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Human Activity Recognition (HAR) has emerged as a major player in this era of cutting-edge technological advancement. A key role that HAR plays is its ability to remotely monitor people. The objective of this paper is to classify human activities based on the data retrieved from smartphone sensors (accelerometer and gyroscope). The human activities that will be classified are namely; sitting, standing, climbing up and down the staircase, walking and laying down. To perform HAR from the data obtained, machine learning models are formed and fine-tuned in order to achieve the best results. The classic Machine Learning algorithms that have been put to use are Logistic regression, Linear and Kernel SVM, Decision Tree and Random Forest. Furthermore, a feed-forward Deep Neural Network and a 1D Convolutional Neural Network are proposed and compared with the aforementioned machine learning algorithms. Evaluation of a particular model has been carried out based on its Recall score, Precision and F1 score. Based on the results obtained from the evaluation process, it was found that the SVM and the proposed 1D convolution neural network were the best-performing models.

ObjectivesThe National Mental Health Survey (NMHS) of India was undertaken with the objectives of (1) estimating the prevalence and patterns of various mental disorders in representative Indian population and (2) identifying the treatment gap, healthcare utilisation, disabilities and impact of mental disorders. This paper highlights findings pertaining to depressive disorders (DD) from the NMHS.DesignMultisite population-based cross-sectional study. Subjects were selected by multistage stratified random cluster sampling technique with random selection based on probability proportionate to size at each stage.SettingConducted across 12 states in India (representing varied cultural and geographical diversity), employing uniform, standardised and robust methodology.ParticipantsA total of 34 802 adults (>18 years) were interviewed.Main outcome measurePrevalence of depressive disorders (ICD-10 DCR) diagnosed using Mini International Neuropsychiatric Interview V.6.0.ResultsThe weighted prevalence of lifetime and current DD was 5.25% (95% CI: 5.21% to 5.29%, n=34 802) and 2.68% (95% CI: 2.65% to 2.71%, n=34 802), respectively. Prevalence was highest in the 40–59 age groups (3.6%, n=10 302), among females (3.0%, n=18 217) and those residing in cities with population >1 million (5.2%, n=4244). Age, gender, place of residence, education and household income were found to be significantly associated with current DD. Nearly two-thirds of individuals with DD reported disability of varying severity, and the treatment gap for depression in the study population was 79.1%. On an average, households spent INR1500/month (~US$ 23.0/month) towards care of persons affected with DD.ConclusionAround 23 million adults would need care for DD in India at any given time. Since productive population is affected most, DD entails considerable socioeconomic impact at individual and family levels. This is a clarion call for all the concerned stakeholders to scale up services under National Mental Health Programme in India along with integrating care for DD with other ongoing national health programmes.

The biosynthesis of plumbagin is known to occur via the acetate polymalonate pathway; however there are several intermediary steps that remain unidentified that leads to its synthesis. The study identifies enzyme naphthoate synthase to catalyze the cyclization of O-malonyl benzoyl CoA to form an intermediate that is acted upon by thioesterase before the reaction proceeds to form plumbagin. Two possible structures were predicted for this intermediate using quantum mechanics studies. A total of 60 ns molecular dynamics simulations revealed the most probable intermediate structure of the predicted two.