Scientific High Performance Computing (HPC) Applications On The Azure Cloud Platform

Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations

Primary arthroplasty as an option for surgical treatment of unstable intertrochanteric fracture femur in elderly patients: a retrospective study

Background: An unstable intertrochanteric fracture in osteoporotic elderly patients presents a very challenging problem to the surgeons. This category of patients if kept in bed for long tends to have many complications like deep vein thrombosis, pneumonia, bed sores, etc. Surgeon’s worldwide face difficulty in getting a good anatomical reduction and do not allow early mobility to these patients because of risk of loss of reduction and implant cut out. The purpose of our study is to study the usefulness of arthroplasty in these patients in terms of better functional outcomes and no increase in complication rates.Methods: It was a retrospective study involving 25 patients (17 females, 8 males) with mean age of 76±2.3 years having intertrochanteric femur fractures (AO type A 2.2 and above) with osteoporosis operated upon with bipolar hemiarthroplasty at IQ City Medical College and NM Hospital between January 2015 and January 2017.Results: All the patients were followed up for a period of 1 year. We had a mean operative time of 75 min (range 55-125 min) with an average blood loss of 450 ml. 4 of our patients required postoperative blood transfusion. All patients walked on 2nd postoperative day. We had a mean Harris hip score of 82 and mean VAS of 1 at the end of 1 year.Conclusions: Hemiarthroplasty done in cases of unstable intertrochanteric femur fractures in elderly patients with osteoporotic bones allows early weight bearing thus improves the final functional outcomes. Further randomized trials are required before deriving any conclusions

PRE-ANALYTICAL VARIABLES IN CLINICAL CHEMISTRY: TRAINING MEDICAL UNDERGRADUATES THROUGH CASE BASED DISCUSSION

Background: Pre-analytical variables in clinical chemistry are factors prior to the biochemical analysis of samples affecting laboratory test results accounting for 32-75% of errors leading to misdiagnosis, decreased quality of medical care services and wastage of monetary resources. Aim: To educate first year medical undergraduates about pre-analytical variables through case based discussion and lecture method of teaching and assess the gain in knowledge by these methods. Methods and material: Two batches of medical students namely A (N=50) and B (N=52) were assessed for their background knowledge on the topic using an MCQ based questionnaire (pre-test). Batch A and B were taught through didactic lecture and case based discussion respectively. Post-test questionnaire was conducted to test the gain in knowledge of both batches. Delayed post-test was conducted after 2 weeks to assess retention of knowledge amongst students. Results: Pre-test scores of Batch A and B were not significantly different indicating that both batches had similar background knowledge of topic. Post-test scores vs. pre-test scores were significantly higher in both batches implying that both batches benefitted from their respective teaching sessions. But post-test score of Batch B was significantly higher than that of Batch A indicating higher gain of knowledge through case based discussion. Delayed post-test score was also significantly higher in Batch B vs. A implying better retention of knowledge through case based discussions. Conclusion: Topic ˜Pre-analytical variables in clinical chemistry must be included in undergraduate medical curriculum. Case based discussion could be an effective module for teaching the same. Key words: Case based discussion; Didactic lecture; Medical students; Medical education; Pre-analytical variables

PRE-ANALYTICAL VARIABLES IN CLINICAL CHEMISTRY: TRAINING MEDICAL UNDERGRADUATES THROUGH CASE BASED DISCUSSION

Background: Pre-analytical variables in clinical chemistry are factors prior to the biochemical analysis of samples affecting laboratory test results accounting for 32-75% of errors leading to misdiagnosis, decreased quality of medical care services and wastage of monetary resources. Aim: To educate first year medical undergraduates about pre-analytical variables through case based discussion and lecture method of teaching and assess the gain in knowledge by these methods. Methods and material: Two batches of medical students namely A (N=50) and B (N=52) were assessed for their background knowledge on the topic using an MCQ based questionnaire (pre-test). Batch A and B were taught through didactic lecture and case based discussion respectively. Post-test questionnaire was conducted to test the gain in knowledge of both batches. Delayed post-test was conducted after 2 weeks to assess retention of knowledge amongst students. Results: Pre-test scores of Batch A and B were not significantly different indicating that both batches had similar background knowledge of topic. Post-test scores vs. pre-test scores were significantly higher in both batches implying that both batches benefitted from their respective teaching sessions. But post-test score of Batch B was significantly higher than that of Batch A indicating higher gain of knowledge through case based discussion. Delayed post-test score was also significantly higher in Batch B vs. A implying better retention of knowledge through case based discussions. Conclusion: Topic ˜Pre-analytical variables in clinical chemistry must be included in undergraduate medical curriculum. Case based discussion could be an effective module for teaching the same. Key words: Case based discussion; Didactic lecture; Medical students; Medical education; Pre-analytical variables

Dyke-Davidoff-Masson syndrome as sequelae of typhoid encephalitis?

Dyke-Davidoff-Masson syndrome (DDMS) is characterized by cerebral hemiatrophy with homolateral hypertrophy ofthe skull and sinuses in association with contralateral hemiplegia, seizures, mental retardation, difficulty, and impairmentof speech development. Among the various complications of typhoid fever, neuropsychiatric manifestations constitutea major portion. However, DDMS post typhoid encephalitis has not been reported in the literature. We report a case ofDDMS in an 8-year-old boy who presented with multiple seizures, impaired speech, behavioral changes, and mentalretardation following typhoid encephalitis

Hydrological Impacts Of Climate Change – Challenges, Uncertainty And Limitations

How climate change impacts water resources in the future is an important question that all hydrologists want to have an answer. Climate prediction scenarios are available from many Global Circulation Models for the 21st century. These prediction datasets are typically used as input to a hydrological model for simulating impacts on hydrology, particularly river runoff, evaporation, and storage changes. Because hydrological models are usually run on a much smaller resolutions than climate models, the climate prediction datasets are usually downscaled to represent local climate for using in a hydrological model. The uncertainty in the GCMs, downscaling and hydrological models makes the process complicated and heavily restricts our ability to make predictions of hydrological impacts. This becomes more challenging in a mountainous catchment where the availability of hydro-climatic data are limited. We illustrated some of these issues and their impacts on hydrological simulations using two catchments from the Himalayan region: the Koshi River (~58,000 km2), Nepal, and the source region of the Yellow River (~120,000 km2), China. Climate predictions used are from a number of GCMs participated in the Coupled Model Intercomparison Project (CMIP3). In both examples we used process-based distributed hydrological models: the Soil and Water Assessement Tool (SWAT) for the Koshi and WaSiM for the Yellow River

DeepTMH: Multimodal Semi-supervised framework leveraging Affective and Cognitive engagement for Telemental Health

To aid existing telemental health services, we propose DeepTMH, a novel framework that models telemental health session videos by extracting latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature. Our approach leverages advances in semi-supervised learning to tackle the data scarcity in the telemental health session video domain and consists of a multimodal semi-supervised GAN to detect important mental health indicators during telemental health sessions. We demonstrate the usefulness of our framework and contrast against existing works in two tasks: Engagement regression and Valence-Arousal regression, both of which are important to psychologists during a telemental health session. Our framework reports 40% improvement in RMSE over SOTA method in Engagement Regression and 50% improvement in RMSE over SOTA method in Valence-Arousal Regression. To tackle the scarcity of publicly available datasets in telemental health space, we release a new dataset, MEDICA, for mental health patient engagement detection. Our dataset, MEDICA consists of 1299 videos, each 3 seconds long. To the best of our knowledge, our approach is the first method to model telemental health session data based on psychology-driven Affective and Cognitive features, which also accounts for data sparsity by leveraging a semi-supervised setup

Secondary Seizures in the Pediatric Population in Two Tertiary Hospitals in India

Objective: To evaluate the clinical pattern of secondary seizures which includes acute and remote symptomatic seizures among hospitalized patients in two healthcare centers and to assess the outcomes among hospitalized patients having secondary seizures. Methods: This multicentric cross-sectional study was conducted in two tertiary hospitals in Odisha and Tamil Nadu, India, for a period of four years. A total of 274 patients in the age group between 6 months to 12 years participated in the study. A structured proforma was used to document the clinical pattern and causes of the secondary seizures. Results: Among the participants in Odisha and Tamil Nadu hospitals, focal seizures constituted 67.5%. Generalized seizures were present in 32.4%. The key causes of seizures in Odisha were malaria, cerebral palsy, and viral meningitis, while in Tamil Nadu, the causes were neurocysticercosis, cerebral palsy, and viral meningitis. Conclusion: Since the majority of the causes are preventable, it is important to address the issue at the public health level, by providing improved sanitation and adequate awareness on the secondary seizure and its causes. It is also important that the physicians are well conversant with the early case detection and treatment of primary diseases causing secondary seizures