The microbiota metabolite, phloroglucinol, confers long-term protection against inflammation.

Phloroglucinol is a key byproduct of gut microbial metabolism that has been widely used as a treatment for irritable bowel syndrome. Here, we demonstrate that phloroglucinol tempers macrophage responses to pro-inflammatory pathogens and stimuli. , phloroglucinol administration decreases gut and extraintestinal inflammation in murine models of inflammatory bowel disease and systemic infection.

Graph neural network-based anomaly detection for river network systems.

Background: Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology.Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions.

clusterBMA: Bayesian model averaging for clustering.

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty.

A global perspective on the intrinsic dimensionality of COVID-19 data.

We develop a novel global perspective of the complexity of the relationships between three COVID-19 datasets, the standardised per-capita growth rate of COVID-19 cases and deaths, and the Oxford Coronavirus Government Response Tracker COVID-19 Stringency Index (CSI) which is a measure describing a country's stringency of lockdown policies. We use a state-of-the-art heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. Our findings suggest that these highly popular COVID-19 statistics may project onto two low-dimensional manifolds without significant information loss, suggesting that COVID-19 data dynamics are generated from a latent mechanism characterised by a few important variables.

Microbiota and fungal-bacterial interactions in the cystic fibrosis lung.

The most common genetic hereditary disease affecting Caucasians is cystic fibrosis (CF), which is caused by autosomal recessive mutations in the CFTR gene. The most serious consequence is the production of a thick and sticky mucus in the respiratory tract, which entraps airborne microorganisms and facilitates colonization, inflammation and infection. Therefore, the present article compiles the information about the microbiota and, particularly, the inter-kingdom fungal-bacterial interactions in the CF lung, the molecules involved and the potential effects that these interactions may have on the course of the disease.

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products.

Correction to: Who meets national early childhood sleep guidelines in Aotearoa New Zealand? A cross-sectional and longitudinal analysis.

[This corrects the article DOI: 10.1093/sleepadvances/zpac002.].

Who meets national early childhood sleep guidelines in Aotearoa New Zealand? A cross-sectional and longitudinal analysis.

Study Objectives: To investigate the proportion of children in Aotearoa New Zealand (NZ) who do or do not meet sleep duration and sleep quality guidelines at 24 and 45 months of age and associated sociodemographic factors.

Methods: Participants were children ( = 6490) from the longitudinal study of child development with sleep data available at 24 and/or 45 months of age (48.2% girls, 51.

Connecting virtual reality and ecology: a new tool to run seamless immersive experiments in R.

Virtual reality (VR) technology is an emerging tool that is supporting the connection between conservation research and public engagement with environmental issues. The use of VR in ecology consists of interviewing diverse groups of people while they are immersed within a virtual ecosystem to produce better information than more traditional surveys. However, at present, the relatively high level of expertise in specific programming languages and disjoint pathways required to run VR experiments hinder their wider application in ecology and other sciences.

Development and evaluation of a matrix for assessing fatigue-related risk, derived from a national survey of nurses' work patterns.

Background: Multiple aspects of nurses' rosters interact to affect the quality of patient care they can provide and their own health, safety and wellbeing.

Objectives: (1) Develop and test a matrix incorporating multiple aspects of rosters and recovery sleep that are individually associated with three fatigue-related outcomes - fatigue-related clinical errors, excessive sleepiness and sleepy driving; and (2) evaluate whether the matrix also predicts nurses' ratings of the effects of rosters on aspects of life outside work.

Design: Develop and test the matrix using data from a national survey of nurses' fatigue and work patterns in six hospital-based practice areas with high fatigue risk.

Fatigue and nurses' work patterns: An online questionnaire survey.

Background: Fatigue resulting from shift work and extended hours can compromise patient care and the safety and health of nurses, as well as increasing nursing turnover and health care costs.

Objectives: This research aimed to identify aspects of nurses' work patterns associated with increased risk of reporting fatigue-related outcomes.

Design: A national survey of work patterns and fatigue-related outcomes in 6 practice areas expected to have high fatigue risk (child health including neonatology, cardiac care/intensive care, emergency and trauma, in-patient mental health, medical, and surgical nursing).

Monitoring the Effectiveness of Fatigue Risk Management: A Survey of Pilots' Concerns.

Introduction: Airlines are required to monitor the effectiveness of their pilot fatigue risk management. The present survey sought the views of all pilots at Delta Air Lines on fatigue-related issues raised by their colleagues participating in regular airline safety audits.

Methods: All 13,217 pilots from 9 aircraft fleets were invited to participate in an anonymous online survey.