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Single‐atom catalysts have garnered significant attention due to their exceptional atom utilization and unique properties. However, the practical application of these catalysts is often impeded by challenges such as sintering‐induced instability and poisoning of isolated atoms due to strong gas adsorption. In this study, we employed the mechanochemical method to insert single Cu atoms into the subsurface of Fe2O3 support. By manipulating the location of single atoms at the surface or subsurface, catalysts with distinct adsorption properties and reaction mechanisms can be achieved. It was observed that the subsurface Cu single atoms in Fe2O3 remained isolated under both oxidation and reduction environments, whereas surface Cu single atoms on Fe2O3 experienced sintering under reduction conditions. The unique properties of these subsurface single‐atom catalysts call for innovations and new understandings in catalyst design. [ABSTRACT FROM AUTHOR]

INTRODUCTION: CD33 rs3865444 and hypertension (HTN) are related to cognitive impairment, individually. However, little is known about their combined effects on cognitive function in older adults. METHODS: This population‐based study included 4368 dementia‐free participants (age ≥65 years) in the Multimodal Interventions to Delay Dementia and Disability in Rural China (MIND‐China), with data available in 1044 persons for gray matter volume and 85 persons for cerebral blood flow (CBF). We used general linear regression and mediation models to examine the associations of rs3865444 and HTN with cognition, brain atrophy, and CBF. RESULTS: Among rs3865444 CC carriers, HTN and late‐life HTN were significantly associated with impaired cognition. Midlife and late‐life HTN were correlated with brain atrophy. CD33 rs3865444 CC moderated the mediation effect of gray matter volume on the association between HTN and global cognition. HTN was correlated with low CBF in rs3865444 CC carriers. DISCUSSION: There are synergistic associations of CD33 rs3865444 and HTN with brain and cognitive aging in dementia‐free older adults. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We examined the associations of polygenic risk score (PRS) with Alzheimer's disease (AD) and plasma biomarkers in the Chinese population. METHODS: This population‐based study used baseline data from MIND‐China (2018; n = 4873) and follow‐up data from dementia‐free individuals (2014–2018; n = 2117). We measured AD‐related plasma biomarkers in a subsample (n = 1256). Data were analyzed using logistic and Cox regression models. RESULTS: We developed PRS with (PRSAPOE) and without (PRSnon‐APOE) apolipoprotein E (APOE) gene. In the longitudinal analysis, PRSAPOE was associated with a multivariable‐adjusted hazards ratio of 1.91 (95% CI = 1.13–3.23) for AD. PRSAPOE in combination with demographics yielded discriminative (area under the curve [AUC]) and predictive(C‐statistic) accuracy of 0.80 (95% confidence interval [CI] = 0.77–0.84) and 0.80 (0.77–0.82), respectively. PRSnon‐APOE showed an association with AD risk similar to PRSAPOE. PRSAPOE, but not PRSnon‐APOE, was associated with reduced plasma Aβ42/Aβ40 ratio and increased Neurofilament light chain (NfL) (p < 0.05). DISCUSSION: The PRS with and without APOE gene, in combination with demographics, shows good discriminative and predictive ability for AD. The AD‐related pathologies underlie AD risk associated with PRSAPOE. Highlights: The PRSAPOE and PRSnon‐APOE were associated with AD risk in the Chinese population.The PRSAPOE and PRSnon‐APOE, in combination with demographics, showed good discriminative and predictive ability for AD.The AD‐related pathologies underlie the AD risk associated with PRSAPOE but not PRSnon‐APOE. [ABSTRACT FROM AUTHOR]

Over the past 20 years, drive-by monitoring, using data from sensors in passing vehicles, has become increasingly popular due to its low cost and high efficiency at a network level. Despite significant advances, the dynamics of vehicles remains one of the main challenges to overcome before accurately identifying critical information about the infrastructure being crossed, bridges or road pavements. This paper introduces a novel approach to estimating bridge profiles (i.e. road surface profiles on the bridges) and vehicle properties using a fleet of passing vehicles. In this method, bridge profiles are first calculated using the innovative Inverse Newmark-beta integration method, and the cross-entropy optimization algorithm is employed to solve the problem. Numerical results demonstrate that the proposed approach is highly effective in extracting bridge profiles and predicting vehicle properties, even in scenarios with significant levels of measurement noise. [ABSTRACT FROM AUTHOR]

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Linear combinations of DNN unit activities are widely used to build predictive models of neuronal activity in the visual cortex. Nevertheless, prediction performance in these models is often investigated on stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a generalization gap in how predicting neuronal responses to synthetically generated out-of-distribution (OOD) stimuli. Here, we investigated how the recent progress in improving DNNs' object recognition generalization, as well as various DNN design choices such as architecture, learning algorithm, and datasets have impacted the generalization gap in neural predictivity. We came to a surprising conclusion that the performance on none of the common computer vision OOD object recognition benchmarks is predictive of OOD neural predictivity performance. Furthermore, we found that adversarially robust models often yield substantially higher generalization in neural predictivity, although the degree of robustness itself was not predictive of neural predictivity score. These results suggest that improving object recognition behavior on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex. Author summary: Inspired by the neural circuits of the brain, deep neural networks (DNN) have been steadily improving in their ability to perform foundational visual tasks such as object recognition. Whereas, early models struggled with generalization to abstract visual domains such as line drawings and cartoons, recent advancement have approached near-human recognition capabilities. Moreover, the unit activity in these networks exhibit strong similarities with the activity of single-unit recordings along the primate ventral visual cortex. This capability of DNNs has provided visual neuroscientists with precise models for exploring the neural underpinnings of object recognition. Our research probes whether enhancements in neural networks' recognition of out-of-distribution objects correlate with improved predictability of brain activity in the visual cortex of monkeys to synthetic stimuli. We found that the out of distribution object recognition performance on natural image datasets is not a reliable measure of neural predictivity. However, DNN models that were trained to be more resilient to adversarially generated noise patterns as well as DNN ensembles, consistently yielded better generalization in neural predictivity. Altogether, our results suggest that improving object recognition behaviour on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex. [ABSTRACT FROM AUTHOR]

Purpose: Construct an exercise intervention program for patients with sarcopenic obesity. Material and Methods: Based on the COM-B theoretical model and evidence-based principles, the program was constructed using qualitative methods of literature analysis and Delphi method. The Delphi panel consisted of 15 experts from the fields of clinical medicine, rehabilitation medicine, medical technology, and nursing. Results: Fifteen experts were consulted, and the consultation recovery rate was 100%; the authority coefficient of the 1st round was 0.83, with coefficients of variation ranging from 0.00 to 0.27, and importance scores ranging from (4.13± 1.13) to (5± 0); the authority coefficient of the 2nd round was 0.82, with coefficients of variation ranging from 0.00 to 0.20, and importance scores ranging from (4.53± 0.64) to (5± 0); Kendall's harmony coefficient was 0.102, 0.115, respectively, and the differences were statistically significant(P < 0.05). The constructed exercise intervention program for patients with sarcopenic obesity included 4 primary indicators, 12 secondary indicators, and 28 tertiary indicators. Conclusion: The constructed exercise intervention program for patients with sarcopenic obesity is scientific, feasible and generalizable, and can provide useful reference for related personnel to develop exercise programs for patients with sarcopenic obesity. [ABSTRACT FROM AUTHOR]

INTRODUCTION: To examine the burden and clusters of multimorbidity in association with mild cognitive impairment (MCI), dementia, and Alzheimer's disease (AD)‐related plasma biomarkers among older adults. METHODS: This population‐based study included 5432 participants (age ≥60 years); of these, plasma amyloid beta (Aβ), total tau, and neurofilament light chain (NfL) were measured in a subsample (n = 1412). We used hierarchical clustering to generate five multimorbidity clusters from 23 chronic diseases. We diagnosed dementia and MCI following international criteria. Data were analyzed using logistic and linear regression models. RESULTS: The number of chronic diseases was associated with dementia (multivariable‐adjusted odds ratio = 1.22; 95% confidence interval [CI] = 1.11 to 1.33), AD (1.13; 1.01 to 1.26), vascular dementia (VaD) (1.44; 1.25 to 1.64), and non‐amnestic MCI (1.25; 1.13 to 1.37). Metabolic cluster was associated with VaD and non‐amnestic MCI, whereas degenerative ocular cluster was associated with AD (p < 0.05). The number of chronic diseases was associated with increased plasma Aβ and NfL (p < 0.05). DISCUSSION: Multimorbidity burden and clusters are differentially associated with subtypes of dementia and MCI and AD‐related plasma biomarkers in older adults. Highlights: We used hierarchical clustering to generate five clusters of multimorbidity.The presence and load of multimorbidity were associated with dementia and mild cognitive impairment.Multimorbidity clusters were differentially associated with subtypes of dementia and Alzheimer's disease plasma biomarkers. [ABSTRACT FROM AUTHOR]