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The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17‐gene prognosis‐related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine‐learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C‐index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single‐cell RNA‐seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high‐risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC. [ABSTRACT FROM AUTHOR]

Simple Summary: Various studies have confirmed that the parity, days in milk (DIM), and somatic cell score (SCS) affected the fatty acid contents in milk and the Fourier-transform mid-infrared spectroscopy (FT-MIRS). However, the characteristics of fatty acid group contents predicted by FT-MIRS in Chinese Holstein cows have not been clearly demonstrated. This study employed FT-MIRS data to study the influencing factors and alterations in milk fatty acid contents in Chinese Holstein cows from large-scale dairy farms in Northern China. This study provided new ideas for the expansion of Dairy Herd Improvement laboratory determination indicators in China and the breeding of dairy cows with excellent milk quality. Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96–125 DIM, and SCFA and MCFA contents reached the highest at 276–305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids. [ABSTRACT FROM AUTHOR]

Simple Summary: Fourier Transform Infrared Spectroscopy (FT-MIRS) is widely used in milk quality detection, dairy herd improvement (DHI), and other fields. It is an economical, fast, accurate, and nondestructive batch tool for determining production performance phenotypes of dairy cows. Milk is one of the most important ways to provide the human body with the fatty acids it needs. There are a huge number of dairy cows in China. Therefore, it is possible to control fatty acid production in the milk source through targeted husbandry and breeding on large pastures to improve the quality of milk production. However, this work has not yet officially begun in China. In summary, our work uses FT-MIRS for the first time to study the phenotypic properties of milk fatty acid content and the genetic mechanism of its formation and to estimate genetic parameters. At the same time, SNPs significantly related to fatty acid content were discovered and the genes or adjacent genes had critical regulatory effects on milk fat synthesis, milk protein synthesis, adipocyte differentiation, mammary gland development, milk synthesis, and growth and development in dairy cows, thus providing a new perspective for cow genetic selection in China. Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for "Fixed and random model Circulating Probability Unification". The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids. [ABSTRACT FROM AUTHOR]

CF‐PDA‐M hybrid fillers are prepared by three metal cations (M) assisting polydopamine (PDA) through Fe3+, Ni2+, and Al3+. The metal cations promote the polymerization of PDA on the fiber surface, shorten the modification time of the fibers, and ensure that the short‐cut carbon fibers (CF) do not agglomerate during water bath stirring while keeping the structure of the CF undamaged, which is a green and efficient method. After PDA modification, the roughness and surface activity of the fiber surface increase. Finally, CF‐PDA‐M is used as a filler and added to neoprene latex and natural latex, which are prepared into composites by wet blending, and the CF are characterized by different techniques. The results show that the hydroxyl and amino groups on the surface of CF‐PDA‐M increase the cross‐linking density of the composites, establish a good stress cross‐linking network, shorten the vulcanization time, effectively prevent the agglomeration phenomenon of the CF in the rubber, and improve the dispersion of the CF in the composite. After modification, the tensile strength and 300% constant tensile strength of CF‐PDA‐M increase by more than 10% and 30%, respectively, over CF composites, and the rolling resistance is reduced. This study provides a new and effective strategy for CF surface functionalization, which improves the processing efficiency and mechanical properties of rubber products and has a broad application prospect in the rubber industry. [ABSTRACT FROM AUTHOR]

Reference methods for determining the contents of functional traits in camel milk are expensive and complex, hindering their widespread use. Mid‐infrared technology has the advantages of fast, economic, batch processing for dairy detection. In the present study, mid‐infrared spectroscopy (MIRS) prediction models previously established based on cow milk were used to predict 1101 Bactrian camel milk samples in terms of fat, protein, lactose, total solids, total casein, β‐casein, K, Ca, Na and Mg contents. The MIRS‐predicted values for the 10 traits were close to the reference values, demonstrating the utility and efficiency of the proposed method. Furthermore, the quality characteristics of camel milk were compared with those of other dairy animals, confirming that it is a valuable and rich source of essential nutrients for humans. [ABSTRACT FROM AUTHOR]

Mesoscale eddies are a common occurrence in the Kuroshio Extension (KE) that have a major impact on the levels of salinity and heat transport in the Northwest Pacific, the strength of the Kuroshio jet, and the fluctuations of the Kuroshio's trajectory. In this study, a purely data-driven machine learning model, Temporal Modes-Enhanced Neural Network (TMENN), is proposed to forecast the spatiotemporal variation of mesoscale eddies based on daily sea surface height (SSH) data over a 20-year period (2000–2019) in the Kuroshio Extension. To reduce computational costs and facilitate faster forecasting, raw SSH data are decomposed into spatial modes and temporal modes (principal components, PCs) by empirical orthogonal functions (EOF) analysis, and the first 117 PCs (a total of 8384 PCs), wherein the cumulative variance contribution rate reaches 95%, are selected solely as the predictors of TMENN to train and forecast. Forecasting reconstruction results show that the model can reliably forecast the evolution of the eddy in the KE for about 30 days. Additionally, three classical mesoscale eddy processes are selected to verify the accuracy of the model, namely cold eddy attachment, warm eddy shedding, and attachment, and the results indicate that the model can well capture the evolution process of mesoscale eddies. [ABSTRACT FROM AUTHOR]