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Precisely quantifying the spatiotemporal variation patterns of ecosystem water use efficiency (WUE) (i.e., WUE and WUE ) and carbon use efficiency (CUE) across diverse regions, as well as identifying the spatial heterogeneity of their principal influencing factors, are crucial for elucidating the complex underlying mechanisms governing carbon and water cycles in the Yellow River Basin (YRB). In this study, we utilized multi-source remote sensing data, and employed Ensemble Empirical Mode Decomposition (EEMD) to explore the nonlinear spatiotemporal trends and patterns of WUE , WUE , and CUE within the YRB ecosystem. Additionally, we applied the optimally parameterized XGBoost and SHAP models to discern the spatial heterogeneity of the key factors driving their spatiotemporal variations. The results showed that: (1) The WUE , WUE , and CUE of the YRB ecosystem exhibited a spatial distribution pattern characterized by higher values in the southeast and lower values in the northwest, with these metrics were predominantly concentrated at elevations ranging from 1000 to 1500 meters. (2) The interannual change rates of the yearly average values of WUE , WUE and CUE in the YRB ecosystem were 0.008 , 0.005 , and 0.001, respectively. The predominant change patterns for WUE and WUE were monotonic increases, covering approximately 42.44% and 41.97% of the watershed area, respectively. In contrast, the change pattern for CUE was primarily a decrease followed by an increase, observed across 42.51% of the watershed area. (3) In the YRB ecosystem, the leaf area index (LAI) emerged as the primary determinant of WUE and WUE . Specifically, WUE and WUE both showed an upward trend in tandem with the increase in LAI. Furthermore, temperature was identified as the key driving factor for CUE within the YRB ecosystem. (4) In the YRB ecosystem, LAI exhibited the highest importance index for both WUE and WUE . It played a dominant role in approximately 42.80% and 45.35% of the study areas for WUE and WUE , respectively. Conversely, temperature was a crucial factor influencing the spatial variability of CUE in the YRB ecosystem, exerting a predominant influence in 38.88% of the study areas.

Understanding the spatiotemporal patterns of cropland carbon and carbon water use efficiency (CWUE) and its driving factors is essential for sustainable agricultural development. Based on a multi-source remote sensing dataset, this study applies a trend analysis (Sen + Mann–Kendall), a dual-type randomized extraction algorithm, and an optimized XGBoost model to examine the spatiotemporal variations in cropland CWUE, including the water use efficiency of net primary production (WUENPP), water use efficiency of gross primary production (WUEGPP), and carbon use efficiency (CUE) in Henan Province from 2001 to 2019. This study further quantifies the impact of irrigation on the cropland CWUE and explores the synergistic effects of its driving factors in irrigated areas. Results reveal significant regional differences in cropland CWUE across Henan Province. Higher multi-year average values of CUE and WUENPP were observed in the western region, while the WUEGPP was more prominent in the south-central region. Over 76% of cropland areas showed a general downward trend in three indicators, with significant interannual declines. Non-irrigated cropland exhibited higher CWUE values than irrigated ones. The average values over multiple years of the WUEGPP, WUENPP, and CUE of irrigated cropland were 2.51 g C m−2 mm−1, 1.08 g C m−2 mm−1, and 0.43, respectively. Sunlight was the dominant factor influencing the WUEGPP in irrigated areas, while precipitation primarily regulated the WUENPP and CUE. The influence of the gross domestic product (GDP) was found to be minimal. Notably, both the leaf area index (LAI) and precipitation exhibited a shift from a positive to negative influence on CUE once their values exceeded optimal thresholds, indicating that resource overabundance can lead to physiological limitations. This study offers valuable insights into how irrigated cropland responds to the combined effects of multiple environmental and socio-economic drivers.

Post-translational modifications (PTMs) of proteins, by altering the structural conformation of precursor polypeptides, play an indispensable role in augmenting the diversity and stability of the proteome. PTMs exert profound influence on various hallmarks of tumor biology, including cellular proliferation, apoptosis, angiogenesis and metastatic dissemination. Accordingly, advancing our understanding of PTMs holds substantial promise for broadening the therapeutic landscape of oncology. Among these modifications, palmitoylation, a reversible lipid-based PTM, critically modulates protein stability, membrane localization, protein-protein interactions and signal transduction cascades. Dysregulation of palmitoylation has been increasingly implicated in tumorigenesis, suggesting its aberrant forms as putative targets for therapeutic intervention. The present review delineates the biochemical mechanisms underlying protein palmitoylation and synthesizes current insights into its multifaceted roles in tumor progression, immune modulation and metabolic regulation, thereby offering novel perspectives for the development of targeted cancer therapies.

A worldwide outbreak of coronavirus disease (COVID-19), since 2019, has brought a disaster to people all over the world. Many researchers carried out clinical epidemiological studies on patients with COVID-19 previously, but risk factors for patients with different levels of severity are still unclear. 562 patients with laboratory-confirmed COVID-19 from 12 hospitals in China were included in this retrospective study. Related clinical information, therapies, and imaging data were extracted from electronic medical records and compared between patients with severe and non-severe status. We explored the risk factors associated with different severity of COVID-19 patients by logistic regression methods. Based on the guideline we cited, 509 patients were classified as non-severe and 53 were severe. The age range of whom was 5-87 years, with a median age of 47 (IQR 35.0-57.0). And the elderly patients (older than 60 years old) in non-severe group were more likely to suffer from fever and asthma, accompanied by higher level of D-dimer, red blood cell distribution width and low-density lipoprotein. Furthermore, we found that the liver and kidney function of male patients was worse than that of female patients in both severe and non-severe groups with different age levels, while the severe females had faster ESR and lower inflammatory markers. Of major laboratory markers in non-severe cases, baseline albumin and the lymphocyte percentage were higher, while the white blood cell and the neutrophil count were lower. In addition, severe patients were more likely to be accompanied by an increase in cystatin C, mean hemoglobin level and a decrease in oxygen saturation. Besides that, advanced age and indicators such as count of white blood cell, glucose were proved to be the most common risk factors preventing COVID-19 patients from aggravating. The potential risk factors found in our study have shown great significance to prevent COVID-19 patients from aggravating and turning to critical cases during treatment. Meanwhile, focusing on gender and age factors in groups with different severity of COVID-19, and paying more attention to specific clinical symptoms and characteristics, could improve efficacy of personalized intervention to treat COVID-19 effectively.

Background and Purpose. Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and identify the risk factors for COVID-19 patients complicated with diabetes. Methods. In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors. Results. 538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. The median age was 47 years (IQR 35.0-56.0). And the elderly patients with diabetes were more likely to have dry cough, and the alanine aminotransferase, lactate dehydrogenase, Ca, and mean hemoglobin recovery rate were higher than the other groups. Furthermore, we also found the liver and kidney function of male patients was worse than that of female patients, while female cases should be paid more attention to the occurrence of bleeding and electrolyte disorders. Moreover, advance age, blood glucose, gender, prothrombin time, and total cholesterol could be considered as risk factors for COVID-19 patients with diabetes through the multivariable logistic regression model in our study. Conclusion. The potential risk factors found in our study showed a major piece of the complex puzzle linking diabetes and COVID-19 infection. Meanwhile, focusing on gender and age factors in COVID-19 patients with or without diabetes, specific clinical characteristics, and risk factors should be paid more attention by clinicians to figure out a targeted intervention to improve clinical efficacy worldwide.