morphometry, nutritional, and surgical information. Using the web application, clinicians and gastric cancer patients will be able to efficiently manage mortality risk factors