KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine ß-Hydroxybutyrylation (Kbhb) modification sites.

Lysine ß-hydroxybutyrylation is an important post-translational modification (PTM) involved in various physiological and biological processes. In this research, we introduce a novel predictor KbhbXG, which utilizes XGBoost to identify ß-hydroxybutyrylation modification sites based on protein sequence information. The traditional experimental methods employed for the identification of ß-hydroxybutyrylated sites using proteomic techniques are both costly and time-consuming. Thus, the development of computational methods and predictors can play a crucial role in facilitating the rapid identification of ß-hydroxybutyrylation sites. Our proposed KbhbXG model first utilizes machine learning algorithm XGBoost to predict ß-hydroxybutyrylation modification sites. On the independent test set, KbhbXG achieves an accuracy of 0.7457, specificity of 0.7771, and an impressive area under the curve (AUC) score of 0.8172. The high AUC score achieved by our method demonstrates its potential for effectively identifying novel ß-hydroxybutyrylation sites, thereby facilitating further research and exploration of the ß-hydroxybutyrylation process. Also, functional analyses have revealed that different organisms preferentially engage in distinct biological processes and pathways, which can provide valuable insights for understanding the mechanism of ß-hydroxybutyrylation and guide experimental verification. To promote transparency and reproducibility, we have made both the codes and dataset of KbhbXG publicly available. Researchers interested in utilizing our proposed model can access these resources at https://github.com/Lab-Xu/KbhbXG.

Incentive strategy of safe and intelligent production in assembled steel plants from the perspective of evolutionary game.

Due to the numerous cross-operations and poor information communication, it is easy to cause production safety accidents in traditional assembled steel plants. The transformation and upgrading of smart production in the assembly steel plants is helpful to improve the efficiency of safety management. In order to effectively reduce the safety risks in the production of assembled steel components, this paper integrates policy incentives and safety supervision, constructs an evolutionary game model between the government and assembled steel producers, and analyzes the strategic evolution rules and stability conditions of stakeholders through the replication dynamics equation. Moreover, based on the empirical simulation of the Fuzhou X Steel Structure Plant project, the effectiveness of the evolutionary model incentive strategy setting is verified. The results show that whether an assembled steel plants adopt a smart management strategy or not is influenced by the government's incentive subsidy mechanism, penalty mechanism, the benefits and costs generated by traditional/ smart management, the probability and loss of safety accidents and other factors. The conclusion is important for upgrading the safety management mode, improving the safety production efficiency and constructing the safety supervision system of the assembled steel smart plant.

Polysaccharides improved the viscoelasticity, microstructure, and physical stability of ovalbumin-ferulic acid complex stabilized emulsion.

This study explored the mechanism underlying the interactions between polysaccharides and ovalbumin-ferulic acid (OVA-FA) and the effect of polysaccharides on OVA-FA-stabilized emulsions. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to study the polysaccharide OVA-FA interactions mechanism and to resolve the changes in the protein secondary structure and crystal structure. OVA-FA-polysaccharide-stabilized emulsions were studied using confocal laser scanning microscopy (CLSM), and their rheological properties and stability were determined. The results showed that the non-covalent interactions between polysaccharides and OVA-FA led to an increase in the ß-sheet content of OVA and a decrease in the α-helix and random coil contents. The stability of the OVA-FA-polysaccharide-stabilized emulsions was better compared with that of the OVA-FA-stabilized emulsions. By comparing the different OVA-FA-polysaccharide-stabilized emulsions, we observed that OVA-FA-agar did not stabilize the emulsion well, while the OVA-FA-SA- and OVA-FA-KC-stabilized emulsions had good elasticity, and the microstructure and storage stability of the OVA-FA-KC-stabilized emulsion were better. Our findings provide a new perspective for the application of OVA-FA-KC in complex food emulsions.

Image Quality Assessment Guided Collaborative Learning of Image Enhancement and Classification for Diabetic Retinopathy Grading.

Diabetic retinopathy (DR) is one of the most serious complications of diabetes and is a prominent cause of permanent blindness. However, the low-quality fundus images increase the uncertainty of clinical diagnosis, resulting in a significant decrease on the grading performance of the fundus images. Therefore, enhancing the image quality is essential for predicting the grade level in DR diagnosis. In essence, we are faced with three challenges: (I) How to appropriately evaluate the quality of fundus images; (II) How to effectively enhance low-quality fundus images for providing reliable fundus images to ophthalmologists or automated analysis systems; (III) How to jointly train the quality assessment and enhancement for improving the DR grading performance. Considering the importance of image quality assessment and enhancement for DR grading, we propose a collaborative learning framework to jointly train the subnetworks of the image quality assessment as well as enhancement, and DR disease grading in a unified framework. The key contribution of the proposed framework lies in modelling the potential correlation of these tasks and the joint training of these subnetworks, which significantly improves the fundus image quality and DR grading performance. Our framework is a general learning model, which may be useful in other medical images with low-quality data. Extensive experimental results have shown that our method outperforms state-of-the-art DR grading methods by a considerable 73.6% ACC/71.2% Kappa and 88.5% ACC/86.3% Kappa on Messidor and EyeQ benchmark datasets, respectively. In addition, our method significantly enhances the low-quality fundus images while preserving fundus structure features and lesion information. To make the framework more general, we also evaluate the enhancement results in more downstream tasks, such as vessel segmentation.

Self-stigma and quality of life among Chinese American breast cancer survivors: A serial multiple mediation model.

OBJECTIVE: Despite the increasing scholarly attention toward self-stigma among Asian breast cancer survivors, research is limited about the underlying psychological mechanisms by which self-stigma may influence quality of life for this population. The present study investigated how self-stigma is associated with quality of life among Chinese American breast cancer survivors by examining the serial mediating effects of concerns about breast cancer, self-efficacy for coping with cancer, and depressive symptoms. METHODS: Chinese American breast cancer survivors (n = 112) completed a questionnaire packet assessing self-stigma related to breast cancer, concerns about breast cancer, self-efficacy for coping with cancer, depressive symptoms, and quality of life. Path analysis was conducted to test the hypothesized serial multiple mediation model. RESULTS: The hypothesized model was supported: self-stigma was negatively associated with quality of life through concerns about breast cancer, self-efficacy, and depressive symptoms. After the mediators were controlled for, the direct effect of self-stigma on quality of life was no longer significant. CONCLUSIONS: Our findings suggest that concerns about breast cancer, self-efficacy for coping, and depressive symptoms are important pathways through which self-stigma may influence quality of life among Chinese American breast cancer survivors. Healthcare practitioners should be aware of survivors' self-stigma and make efforts to alleviate survivors' excessive cancer concerns, facilitate their self-efficacy, and offer emotional support to improve quality of life for this population.

EECP improves markers of functional capacity regardless of underlying ranolazine therapy.

OBJECTIVE: Enhanced external counterpulsation (EECP) and ranolazine are approved treatments for patients with chronic stable angina by the United States Food and Drug Administration (FDA). Whether EECP offers clinical benefits regardless of underlying ranolazine therapy needs further investigation. METHODS: This was a retrospective evaluation of patients referred to a specialized EECP center. Patients having data on 6-Minute Walk Distance (6MWD) or Duke Activity Status Index (DASI) were categorized into two groups (EECP with ranolazine or EECP only). The primary endpoints were change in 6MWD and DASI before and after a full course of EECP within each of the two groups. Inter-group differences were also assessed. The Wilcoxon test was utilized to compare the change from baseline within each group and the Mann-Whitney U test to compare difference between groups. RESULTS: A total of 2836 patient records (age 66.9 ± 10 years) were identified (1193 in EECP and ranolazine group and 1643 in EECP only group). EECP added to baseline ranolazine resulted in a statistically significant improvement in 6MWD and DASI (+126 feet (IQR: 230 feet), and +13.35 (IQR: 17.11), respectively, P<0.001 for both). Similarly, the EECP only group showed a statistically significant improvement in 6MWD and DASI (+140 feet (IQR: 225 feet) and +13.49 (IQR: 18.02), respectively, P<0.001 for both). There was no statistically significant difference between the two groups when comparing the change from baseline in 6MWD and DASI score (P=0.256 and P=0.056 respectively). CONCLUSION: EECP improves markers of functional capacity regardless of baseline ranolazine therapy. EECP's unique safety profile advocates for its early consideration in the treatment algorithm.