Classification of bone defects using natural and synthetic X-ray images

In this thesis, we study methods to reduce the amount of data needed to create deep learning models that can detect defects in bones from X-ray images. Detecting defects in bones from X-ray images and properly annotating the images is the paramount step when it comes to corrective surgeries of bones. Annotations or labels, such as radial inclination and volar tilt are measurements that are necessary for many corrective surgeries. Generating these annotations is an arduous and manual task for medical professionals. By being able to automate the process of generating these annotations, it will be possible to reduce a significant amount of labor of these professionals. Modern deep learning models are heavily reliant upon availability of a large amount of properly labeled data for their training. In this thesis, we experimented to find methods to create appropriate synthetic data that can be combined with natural data to train deep learning models. We designed three deep learning models to generate two different forms of annotations. The first goal was to use cycle consistent generative adversarial networks to create proper synthetic images. Then we used the synthetic images to improve classifier models that can detect defects in bones. In the end, we expanded the cycle consistent generative adversarial network so that it can accommodate three input domains instead of two and called it multi-cycleGAN. We used multi-cycleGAN to segment bones from natural X-ray images. Our experiments concluded that by adding proper synthetic images with natural images, we can improve the performance of classifiers significantly and circumvent the persistent issue of unavailability of data. However, the multi-cycleGAN model did not generate a very accurate segmentation of bones. It was able to segment bones of forearm better than bones of wrist. It was able to understand the overall shape and positioning of the wrists in X-ray images but it did not produce proper segmentations of the individual fingers

Fair Heroes and Heroines, Dark Commoners-Colourism in Bangla Films

In human society there exists colour-based variation. In many ethnicities, colour variation is associated with beauty and attractiveness prevails in society. In dominant endogamous societies within an ethnicity, these characteristics are preserved and highly prioritized as markers of physical attractiveness in that ethnicity. Such dominant groups within an ethnicity dominate others, among other things, in terms of public portrayal, places, spaces and various opportunities on the basis of characteristics largely possessed and hence prized by dominant groups and are held up as aspirational goals to the rest. People who have fairer skin tone as compared to the darker skin tone tries to dominate other in terms of public portrayal. These public portrayals are clearly seen in the case of visual mass media like cinema and advertisement. This paper explores whether such skin colour tone based bias exists in case of Bangali ethnicity. The skin tone of heroes and heroines of popular Bangla films produced in West Bengal was taken as a proxy to explore the nature of skin colour tone based bias (if any), in case of the Bangla mother tongue population – the 5th largest mother tongue population in the world. We found that the heroes and heroines have significantly lighter skin tones than other males and females of same ethnicity who are portrayed in a film. The results suggest that there exists significant skin colour based bias in the selection of heroes and heroines in Bangla films

Fair Heroes and Heroines, Dark Commoners-Colorism in Bangla Films

In human society there exists colour based variation. In many ethnicities, colour variation is associated with beauty and attractiveness prevails in society. In dominant endogamous societies within an ethnicity, these characteristics are preserved and highly prioritized as markers of physical attractiveness in that ethnicity. Such dominant groups within an ethnicity dominate others, among other things, in terms of public portrayal, places, spaces and various opportunities on the basis of characteristics largely possessed and hence prized by dominant groups and are held up as aspiration goals to the rest. People who have fairer skin tone as compared to the darker skin tone tries to dominate other in terms of public portrayal. These public portrayals are clearly seen in the case of visual mass media like cinema and advertisement. This paper explores whether such skin colour tone based bias exists in case of Bangali ethnicity. The skin tone of heroes and heroines of popular Bangla films produced in West Bengal was taken as a proxy to explore the nature of skin colour tone based bias (if any), in case of the Bangla mother tongue population, the 5th largest mother tongue population in the world. We found that the heroes and heroines have significantly lighter skin tones than other males and females of same ethnicity who are portrayed in a film. The results suggest that there exists significant skin colour based bias in the selection of heroes and heroines in Bangla films

RADAR-Pipeline: Scalable Feature Generation for Mobile Health Data

Introduction & Background RADAR-Pipeline is an open-source Python framework designed to simplify and enhance mobile health data analysis. It has been designed to efficiently read and process the large amount of data generated through the RADAR-Base platform. RADAR-base is a scalable, real-time streaming and analytics open-source platform to facilitate research access and customisation requirements. Studies using the Radar-base platform have collected fine-grained longitudinal data from wearables and phones. The data can potentially create multitudes of digital biomarkers, which can be used to inform us greatly about the disease condition. Due to the sheer size of the data, it can be difficult for researchers to read and process those data -- a common task is identifying useful features and common data processing/analysis steps previously used by the community. Up to now, these have been hand-crafted by individual data scientists, often lacking the capability to be easily reused by the community without author-specific knowledge. Furthermore, generating variables based on already established research on a larger scale can be challenging and could hinder replication. Hence, we have designed RADAR-Pipeline to help researchers overcome these challenges. It empowers them to create and share their data analysis and visualisation pipelines, fostering collaboration and knowledge sharing within the research community. Objectives & Approach The primary objective of RADAR-Pipeline is to offer researchers a user-friendly and powerful platform to develop and share their research.  Researchers can build reusable analysis and visualisation pipelines to ensure consistent and reliable results. It simplifies big data analysis by leveraging Apache Spark to handle large and complex mobile health datasets efficiently.  Researchers can also save time and effort by reusing and extending existing pipelines built by others. Finally, the RADAR-Pipeline promotes collaboration and recognition by allowing researchers to share their work through the RADAR-base Analytics Catalogue, making their pipelines citable and accessible to the wider research community. Whilst Radar-pipeline has been designed to read data from Radar-base, it can also be used to read data from any dataset which uses Hadoop Distributed File System (HDFS) file system namespace. Relevance to Digital Footprints Mobile health data is rich and valuable for understanding human behaviour and health. RADAR-Pipeline addresses the challenges associated with analysing large and complex mobile health datasets, enabling researchers to extract valuable insights that can be used to (1) Improve public health: By enabling efficient analysis of large-scale mobile health data, RADAR-Pipeline can contribute to research efforts aimed at improving population health outcomes and developing effective interventions; (2) Personalised healthcare: By facilitating the extraction of individual-level features from mobile health data, RADAR-Pipeline can seamlessly be integrated with Kafka data streams and machine learning pipelines to process the data in real-time, which can then be utilised to create more effective and targeted real-time interventions. (3) Promote reproducible research: The framework's emphasis on transparency and reproducibility in research aligns with the conference's focus on the responsible use of digital mobile health data. Conclusions & Implications RADAR-Pipeline is a valuable tool for researchers, offering them the means to harness the potential of mobile health data. By adopting this framework, researchers can achieve efficient and scalable data analysis, thereby streamlining the extracting insights from digital footprints. This efficiency enables researchers to delve deeper into the data and uncover valuable patterns and trends. Furthermore, RADAR-Pipeline promotes collaboration and knowledge sharing within the research community. By providing a standardised framework for data analysis, RADAR-Pipeline facilitates collaboration among researchers, leading to the sharing of best practices and the dissemination of knowledge

Evaluation of bifacial module technologies with combined-accelerated stress testing

In view of the increasing interest and market share of bifacial cells and modules, suitable substrates such as glass and transparent backsheets along with ethylene vinyl acetate (EVA) and polyolefin elastomer (POE) encapsulants were examined in combined-accelerated stress testing (C-AST) to evaluate and compare degradation modes. Testing with both monofacial and bifacial cells, we found that glass–glass modules with monofacial cells led to greater grid finger breakage than those with polymeric backsheets. This is attributed to previous X-ray topography and modeling work showing higher stress in cells and interconnections in glass–glass modules than glass backsheet modules. Consistent with the objectives of C-AST, which stresses modules at levels corresponding to the limits seen in the natural environment, we observed the UV-fluorescence signatures of modules tested in C-AST (considering the degradation associated with developing chromophores, moisture penetration, and photobleaching effects) to be like those in fielded modules, more so than other chamber stress testing implemented for comparison. We found light-induced degradation (LID) in module types with regenerated (inactive) cells with C-AST, suggesting the possibility of LID destabilization in some field conditions. We could also distinguish potential-induced degradation (PID) on the back of the bifacial passivated emitter and rear cells (PERC) in C-AST. Confirming with ex situ tests, we found polarization-type PID most prevalent in glass-glass modules with EVA as would be anticipated considering the greater leakage current through such module encapsulation. Unlike PID tests performed in the dark, which can lead to false positive PID test results, field-representative illumination is experienced by the modules on the front and back sides while −1200 V system voltage is applied in C-AST, supporting the conclusion that this module type with glass-glass construction would be susceptible to PID in the field.</p

Dietary and environmental risk factors in Parkinson's and Alzheimer's disease: A semi-quantitative pilot study

Objective: Environmental influence and dietary variations are well-known risk factors for various diseases including neurodegenerative disorders. Preliminary evidence suggests that diet in early-life and living environment might influence the incidence of Parkinson's disease (PD) in later phase of life. There have been limited epidemiologic studies on this aspect especially in India. In this hospital-based case-control study, we intended to identify dietary and environmental risk factors of PD. Methods: Patients with PD (n = 105), Alzheimer's disease (AD) (n = 53) and healthy individuals (n = 81) were recruited. Dietary intake and environmental exposures were assessed using a validated Food-Frequency and Environmental Hazard Questionnaire. Their demographic details and living environment were also recorded using the same questionnaire. Results: Pre-morbid consumption of carbohydrate and fat was significantly higher whereas dietary fiber and fruit content was significantly lesser in PD as compared to AD and healthy age-matched controls. Meat and milk intake was the highest among all the food groups in PD patients. Rural living and their habitation near water bodies were significantly more frequent in PD patients. Conclusion: We found that past intake of carbohydrate, fat, milk, and meat are associated with increased risk of PD. On the other hand, rural living and habitat near water bodies might be associated with incidence and severity of PD. Hence, preventive strategies related to dietary and environmental modulators in PD might be clinically useful in the future

Data_Sheet_1_Effects of non-invasive vagus nerve stimulation on clinical symptoms and molecular biomarkers in Parkinson’s disease.doc

Non-invasive vagus nerve stimulation (nVNS) is an established neurostimulation therapy used in the treatment of epilepsy, migraine and cluster headache. In this randomized, double-blind, sham-controlled trial we explored the role of nVNS in the treatment of gait and other motor symptoms in Parkinson’s disease (PD) patients. In a subgroup of patients, we measured selected neurotrophins, inflammatory markers and markers of oxidative stress in serum. Thirty-three PD patients with freezing of gait (FOG) were randomized to either active nVNS or sham nVNS. After baseline assessments, patients were instructed to deliver six 2  min stimulations (12  min/day) of the active nVNS/sham nVNS device for 1  month at home. Patients were then re-assessed. After a one-month washout period, they were allocated to the alternate treatment arm and the same process was followed. Significant improvements in key gait parameters (speed, stance time and step length) were observed with active nVNS. While serum tumor necrosis factor- α decreased, glutathione and brain-derived neurotrophic factor levels increased significantly (p Clinical trial registration: identifier ISRCTN14797144.</p

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-