Studying the Imaging Characteristics of Ultra Violet Imaging Telescope (UVIT) through Numerical Simulations

Ultra-Violet Imaging Telescope (UVIT) is one of the five payloads aboard the Indian Space Research Organization (ISRO)'s ASTROSAT space mission. The science objectives of UVIT are broad, extending from individual hot stars, star-forming regions to active galactic nuclei. Imaging performance of UVIT would depend on several factors in addition to the optics, e.g. resolution of the detectors, Satellite Drift and Jitter, image frame acquisition rate, sky background, source intensity etc. The use of intensified CMOS-imager based photon counting detectors in UVIT put their own complexity over reconstruction of the images. All these factors could lead to several systematic effects in the reconstructed images. A study has been done through numerical simulations with artificial point sources and archival image of a galaxy from GALEX data archive, to explore the effects of all the above mentioned parameters on the reconstructed images. In particular the issues of angular resolution, photometric accuracy and photometric-nonlinearity associated with the intensified CMOS-imager based photon counting detectors have been investigated. The photon events in image frames are detected by three different centroid algorithms with some energy thresholds. Our results show that in presence of bright sources, reconstructed images from UVIT would suffer from photometric distortion in a complex way and the presence of overlapping photon events could lead to complex patterns near the bright sources. Further the angular resolution, photometric accuracy and distortion would depend on the values of various thresholds chosen to detect photon events.Comment: Submitted to PASP, 16 Pages, 9 figure

Comparative study of effects of ramosetron and ondansetron on global satisfaction of patients on cisplatin chemotherapy in head and neck cancers

Background: To compare level of satisfaction of the patients receiving ramosetron and ondansetron in prevention of acute and delayed nausea and vomiting associated with cisplatin chemotherapy.Methods: 60 patients were recruited in the study. Patients were randomly allocated to ramosetron (R) and ondansetron group (O). Patients were screened between day 1 and day 7. Study visits included clinic visits on day 8, day 9 and day 14. Patient diaries were used to record patients’ global satisfaction which was based on severity of nausea and vomiting using visual analogue scale (VAS), recorded daily until day 12 starting from day 8. On 14th day the patient diary cards were collected back.Results: VAS score was significantly lower in R group (46.2±4.95) as compared to O group (63.7±5.06) (p<0.01) in acute phase of nausea and vomiting indicating level of satisfaction higher in R group. Similarly, in delayed and overall phase R group (49.57±14.63 and 48.9±12.91 respectively) experienced lower range of scoring on VAS scale as compared to O group (63.0±8.49 and 63.10±7.38 respectively). The difference was statistically significant (p<0.01).Conclusions: Level of overall satisfaction of the patients in R group was significantly higher as compared to O group in patients receiving the two drugs for prevention of nausea and vomiting caused by cisplatin chemotherapy in head and neck cancer patients

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

ROLE OF DIET AND LIFESTYLE IN THE PREVENTION OF MADHUMEHA

Ayurveda is a natural health care system that originated in India since the beginning of civilization. It is described by Acharaya Charak that to achieve Purushartha Chatushtaya, Arogya is necessary. Ayurveda strongly emphasize on preventive and promotive aspects of health rather than curative. The concepts of Dincharya, Ritucharya, Sadvritta, and Achara Rasayana along with guidelines for healthy diet and lifestyle is well established in Ayurveda, but in current scenario, hardly anyone aptly follow it. As a result, there is tremendous rise in lifestyle disorders as pandemics, Diabetes being most menacing among them. Diabetes is the fourth leading cause of global death by disease. Type 2 DM is responsible for approximately 90% of cases. In Ayurveda, Madhumeha one of the types of Vataja Prameha is compared to Diabetes Mellitus because of having similarities of disease in respect to etiopathogenesis, clinical features and prognosis. The main causes of Madhumeha are lack of exercise, improper food habits, excessive intake of food having Snigdha and Guru Guna and food which causes vitiation of Kapha Dosha. Modern therapeutics has many limitations but Ayurvedic principles of management can help the patient to control blood glucose level and have better routine life. So Ayurvedic lifestyle guidelines of adopting a healthy dietary pattern together with physical activity are valuable tools in the prevention of Madhumeha

VIVID: A web application for variant interpretation and visualization in multi-dimensional analyses

Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3D visualization of genotypic information encoded in Variant Call Format on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritizing targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

Machine Learning for Microcontroller-Class Hardware: A Review.

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward

Fuel-Efficiency Improvement by Component-Size Optimization in Hybrid Electric Vehicles

Hybrid electric vehicles (HEV) play an important role in sustainable transportation systems. The component size of HEV plays a vital role in the fuel efficiency of vehicles. This paper presents a divided rectangle (DIRECT) method for component sizing of vehicles to ensure better fuel efficiency and satisfying drivability. A state–space model was used to represent the design problem. A constraint multi-input multi-output optimization problem was solved by our DIRECT optimization algorithm. Efficacy of the algorithm was tested with standard drive cycles, including drive cycles for Indian urban and highway conditions representing various driving scenarios in the country. The simulation results illustrated the effectiveness of the proposed algorithm