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Abstract As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Abstract Type 2 diabetes mellitus combined with metabolic dysfunction-associated steatotic liver disease (MASLD) leads to an increasing incidence of liver injury year by year, and patients are at a significantly higher risk of developing cirrhosis or even liver failure. No drugs have emerged to specifically treat this disease. The aim of this study is to investigate the mechanisms and causative hub genes of type 2 diabetes combined with MASLD. The data were obtained through the GEO platform for bioinformatics analysis and validated by in vitro experiments to find the causative targets of type 2 diabetes mellitus combined with MASLD, which will provide some theoretical basis for the development of future therapeutic drugs. GSE23343 and GSE49541 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) in type 2 diabetes mellitus combined with MASLD for functional enrichment analysis. And STRING database and Cytoscape software were used to construct Protein–Protein Interaction (PPI) and hub gene networks. And GO (gene ontology, GO) analysis and KEGG (Kyoto encyclopedia of genes and genomes, KEGG) enrichment analysis were performed on target genes. A total of 185 co-expressed DEGs were obtained by differential analysis, and 20 key genes involved in the development and progression of type 2 diabetes were finally screened. These 20 key genes were involved in 529 GO enrichment results and 20 KEGG enrichment results, and were mainly associated with ECM–receptor interaction, Focal adhesion, Human papillomavirus infection, PI3K-Akt signaling pathway, and the Toll-like receptor signaling pathway. A total of two target genes (SPP1, collagen IV) were found to be highly correlated with type 2 diabetes mellitus combined with MASLD. Real time PCR results showed that there was a significant difference in SPP1 and collagen IV mRNA expression among the three groups (P

AIM: To observe the imaging characteristics of the affected eyes of patients with central serous chorioretinopathy(CSC)of different ages and their asymptomatic fellow eyes.METHODS: Retrospective study. A total of 76 cases(88 eyes)of CSC patients diagnosed in the ophthalmology department of our hospital from April to September, 2023 and 35 cases(35 eyes of asymptomatic fellow eyes of patients with unilateral CSC)were selected for the study. According to age, they were divided into young and middle-aged groups(50 years old). The imaging features of the affected eyes of CSC patients of different ages and their asymptomatic fellow eyes were observed.RESULTS: The subfoveal choroidal thickness(SFCT)of CSC eyes in the young and middle-aged patients(487.30±83.33 μm)was significantly greater than that of the middle-aged group(414.17±96.02 μm, P

Abstract Background Food systems instantiate the complex interdependencies across humans, physical environments, and other organisms. Applying One Health approaches for agri-food system transformation, which adopts integrated and unifying approaches to optimize the overall health of humans, animals, plants, and environments, is crucial to enhance the sustainability of food systems. This study develops a potential assessment tool, named the global One Health index-Food Security (GOHI-FS), aiming to evaluate food security performance across countries/territories from One Health perspective and identify relevant gaps that need to be improved for sustainable food systems. Methods We comprehensively reviewed existing frameworks and elements of food security. The indicator framework of GOHI-FS was conceptualized following the structure-process-outcome model and confirmed by expert advisory. Publicly available data in 2020 was collected for each indicator. The weighting strategy was determined by the Fuzzy Analytical Hierarchy Process. The data for each indicator was normalized and aggregated by weighted arithmetic mean. Linear regressions were performed to evaluate the associations of GOHI-FS with health and social-economic indicators. Results The GOHI-FS includes 5 first-level indicators, 19 second-level indicators and 45 third-level indicators. There were 146 countries/territories enrolled for evaluation. The highest average score of first-level indicators was Nutrition (69.8) and the lowest was Government Support and Response (31.3). There was regional heterogeneity of GOHI-FS scores. Higher median scores with interquartile range (IQR) were shown in North America (median: 76.1, IQR: 75.5–76.7), followed by Europe and Central Asia (median: 66.9, IQR: 60.1–74.3), East Asia and the Pacific (median: 60.6, IQR: 55.5–68.7), Latin America and the Caribbean (median: 60.2, IQR: 57.8–65.0), Middle East and North Africa (median: 56.6, IQR: 52.0–62.8), South Asia (median: 51.1, IQR: 46.7–53.8), and sub-Saharan Africa (median: 41.4, IQR: 37.2–46.5). We also found significant associations between GOHI-FS and GDP per capita, socio-demographic index, health expenditure and life expectancy. Conclusions GOHI-FS is a potential assessment tool to understand the gaps in food security across countries/territories under the One Health concept. The pilot findings suggest notable gaps for sub-Saharan Africa in numerous aspects. Broad actions are needed globally to promote government support and response for food security.

Activation of brown adipose tissue (BAT) contributes to energy dissipation and metabolic health. Although mineralocorticoid receptor (MR) antagonists have been demonstrated to improve metabolism under obesity, the underlying mechanisms remain incompletely understood. We aimed to evaluate the role of BAT MR in metabolic regulation. After 8 weeks of high-fat diet (HFD) feeding, BAT MR KO (BMRKO) mice manifested significantly increased bodyweight, fat mass, serum fasting glucose, and impaired glucose homeostasis compared with littermate control (LC) mice, although insulin resistance and fasting serum insulin were not significantly changed. Metabolic cage experiments showed no change in O2 consumption, CO2 production, or energy expenditure in obese BMRKO mice. RNA sequencing analysis revealed downregulation of genes related to fatty acid metabolism in BAT of BMRKO-HFD mice compared with LC-HFD mice. Moreover, H&E and immunohistochemical staining demonstrated that BMRKO exacerbated HFD-induced macrophage infiltration and proinflammatory genes in epididymal white adipose tissue (eWAT). BMRKO-HFD mice also manifested significantly increased liver weights and hepatic lipid accumulation, an increasing trend of genes related to lipogenesis and lipid uptake, and significantly decreased genes related to lipolytic and fatty acid oxidation in the liver. Finally, the level of insulin-induced AKT phosphorylation was substantially blunted in eWAT but not liver or skeletal muscle of BMRKO-HFD mice compared with LC-HFD mice. These data suggest that BAT MR is required to maintain metabolic homeostasis, likely through its regulation of fatty acid metabolism in BAT and impacts on eWAT and liver.

When judging the optical properties of a translucent object, humans often look at sharp geometric features such as edges and thin parts. An analysis of the physics of light transport shows that these sharp geometries are necessary for scientific imaging systems to be able to accurately measure the underlying material optical properties. In this article, we examine whether human perception of translucency is likewise affected by the presence of sharp geometry, by confounding our perceptual inferences about an object's optical properties. We use physically accurate simulations to create visual stimuli of translucent materials with varying shapes and optical properties under different illuminations. We then use these stimuli in psychophysical experiments, where human observers are asked to match an image of a target object by adjusting the material parameters of a match object with different geometric sharpness, lighting, and three-dimensional geometry. We find that the level of geometric sharpness significantly affects perceived translucency by observers. These findings generalize across a few illumination conditions and object shapes. Our results suggest that the perceived translucency of an object depends on both the underlying material's optical parameters and the three-dimensional shape of the object. We also find that models based on image contrast cannot fully predict the perceptual results.

IntroductionLow temperature is the most common method used to maintain the freshness of Phlebopus portentosus during long-distance transportation. However, there is no information regarding the nutritional changes that occur in P. portentosus preserved postharvest in low temperature.MethodsIn this study, the changes in flavor quality and bioactive components in fruiting bodies stored at 4 °C for different storage periods were determined through LC/MS and GC/MS analyses. Sampling was performed at 0, 3, 5, 7, and 13 days storage.Results and DiscussionBased on the results, the metabolites present in caps and stipes were different at the same period and significantly different after 7 days of storage. A total of 583 and 500 different metabolites were detected in caps and stipes, respectively, and were mainly lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds and others. Except for prenol lipids and nucleotides, the expression levels of most metabolites increased with longer storage time. In addition, geosmin was identified as the major contributor to earthy-musty odors, and the level of geosmin was increased when the storage time was short.ConclusionThe variations in these metabolites might cause changes in flavor quality and bioactive components in P. portentosus. Variations in these metabolites were thoroughly analyzed, and the results revealed how storage processes affect the postharvest quality of P. portentosus for the first time.

BackgroundCytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) is the standard treatment for patients with peritoneal cancer (PC). Following CRS-HIPEC, patients may also face risks caused by whole body hyperthermia. This study analyzed the incidence of temperature increases following CRS-HIPEC and identified the attendant risk factors.MethodsA retrospective analysis was carried out among 458 patients who received CRS-HIPEC at the Fourth Hospital of Hebei Medical University between August 2018 and January 2021. The patients were divided into two groups according to post-HIPEC axillary temperature (≥38°C), with the demographics and the laboratory test results subsequently analyzed and compared, and the risk factors pertaining to temperature increases analyzed using univariate and multivariate logistic regression.ResultsDuring CRS-HIPEC, 32.5% (149/458) of the patients with a temperature increase had an axillary temperature of not lower than 38°C, and 8.5% (39/458) of the patients with hyperpyrexia had an axillary temperature of not lower than 39°C. Female gender, gynecological malignancies, type of chemotherapy drug, increased postoperative neutrophil percentage, and a sharp drop in postoperative prealbumin were associated with the incidence of a temperature increase and axillary temperatures of >38°C. Among these factors, the type of chemotherapy drug was identified as an independent risk factor for a temperature increase during CRS-HIPEC.ConclusionBy determining the risk factors pertaining to temperature increases during CRS-HIPEC, medical staff can identify the attendant risks among the patients and thus take preventive measures in a timely manner to maintain the patient’s body temperature at a stable level. This suggests that further clinical research should be conducted to build a risk-prediction model for temperature increases following CRS-HIPEC.

We can visually discriminate and recognize a wide range of materials. Meanwhile, we use language to describe what we see and communicate relevant information about the materials. Here, we investigate the relationship between visual judgment and language expression to understand how visual features relate to semantic representations in human cognition. We use deep generative models to generate images of realistic materials. Interpolating between the generative models enables us to systematically create material appearances in both well-defined and ambiguous categories. Using these stimuli, we compared the representations of materials from two behavioral tasks: visual material similarity judgments and free-form verbal descriptions. Our findings reveal a moderate but significant correlation between vision and language on a categorical level. However, analyzing the representations with an unsupervised alignment method, we discover structural differences that arise at the image-to-image level, especially among ambiguous materials morphed between known categories. Moreover, visual judgments exhibit more individual differences compared to verbal descriptions. Our results show that while verbal descriptions capture material qualities on the coarse level, they may not fully convey the visual nuances of material appearances. Analyzing the image representation of materials obtained from various pre-trained deep neural networks, we find that similarity structures in human visual judgments align more closely with those of the vision-language models than purely vision-based models. Our work illustrates the need to consider the vision-language relationship in building a comprehensive model for material perception. Moreover, we propose a novel framework for evaluating the alignment and misalignment between representations from different modalities, leveraging information from human behaviors and computational models. Author summary: Materials are building blocks of our environment, granting access to a wide array of visual experiences. The immense diversity, complexity, and versatility of materials present challenges in verbal articulation. To what extent can words convey the richness of visual material perception? What are the salient attributes for communicating about materials? We address these questions by measuring both visual material similarity judgments and free-form verbal descriptions. We use AI models to create a diverse array of plausible visual appearances of familiar and unfamiliar materials. Our findings reveal a moderate vision-language correlation within individual participants, yet a notable discrepancy persists between the two modalities. While verbal descriptions capture material qualities at a coarse categorical level, precise alignment between vision and language at the individual stimulus level is still lacking. These results highlight that visual representations of materials are richer than verbalized semantic features, underscoring the differential roles of language and vision in perception. Lastly, we discover that deep neural networks pre-trained on large-scale datasets can predict human visual similarities at a coarse level, suggesting the general visual representations learned by these networks carry perceptually relevant information for material-relevant tasks. [ABSTRACT FROM AUTHOR]

The development of highly active and cost-effective electrocatalysts, such as transition-metal oxides and (oxy)-hydroxides, for the oxygen evolution reaction (OER) is of great importance in water-splitting electrolyzers. Building nanohybrids is an effective strategy to improve the OER activity of transition-metal oxides and (oxy)-hydroxides. In this work, we present a feather-like CoFe-(oxy)-hydroxide@Co3O4-CeO2 3D hierarchical heterostructure on nickel foam (NF) by coupling amorphous CoFe-(oxy)-hydroxide on Co3O4-CeO2 hybrid nanorods for enhanced OER performance. Owing to the synergistic effect and interface engineering of CoFe-(oxy)-hydroxide, Co3O4, and CeO2, as well as the large electrochemically active surface area with enhanced exposed active sites, the as-prepared CoFe-(oxy)-hydroxide@Co3O4-CeO2 electrode exhibits superior activity with a required overpotential of 0.228 V to achieve 10 mA cm–2 in 1.0 M KOH, which is among the best values for Co3O4-based and CoFe-based OER electrocatalysts. [ABSTRACT FROM AUTHOR]

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information. [ABSTRACT FROM AUTHOR]

Aphids are sap‐feeding plant pests that depend on their symbiotic relationships with the primary endosymbiont Buchnera aphidicola to adapt to impoverished diets. However, how the host plant affects the aphid primary symbiont and aphid adaptation to host plant transfer are poorly known. In this study, aphid symbiont screening and genotype identification were used to establish 2 aphid strains (Rhopalosiphum maidis [Rm] and Rhopalosiphum padi [Rp] strains) containing only Buchnera without any secondary symbionts for both wheat aphid species (R. maidis and R. padi). Aphid fitness and Buchnera titers were unstable on some of these host plants after transferring to novel host plants (G1–G5), which were influenced by host plant species and generations; however, they stabilized after prolonged feeding on the same plants for 10 generations. The electropenetrography (EPG) records showed that the allocation of aphid feeding time was significantly distinct in the 6 host plants; aphids had more intracellular punctures and spent more nonprobing time on green bristlegrass which was not conducive to its growth compared with other plants. The content of soluble sugar, soluble protein, and amino acid in the leaves of the 6 host plants were also clearly separated. The correlation coefficient analysis showed that the nutrient contents of host plants had significant correlations with aphid feeding behaviors, fitness, and Buchnera titers. In the meantime, aphid fitness, and Buchnera titers were also affected by aphid feeding behaviors. Also, Buchnera titers of aphid natural populations on 6 host plants showed a visible difference. Our study deepened our understanding of the interaction among aphids, endosymbionts, and host plants, indicating that the host plant nutrient content is a predominant factor affecting aphid adaptation to their diet, initially affecting aphid feeding behaviors, and further affecting aphid fitness and Buchnera titers, which would further contribute to exploiting new available strategies for aphid control. [ABSTRACT FROM AUTHOR]

Profiling intestinal gases and their responses to dietary changes can reveal the products and functions of the gut microbiota and their influence on human health. High-fat foods (HFF) can alter the gut microbiota and its metabolites, posing a potential health risk. However, little is known about the effects of HFF on intestinal gas distribution. Therefore, in this study, we used human fecal microorganisms as strains, an in vitro three-chamber colon model and an intestinal gas array sensor as tools. We performed in vitro fermentation using HFF as the fermentation substrate to reveal the effects of HFF on the kinetics of intestinal gas production and changes in the gut microbiota and its metabolites. We found that dietary fatty acids stimulated the production of H2S and volatile organic compounds in the colon, promoted Firmicutes abundance, and decreased Bacteroidetes abundance. These results highlight the potential role of HFF in altering the gut microbiota and intestinal gas, which can lead to health hazards.