Spirituality in Indian University students and its associations with socioeconomic status, religious background, social support, and mental health

The present study aimed to understand spirituality and its relationships with socioeconomic status (SES), religious background, social support, and mental health among Indian university students. It was hypothesized that: - (1) female university students will be more spiritual than male university students, - (2) four domains of spirituality will differ significantly across socioeconomic and religious background of the university students in addition to social support, and; - (3) there will be a positive relationship between spirituality and mental health of university students, irrespective of gender. A group of 475 postgraduate students aged 20–27 years, 241 males and 234 females, from various disciplines of Pondicherry University, India, participated in the study. Students’ background was collected using a structured questionnaire. Overall spirituality and its four dimensions were measured using the Spirituality Attitude Inventory, while mental health status was estimated based on scores of the psychological subscale of the WHO Quality of Life Questionnaire. Female students were significantly more spiritual than male students, particularly in spiritual practice and sense of purpose/connection. Hindu religion and lower family income were associated with lower spirituality. Higher spirituality was associated with congenial family environment and more support from teachers and classmates. There was a strong association between overall spirituality and two spirituality domains (spiritual belief and sense of purpose/connection) with better mental health. Findings suggest an opportunity for open dialogue on spirituality for university students as part of their mental health and support services that fosters a positive mind set and enhancement of resilience.

Next-generation sequencing: A frameshift in skeletal dysplasia gene discovery

In the last decade, huge breakthroughs in genetics - driven by new technology and different statistical approaches - have resulted in a plethora of new disease genes identified for both common and rare diseases. Massive parallel sequencing, commonly known as next-generation sequencing, is the latest advance in genetics, and has already facilitated the discovery of the molecular cause of many monogenic disorders. This article describes this new technology and reviews how this approach has been used successfully in patients with skeletal dysplasias. Moreover, this article illustrates how the study of rare diseases can inform understanding and therapeutic developments for common diseases such as osteoporosis. © International Osteoporosis Foundation and National Osteoporosis Foundation 2013.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.