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This paper presents a deep learning-aided deterioration modeling approach for highway bridge components (i.e., decks, superstructures, and substructures). The method trains a set of deep learning models for the maximum likelihood estimation of parameters in a Markov chain. The likelihood function is based on observed numbers of transitions between different condition states. The deep learning is leveraged for efficient representation of various factors that influence the deterioration process. Aided by deep learning, the proposed method is suitable for deterioration modeling using large-scale and high-dimensional bridge datasets. The study demonstrates an application of this approach using the historical National Bridge Inventory database from 1993 to 2019. The Convolutional Neural Network is adopted as the deep learning model. The proposed method is validated with ten-fold cross-validation that encompasses a nationwide selection of 88,596, 101,414, and 96,244 bridge decks, superstructures, and substructures, respectively. The validation shows this approach achieved a robust and low prediction error with the maximum mean-squared-error near 0.5 in a 26-years-forecast. The proposed method is also compared with the conventional Artificial Neural Network and four selected Markov-chain based deterioration modeling approaches. The study shows this approach can be a promising data-driven tool for deterioration modeling of highway bridge components. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to demonstrate the theoretical relationship between the layout of four-sensor dynamic acoustic array tracking system and systematic observation accuracy, and provide an algorithm to determine the optimal arrangement of four-sensor acoustic array and an indicator to evaluate acoustic array system measurement accuracy. Design/methodology/approach – In the present paper, the measurement principle of the four-sensor dynamic acoustic array tracking system is analyzed, and the system observation model and the conversion relationship between models are established. Subsequently, the optimization algorithm for the four-sensor dynamic acoustic array is deduced, the theoretical optimal arrangement of the four-sensor dynamic acoustic array tracking measurement system is obtained based on the optimal position dilution of precision function (PDOPF) of 2D target, and the static experimental study on sound-source bearing estimation is designed. The theoretical results are compared with the experimental results of the present study. Findings – The measurement accuracy of the four-sensor dynamic acoustic array tracking system is largely dependent on the layout of the acoustic sensor. Theoretical studies and experimental results demonstrated that an optimal PDPOF can be used to analyze the rationality of the layout. It can also serve as an indicator for the layout of the four-sensor dynamic acoustic array tracking system. Originality/value – The PDOPF value is presented as an indicator for the evaluation of the four-sensor dynamic acoustic array systematic observation accuracy based on theoretical analysis. The feasibility of the indicator and the rationality of the sensor layout in practical engineering application are verified through experimental studies on sound-source bearing estimation. The higher the PDOPF value is, the lower the accuracy of the system will be. [ABSTRACT FROM AUTHOR]

Background: Both lipid metabolism and obesity are crucial factors in osteoporosis, influencing the relevance of the cardiometabolic index (CMI), a new body fat index incorporating obesity and lipid metrics. Our study aims to explore the relationship between CMI and lumbar spine bone mineral density (BMD) and the mediating role of body fat percentage., Methods: Utilizing the National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2018, we conducted a cross-sectional analysis. We employed multiple linear regression models, subgroup analyses, generalized additive models (GAM), smooth curve fitting, and mediation analysis to evaluate the linear and nonlinear relationships between CMI and lumbar spine BMD., Results: The study involved 5,124 participants with an average lumbar spine BMD of 1.03 ± 0.15 g/cm 2 . We identified a negative correlation between CMI and lumbar spine BMD (β = -0.015; 95% CI: -0.023, -0.008). Nonlinear associations were evident, with inflection points at CMI values of 1.12 and 2.86. Subgroup analyses showed consistent negative correlations across all categories without significant differences (p for interaction > 0.05). Moreover, body fat percentage negatively correlated with BMD (β = -0.005; 95% CI: -0.006, -0.004) and mediated 9.41% of the relationship between CMI and BMD., Conclusion: Increased CMI levels are associated with lower lumbar spine BMD, with body fat percentage significantly mediating this relationship. This underscores the importance of managing body composition in the context of bone health, highlighting CMI's potential utility in osteoporosis risk assessment., (© 2024. The Author(s).)

Background: Serum prostate-specific antigen (PSA) is a primary metric for diagnosis and prognosis of prostate cancer (PCa). Exposure to heavy metals, such as lead, cadmium, mercury, and zinc can impact PSA levels in PCa patients. However, it is unclear whether this effect also occurs in men without PCa, which may lead to the overdiagnosis of PCa., Method: Data on a total of 5089 American men who had never been diagnosed with PCa were obtained from the National Health and Nutrition Examination Survey performed from 2003-2010. The relationship between serum PSA levels (dependent variable) and concentrations of lead (μmol/L), cadmium (nmol/L), and mercury (μmol/L) were investigated with dietary zinc intake being used as a potential modifier or covariate in a weighted linear regression model and a generalized additive model. A series of bootstrapping analyses were performed to evaluate sensitivity and specificity using these models., Results: Regression analyses suggested that, in general, lead, cadmium, or mercury did not show an association with PSA levels, which was consistent with the results of the bootstrapping analyses. However, in a subgroup of participants with a high level of dietary zinc intake (≥14.12 mg/day), a significant positive association between cadmium and serum PSA was identified (1.06, 95% CI, P = 0.0268, P for interaction=0.0249)., Conclusions: With high-level zinc intake, serum PSA levels may rise in PCa-free men as the exposure to cadmium increases, leading to a potential risk of an overdiagnosis of PCa and unnecessary treatment. Therefore, environmental variables should be factored in the current diagnostic model for PCa that is solely based on PSA measurements. Different criteria for PSA screening are necessary based on geographical variables. Further investigations are needed to uncover the biological and biochemical relationship between zinc, cadmium, and serum PSA levels to more precisely diagnose PCa., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Importance: Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes., Objective: To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer., Design, Setting, and Participants: This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children's Hospital (n = 31), Lucile Packard Children's Hospital at Stanford University (n = 80), CHOC Children's Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017., Exposures: Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant's tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient's tumor. Results of the UCSC analysis were presented to clinical partners., Main Outcomes and Measures: Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information., Results: Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq-derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%)., Conclusions and Relevance: This study's findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.