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Background/Aims: Primary sclerosing cholangitis (PSC) represents the most common hepatobiliary extraintestinal manifestation of inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD). Limited data exist on PSC in patients with IBD from India. We aimed to assess the prevalence and disease spectrum of PSC in Indian patients with IBD. Methods: Database of IBD patients at 5 tertiary care IBD centers in India were analyzed retrospectively. Data were extracted and the prevalence of PSC-IBD was calculated. Results: Forty-eight patients out of 12,216 patients with IBD (9,231 UC, 2,939 CD, and 46 IBD unclassified) were identified to have PSC, resulting in a prevalence of 0.39%. The UC to CD ratio was 7:1. Male sex and pancolitis (UC) or colonic CD were more commonly associated with PSC-IBD. The diagnosis of IBD preceded the diagnosis of PSC in most of the patients. Majority of the patients were symptomatic for liver disease at diagnosis. Eight patients (16.66%) developed cirrhosis, 5 patients (10.41%), all UC, developed malignancies (3 colorectal cancer [6.25%] and 2 cholangiocarcinoma [4.16%]), and 3 patients died (2 decompensated liver disease [4.16%] and 1 cholangiocarcinoma [2.08%]) on follow-up. None of the patients mandated surgical therapy for IBD. Conclusions: Concomitant PSC in patients with IBD is uncommon in India and is associated with lower rates of development of malignancies. [ABSTRACT FROM AUTHOR]

Early rainfall prediction is very important to ensure the economic balance of any agriculture-dominated country, such as India. Deep learning has recently received considerable attention for predicting rainfall using historical data. However, rainfall depends on multiple factors like ElNino, rising sea levels, warming of the Atlantic Ocean, etc. The changing climate has also significantly increased rainfall variability. The current deep-learning approaches are either based on a limited set of predictors or do not utilize the time-series properties for the recurrence of similar climate conditions every 12 months. Besides, they perform poorly in predicting accurate rainfall estimates for the diverse areas of India due to limited data availability across various regions. Therefore, this paper presents a groundbreaking solution to address the challenge of limited data by introducing RfGan (Rainfall GAN), a novel variant of generative adversarial networks. RfGan aims to fulfill the crucial need for abundant data to ensure the effectiveness of model training. Moreover, a novel rainfall prediction method, RfGanNet, has been introduced that leverages the strengths of the proposed RfGan for data augmentation, long-short-term memory, and convolutional neural networks for rainfall prediction. The performance of RfGanNet has been validated for India and its clustered regions in terms of nine parameters: precision, recall, specificity, Matthews correlation coefficient, accuracy, F1-score, mean absolute error, mean square error and computation time. The experimental results of RfGan are compared against original and synthetic datasets generated from state-of-the-art methods, namely tabular gan (TGAN), Wasserstein gan (WGAN), and conditional tabular gan (CTGAN). The experimental results indicate that the proposed RfNet model, when trained using RfGan-generated data, surpassed all the other considered methods by achieving 97.22 % accuracy. Moreover, RfNet has been compared with other related deep Learning approaches to validate its efficacy. Additionally, an ablation study investigating various LSTM–CNN combinations has been conducted. The findings of the study indicate that oceanic and climate-related predictors have a noteworthy influence on Indian rainfall. Moreover, the study highlights the suitability of combining Bidirectional LSTM, CNN, and multiple predictors for accurate rainfall prediction. • Novel variant of generative adversarial network- RfGan is proposed. • Different combinations of LSTM and CNN for rainfall prediction are tested. • Novel classifier — RfNet is tested on different augmented datasets. • K-means clustered regions of India have been identified. [ABSTRACT FROM AUTHOR]

Rail-based metro system is crucial for supporting a city's expansion and growth. However, urban metro projects being capital intensive with long gestation and pay back periods may not be financially viable but socially desirable. More and more cities in India are announcing metro projects committing huge budgetary resources to these projects at the cost of sectors such as health and education. Given the huge requirement of capital and possibility of attracting willing as well as capable private investors to undertake urban transport projects, promoting public-private partnerships (PPPs) could be a key priority. However, none of the metro projects launched during past 7 years in India have been planned on PPP framework. International experience in PPP metro and our own Delhi airport metro experience have demonstrated that many issues can influence the successful implementation of PPPs in such projects. In this context, outcome of Hyderabad metro is keenly watched as it is one of the largest metro rail projects built under a PPP framework anywhere in the world. The paper reviews the research conducted on critical success factors (CSFs) that are necessary for adopting PPP models in urban metros in Indian context. Eighteen CSFs identified through literature survey have been validated in a pilot study through structured questionnaire and grouped in seven macro-factors through hierarchical cluster analysis. A case study has been attempted using SAP-LAP framework to analyse the identified CSFs in real-life settings of the first two stages of Hyderabad metro project. Key players working on the project were interviewed during a field study to gather feedback on actions they took on factors critical to success. Observations and findings have been synthesised as learnings for future such projects. [ABSTRACT FROM AUTHOR]

The present study deals with biology, ecology and population dynamics of freshwater turtle Kachuga tentoria and its role as water purifier. The study area Panchnada is the site, where five important national rivers meet together and is preserving an appreciable population of nine species of fresh water turtles. Kachuga tentoria was located at all the sampling stations surveyed by the authors, and hence selected for the present study. Different activities (nesting, incubation, predation and other reproductive aspects), climatic conditions, habitat, population density and morphometric features were worked out in detail. A time bound conservation strategy is needed to save this species from extinction. In situ conservation will be more helpful for the recruitment of the population of this species. It will help in the "hatch and release programme" to clean different polluted national rivers.