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The Enshi black pig is a Chinese native breed known for its rich nutrition content and high quality, which has notable traction in the consumer market. In this study, the potential impact of the main commercial cuts from Enshi black pork carcasses (ham, loin, and belly) on the bacteria community of spoiled meat under refrigerated storage (4 °C) was assessed by using a high-throughput sequencing method. Moreover, the spoilage potential of isolated strains from spoiled pork was investigated. The results demonstrated significant differences (p < 0.05) in bacterial community diversity and composition among spoiled ham, loin, and belly samples. Linear discriminant analysis effect size (LEfSe) analysis revealed a total of 20 significantly different potential bacterial biomarkers, with the dominant genera of Pseudomonas, Psychrobacter, Shewanella and Carnobacterium. Additionally, C. divergens THT1-5, isolated from spoiled ham samples, displayed cold adaptation and higher spoilage potential in Enshi black pork. These findings are helpful for identifying key factors contributing to spoilage in Enshi black pork and developing strategies to inhibit bacterial growth during preservation.

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of “network structure” and “loss function”, and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of “nnUNet” network structure and “Cross-Entropy + Iou + Boundary” hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services.

The giant spin–orbit torque (SOT) generated by topological surface states in topological insulators (TIs) provides an energy-efficient writing method for magnetic memory. In this study, we demonstrate a topological insulator/spin valve (TI/SV) device that operates at room temperature. An ultrathin, high-quality TI (Bi0.5Sb0.5)2Te3 (BST) thin film is epitaxially grown as a functional layer on a (0001)-Al2O3 substrate via molecular beam epitaxy in ultrahigh vacuum. Subsequently, Ta/CoFe/Cu/CoFe/IrMn layers are grown on BST/Al2O3 thin films using magnetron sputtering to form TI/SV devices via a subsequent standard lithography process. The resulting TI/SV devices exhibit a giant magnetoresistance of up to ∼1.1% at room temperature. Additionally, a low switching current density of approximately 1.25 × 105 A cm−2 is achieved, which implies high potential for further reducing the energy consumption of SOT-based devices. The SOT conversion efficiency and charge-spin conversion efficiency of the TI layer are approximately 4.74 × 10−6 Oe A−1 cm2 and 1.33, respectively, as extracted from the SOT-induced shift of the magnetic switching field. Moreover, the switching current density reduces steadily with the device size scaling down. This study can facilitate the realization of energy-efficient magnetic memory devices in the future. [ABSTRACT FROM AUTHOR]

Notch high cycle fatigue (HCF) properties and microcrack initiation behavior of Ti-55531 alloy with a multilevel lamellar microstructure under various notch radii were systematically investigated. Results indicate that the reduction of notch root radius significantly promotes fatigue microcrack initiation, and then dramatically reduces the HCF life and strength of the alloy. Cyclic deformation of the alloy is mainly controlled by the slipping and deformation twinning in α plates. The primary fatigue crack initiation micro-mechanism is α/β interface cracking induced by slipping and twinning at all notch HCF specimens. Moreover, the volume fraction of twinning would increase with decreasing of the notch root radius. Interestingly, when the notch root radius is the smallest (R = 0.14 mm) and the stress concentration factor is the largest (Kt = 4), in addition to slipping and twinning, basal stacking faults promoting the cracking of α/β interface could be another crucial HCF microcrack initiation mechanism of the alloy. Furthermore, with decreasing of notch root radius and increasing of stress concentration factor, the size of cycle plastic deformation zone at notch root gradually reduces to the size of α colony and even the α plate. Therefore, the order of influential degree of three different levels microstructure on the crack initiation mechanism of notched HCF can be arranged as α plate > α colony > β GB.

Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites.