Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods.

Developing high-performance alloys is essential for applications in advanced electromagnetic energy conversion devices. In this study, we assess Fe-Co-Ni alloy compositions identified in our previous work through a machine learning (ML) framework, which used both multi-property ML models and multi-objective Bayesian optimization to design compositions with predicted high values of saturation magnetization, Curie temperature, and Vickers hardness. Experimental validation was conducted on two promising compositions synthesized using three different methods: arc melting, ball milling followed by spark plasma sintering (SPS), and chemical synthesis followed by SPS.

Influencing basic behavioral mechanisms of action while targeting daily walking in sedentary adults at risk for cardiovascular disease: randomised factorial design protocol.

Introduction: Cardiovascular disease (CVD) is the leading cause of mortality worldwide, though it may be prevented by increasing physical activity (PA). When behaviour change techniques (BCTs) are bundled together, they increase PA, though which individual BCTs increase PA (and the behavioural mechanism of action (MoA) responsible for said increase) have not been studied. The aim of this study is to conduct a randomised factorial experiment to determine which of four BCTs significantly engage the proposed MoA-self-efficacy for PA-in adults at risk for CVD.

mRNA vaccines encoding membrane-anchored RBDs of SARS-CoV-2 mutants induce strong humoral responses and can overcome immune imprinting.

We investigated mRNA vaccines encoding a membrane-anchored receptor-binding domain (RBD), each a fusion of a variant RBD, the transmembrane (TM) and cytoplasmic tail fragments of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. In naive mice, RBD-TM mRNA vaccines against SARS-CoV-2 variants induced strong humoral responses against the target RBD. Multiplex surrogate viral neutralization (sVNT) assays revealed broad neutralizing activity against a range of variant RBDs.

Characterizing Physiologic Swallowing Impairment Profiles: A Large-Scale Exploratory Study of Head and Neck Cancer, Stroke, Chronic Obstructive Pulmonary Disease, Dementia, and Parkinson's Disease.

Purpose: The purpose of the present study was to use a large swallowing database to explore and compare the swallow-physiology impairment profiles of five dysphagia-associated diagnoses: chronic obstructive pulmonary disease (COPD), dementia, head and neck cancer (HNC), Parkinson's disease (PD), and stroke.

Method: A total of 8,190 patients across five diagnoses were extracted from a de-identified swallowing database, that is, the Modified Barium Swallow Impairment Profile Swallowing Data Registry, for the present exploratory cross-sectional analysis. To identify the impairment profiles of the five diagnoses, we fit 18 partial proportional odds models, one for each of the 17 Modified Barium Swallow Impairment Profile components and the Penetration-Aspiration Scale, with impairment score as the dependent variable and diagnoses, age, sex, and race as the independent variables with interactions between age and diagnoses and between PD and dementia (in effect creating a PD with dementia [PDwDem] group).