Azarabad, M. H. | Search bioRxiv

bioRxiv2025

Analytical meditation improves physiological well-being in expert practitioners: a study on central and peripheral neurophysiological correlates

Meditation has been long associated with improvements in mental well-being, emotional regulation, and attentional control. Yet, the diversity of meditative techniques and participant expertise has hindered the systematic identification of their neurophysiological correlates supporting these benefits. To address this challenge, we investigated the neurophysiological signatures of concentrative and analytical meditation in 35 experienced Tibetan monk practitioners. EEG, ECG, EDA, and respiration were simultaneously recorded during Concentrative, Loving-Kindness and Emptiness meditations. Linear-mixed-models revealed significant modulations in autonomic and cortical activity across meditations. Peripheral indices indicated enhanced parasympathetic tone, decreases respiratory rate, and gradual increase in EDA - reflecting a state of relaxed-alertness with concurrent vagal engagement and sustained sympathetic arousal. EEG analyses supported this state showing elevated gamma-band power during analytical meditations. These findings suggest that advanced meditative states foster an adaptive integration of autonomic and cortical responses, supporting the emergence of relaxed-vigilance - a psychophysiological condition associated with well-being and cognitive flexibility.

neuroscience↗

bioRxiv2019

TAXyl: An in-silico method for predicting the thermal activity for xylanases from GH10 and GH11 families

Growing industrial utilization of enzymes, and the increasing availability of metagenomic data highlights the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. Optimum temperature of enzymes are essential factors to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in-silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in identification of three novel xylanases from sheep and cow rumen microbiota. Here we present TAXyl (Thermal Activity Prediction for Xylanase), a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. Models performance was evaluated through multiple iterations of six-fold cross-validations, and it exhibited a mean accuracy of [~]0.79. TAXyl is freely accessible as a web-service.

bioinformatics↗