Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshwater ecosystem management. This study uses advanced machine learning models to analyse water quality parameters and predict nitrate-N concentrations in the lower stretch of the Ganga River from the observations of six annual periods (2017 to 2022).

A Latent Trait-based Measure as a Data Harmonization and Missing Data Solution Applied to the Environmental Influences on Child Health Outcomes Cohort.

Background: Collaborative research consortia provide an efficient method to increase sample size, enabling evaluation of subgroup heterogeneity and rare outcomes. In addition to missing data challenges faced by all cohort studies like nonresponse and attrition, collaborative studies have missing data due to differences in study design and measurement of the contributing studies.

Methods: We extend ROSETTA, a latent variable method that creates common measures across datasets collecting the same latent constructs with only partial overlap in measures, to define a common measure of socioeconomic status (SES) across cohorts with varying indicators in the Environmental influences on Child Health Outcomes Cohort, a consortium of pregnancy and pediatric cohorts.

LONG-TERM PROGNOSIS OF NON-CELIAC ENTEROPATHIES AND A SCORE TO IDENTIFY PATIENTS WITH POOR OUTCOMES: A 30-YEAR MULTICENTER LONGITUDINAL STUDY.

Introduction: Long-term prognosis of non-celiac enteropathies (NCEs) is poorly understood. We aimed to evaluate long-term outcomes and develop a prognostic score for NCEs.

Methods: NCEs patients from an international multicenter cohort (4 Italian centers,1 UK, 1 French,1 Norwegian,1 USA,1 Indian) followed-up over 30 years were enrolled.

Trends in the incidence and mutational landscape of advanced uterine cancer.

Objective: The aim of this study was to examine disparities in 20-year incidence trends and mutations in advanced-stage uterine cancer in the United States, given poor survival rates.

Methods: Data were obtained from the United States Cancer Statistics for patients from 2001 to 2019 with International Federation of Gynecology and Obstetrics 2009 stage IVA and IVB uterine cancer. SEER∗Stat 8.

Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3.

Apical and basal dendrites of pyramidal neurons receive anatomically and functionally distinct inputs, implying compartment-level functional diversity during behavior. To test this, we imaged in vivo calcium signals from soma, apical dendrites, and basal dendrites in mouse hippocampal CA3 pyramidal neurons during head-fixed navigation. To capture compartment-specific population dynamics, we developed computational tools to automatically segment dendrites and extract accurate fluorescence traces from densely labeled neurons.