ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices.

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualised meditation practices and designed various meditation interventions (MIs), that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has examined the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and non-clinical populations.

Functional connectivity changes in meditators and novices during yoga nidra practice.

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice.

Correction: Characterizing surface wetting and interfacial properties using enhanced sampling (SWIPES).

Correction for 'Characterizing surface wetting and interfacial properties using enhanced sampling (SWIPES)' by Hao Jiang et al., Soft Matter, 2019, 15, 860-869, DOI: .

Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients.

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectrum of conditions: benign steatosis or non-alcoholic fatty liver (NAFL), steatosis accompanied by inflammation and fibrosis or nonalcoholic steatohepatitis (NASH), and cirrhosis.

Characterizing surface wetting and interfacial properties using enhanced sampling (SWIPES).

We introduce an accurate and efficient method for characterizing surface wetting and interfacial properties, such as the contact angle made by a liquid droplet on a solid surface, and the vapor-liquid surface tension of a fluid. The method makes use of molecular simulations in conjunction with the indirect umbrella sampling technique to systematically wet the surface and estimate the corresponding free energy. To illustrate the method, we study the wetting of a family of Lennard-Jones surfaces by water.