Identification and monitoring of cell heterogeneity from plasmid recombination during limonene production.

Detecting alterations in plasmid structures is often performed using conventional molecular biology. However, these methods are laborious and time-consuming for studying the conditions inducing these mutations, which prevent real-time access to cell heterogeneity during bioproduction. In this work, we propose combining both flow cytometry and fluorescence-activated cell sorting, integrated with mechanistic modelling to study conditions that lead to plasmid recombination using a limonene-producing microbial system as a case study.

Sex-associated differences in incentive salience and drinking behaviour in a rodent model of alcohol relapse.

The ability of environmental cues to trigger alcohol-seeking behaviours is thought to facilitate problematic alcohol use. Individuals' tendency to attribute incentive salience to cues may increase the risk of addiction. We sought to study the relationship between incentive salience and alcohol addiction using non-preferring rats to model the heterogeneity of human alcohol consumption, investigating both males and females.

Disentangling Sources of Momentum Fluctuations in Xe+Xe and Pb+Pb Collisions with the ATLAS Detector.

High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time.