Spectral Data Fusion From Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident.

This work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity.

Association Between Neighborhood Deprivation and Gestational Diabetes: A Systematic Review and Meta-Analysis.

Gestational diabetes mellitus (GDM) is a major global health concern, affecting maternal and child health. Although genetic predispositions and individual medical histories are well-recognized risk factors, emerging research suggests a significant impact of external factors like neighborhood socioeconomic characteristics. This study systematically reviews and meta-analyzes the association between neighborhood deprivation and GDM incidence.

Fabrication and characterisation of a flexible thermoelectric generator using PANI/graphite/bismuth telluride composites.

Unique thermoelectric properties of low-cost, widely available conducting polymers and multi-layered graphite structures have motivated the development of flexible thermoelectric generators using screen printing for low-temperature applications. Composites of polyaniline and graphite in different ratios with one weight percentage of bismuth telluride were prepared to fabricate flexible thermoelectric generators. The performance of the devices showed that the addition of graphite to polyaniline reduced the band gap energy from 2.

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records.

Differential associations of anti-cytomegalovirus antibodies and soluble CD14 levels with immunosenescence in people living with HIV on long term antiretroviral therapy.

Background: People living with HIV (PLHIV) demonstrate accelerated aging and immunosenescence in spite of immune-restoration following long-term antiretroviral treatment (ART). Low level inflammation leading to inflammaging plays an important role in mediating premature immunosenescence. Ongoing viral replication, antiretrovirals and subclinical infections with the common viruses like Cytomegalovirus (CMV) are known to induce inflammaging.