Explore the vast range of titles available.

In order to alleviate the pressure on environmental resources faced by meat and dairy production and to satisfy the increasing demands of consumers for food safety and health, alternative proteins have drawn considerable attention in the food industry. However, despite the successive reports of alternative protein food, the processing and application foundation of alternative proteins for meat and dairy is still weak. This paper summarizes the nutritional composition and physicochemical characteristics of meat and dairy alternative proteins from four sources: plant proteins, fungal proteins, algal proteins and insect proteins. The difference between these alternative proteins to animal proteins, the effects of their structural features and environmental conditions on their properties, as well as the corresponding mechanism are compared and discussed. Though fungal proteins, algal proteins and insect proteins have shown some advantages over traditional plant proteins, such as the comparable protein content of insect proteins to meat, the better digestibility of fungal proteins and the better foaming properties of algal proteins, there is still a big gap between alternative proteins and meat and dairy proteins. In addition to needing to provide amino acid composition and digestibility similar to animal proteins, alternative proteins also face challenges such as maintaining good solubility and emulsion properties. Their nutritional and physicochemical properties still need thorough investigation, and for commercial application, it is important to develop and optimize industrial technology in alternative protein separation and modification.

LncRNAs are involved in modulating the individual risk and the severity of progression in metabolic dysfunction-associated fatty liver disease (MASLD), but their precise roles remain largely unknown. This study aimed to investigate the role of lncRNA Snhg3 in the development and progression of MASLD, along with the underlying mechanisms. The result showed that Snhg3 was significantly downregulated in the liver of high-fat diet-induced obesity (DIO) mice. Notably, palmitic acid promoted the expression of Snhg3 and overexpression of Snhg3 increased lipid accumulation in primary hepatocytes. Furthermore, hepatocyte-specific Snhg3 deficiency decreased body and liver weight, alleviated hepatic steatosis and promoted hepatic fatty acid metabolism in DIO mice, whereas overexpression induced the opposite effect. Mechanistically, Snhg3 promoted the expression, stability and nuclear localization of SND1 protein via interacting with SND1, thereby inducing K63-linked ubiquitination modification of SND1. Moreover, Snhg3 decreased the H3K27me3 level and induced SND1-mediated chromatin loose remodeling, thus reducing H3K27me3 enrichment at the Pparg promoter and enhancing PPARγ expression. The administration of PPARγ antagonist T0070907 improved Snhg3 -aggravated hepatic steatosis. Our study revealed a new signaling pathway, Snhg3 /SND1/H3K27me3/PPARγ, responsible for mice MASLD and indicates that lncRNA-mediated epigenetic modification has a crucial role in the pathology of MASLD.

Considering the unique interplay of trams with road traffic, this study explored the issue of instability in tram operations—a prominent medium-capacity rail transit. Our goal was to design a timetable slack time optimization method for scheduling slack time to improve the stability of tram operations. To facilitate this, we derived the travel/dwelling time distribution from historical data, which assisted in estimating interference times and evaluating the requisite slack time. We then developed an integer programming model to calculate both the punctuality rate and expected delay under varying travel times, enabling the creation of alternative slack time schemes. Using a unique tram operation simulation logic, we assessed the operational efficiency and reliability of these alternate schemes based on specific operational indicators. The results suggest that our novel approach to timetable optimization significantly enhances the tram’s adaptability to disruptions, directly improving the passenger experience and tram competitiveness. This work offers a robust framework for timetable optimization for semi-independent right-of-way public transportation.

Background Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. Methods A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. Results The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. Conclusions In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

Transient Marangoni convection induced by an isothermal sidewall of a rectangular pool under a zero-gravity condition is studied using scaling analysis. Scaling analysis shows that there exist a number of flow regimes in each evolution scenario, depending on the Marangoni number, the Prandtl number and the aspect ratio. In a typical evolution scenario, a horizontal surface flow and a vertical flow adjacent to the sidewall may appear. Additionally, a number of scaling laws of the velocity and thickness of transient Marangoni convection are obtained. Further, numerical simulation is performed for validation of the selected scaling laws. There exits good agreement between the numerical results and the scaling predictions.

Washing machines are one of the tools that bring great convenience to people’s daily lives. However, washing machines that have been used for a long time often develop issues such as odor and mold, which can pose health hazards to consumers. There exists a conspicuous gap in our understanding of the microorganisms that inhabit the inner workings of washing machines. In this study, samples were collected from 22 washing machines in Shanghai, China, including both water eluted from different parts of washing machines and biofilms. Quantitative qualitative analysis was performed using fluorescence PCR quantification, and microbial communities were characterized by high-throughput sequencing (HTS). This showed that the microbial communities in all samples were predominantly composed of bacteria. HTS results showed that in the eluted water samples, the bacteria mainly included Pseudomonas, Enhydrobacter, Brevibacterium, and Acinetobacter. Conversely, in the biofilm samples, Enhydrobacter and Brevibacterium were the predominant bacterial microorganisms. Correlation analysis results revealed that microbial colonies in washing machines were significantly correlated with years of use and the type of detergent used to clean the washing machine. As numerous pathogenic microorganisms can be observed in the results, effective preventive measures and future research are essential to mitigate these health problems and ensure the continued safe use of these household appliances.

The transfer city is a key point in air-to-rail intermodal travel (ARIT) that directly influences the service level of the entire system. Although some studies have investigated factors that influence passengers’ ARIT preferences based on subjective surveys, an in-depth understanding of their nonlinear and interactive impacts on passengers’ actual behavior is still lacking. Using passengers’ online booking data in China, this study implements an interpretable ensemble machine learning framework that incorporates decision-making theory to unveil feature importance and the complex nonlinear and interactive effects of various attributes on passengers’ choice of transfer city in the crucial ARIT scenario. The results show that (1) the extreme gradient boosting (XGBoost) model achieves better performance in predicting ARIT transfer city choice than the conventional discrete choice model; (2) attributes related to intermodal services (e.g., ticket price, in-vehicle duration, transfer duration, quantities of flights and trains) are more important than personal demographic characteristics (e.g., age and gender); (3) factors related to service economy and efficiency display nonlinear impacts with fluctuations and critical thresholds; and (4) individuals with different characteristics present heterogeneous preferences for ARIT transfer cities. These findings can provide useful managerial implications for policymakers.

Bénard–Marangoni convection in an open cavity has attracted much attention in the past century. In most of the previous works, liquids with Prandtl numbers larger than unity were used to study in this issue. However, the Bénard–Marangoni convection with liquids at Prandtl numbers lower than unity is still unclear. In this study, Bénard–Marangoni convection in an open cavity with liquids at Prandtl numbers lower than unity in zero-gravity conditions is investigated to reveal the bifurcations of the flow and quantify the heat and mass transfer. Three-dimensional direct numerical simulation is conducted by the finite-volume method with a SIMPLE scheme for the pressure–velocity coupling. The bottom boundary is nonslip and isothermal heated. The top boundary is assumed to be flat, cooled by air and opposed by the Marangoni stress. Numerical simulation is conducted for a wide range of Marangoni numbers (Ma) from 5.0 × 101 to 4.0 × 104 and different Prandtl numbers (Pr) of 0.011, 0.029, and 0.063. Generally, for small Ma, the liquid metal in the cavity is dominated by conduction, and there is no convection. The critical Marangoni number for liquids with Prandtl numbers lower than unity equals those with Prandtl numbers larger than unity, but the cells are different. As Ma increases further, the cells pattern becomes irregular and the structure of the top surface of the cells becomes finer. The thermal boundary layer becomes thinner, and the column of velocity magnitudes in the middle slice of the fluid is denser, indicating a stronger convection with higher Marangoni numbers. A new scaling is found for the area-weighted mean velocity magnitude at the top boundary of um~Ma Pr−2/3, which means the mass transfer may be enhanced by high Marangoni numbers and low Prandtl numbers. The Nusselt number is approximately constant for Ma ≤ 400 but increases slowly for Ma > 400, indicating that the heat transfer may be enhanced by increasing the Marangoni number.

The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

Microstructured surfaces with pillar arrays are widely used to control the wetting morphology and spreading dynamics of droplets. In both simulations and experiments, it is shown that fabricating the surface with various microstructures is a very effective method for achieving the desired symmetry of the moving contact line. However, the method for characterizing miscellaneous pillar-arrayed microstructured surfaces is still insufficient. This paper presents the configurational entropy to characterize the microstructured surfaces with pillar arrays. By calculating the configurational entropy of pillar-arrayed microstructured surfaces, the relationship between the configurational entropy and the wetting morphology of droplets is obtained. For pillar-arrayed microstructured surfaces with the configurational entropy S > 0, the droplet wetting morphology may be much more complex than those with S = 0. The relationship is found to be consistent with the previous results. Furthermore, the wetting dynamics has been analysed. This study may be useful to understand the mechanism of droplet wetting on pillar-arrayed microstructured surfaces and provide insights for the design and manufacture of microstructured surfaces.