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Abstract Land degradation significantly impacts regional economic development and food security, particularly in arid river basins where soil and water conservation is crucial. Understanding the extent and causes of land degradation is pivotal for effectively prevention and management. This study employs the soil adjusted vegetation index (SAVI), the temperature vegetation dryness index (TVDI), and the salinization detection index (SDI), combined with the analytic hierarchy process and the entropy weight method, to construct a comprehensive land degradation index (LDI). Sen’s slope trend analysis and the Mann-Kendall significance test were used to analyze land degradation trends in the Ebinur Lake watershed from 2002 to 2022. Additionally, the optimal parameters-based geographical detector was used to examine the underlying mechanisms of land degradation. The results indicate the following: (1) From 2002 to 2012, the degree of land degradation in the Ebinur Lake watershed worsened, particularly in the eastern and southeastern parts, as well as in the southern region of Toli County. From 2012 to 2022, land degradation significantly improved, with a notable reduction in degraded land area. (2) Over the period of 2002-2022, $$93.08\%$$ 93.08 % of the land in the research region exhibited a declining LDI trend, $$3.95\%$$ 3.95 % showed no change, and only $$2.96\%$$ 2.96 % showed an increasing LDI trend. (3) Moderate, severe, and very severe degradation mainly occurred on grassland and unused land, while light degradation and non-degradation primarily occurred on forest land and cultivated land. (4) Unreasonable land use and overgrazing were identified as the primary factors influencing land degradation, with elevation being a secondary factor. The interaction between land use and other factors was found to be most significant, followed by the synergistic effects of grazing quantity with elevation, annual average temperature, gross domestic product, soil moisture, and elevation with annual average precipitation, and temperature. The results of this study offer an empirical basis and taking decisions assistance for land degradation control in the Ebinur Lake Basin, as well as examples and references for assessing land degradation in other places.

Tremella fuciformis (TF) stem accounts for about 10% of the whole mushroom, and most of them are dumped without utilization. To effectively utilize the waste, the effects of fine grinding and superfine grinding technology on the processing characteristics and physicochemical properties of Tremella fuciformis stem powder (TFSP) were investigated, and the effects of TFSP on the quality of steamed bun were analyzed. The findings indicate that the specific surface area of superfine Tremella fuciformis stem powder (STFSP) is 1.6 times larger than that of fine Tremella fuciformis stem powder (FTFSP). Despite this, the infrared spectra of STFSP and FTFSP are similar. The water solubility index and oil holding capacities of STFSP saw an increase of 18.81%, whereas its water holding capacity decreased by 11.34%. Superfine grinding not only enhanced the processing characteristics but also stabilized the powder form. Moreover, steamed buns incorporating 4% STFSP were judged to have the optimal taste. Superfine grinding significantly influences the physicochemical properties of TFSP, resulting in enhanced quality of steamed buns.

BackgroundThe treatment of severely displaced Rockwood and Wilkins' type C (RWC) thumb metacarpal basal fractures remains controversial in children. This retrospective study aimed to compare the efficacy of two treatment methods, open vs. closed reduction with pinning of such injuries.MethodsThis study included 30 patients with open physes, 14 boys and 16 girls, who all received either closed or open reduction treatment. The primary outcomes of interest included healing time, complications, and functional results, which were evaluated using the improved Mayo score standard. The minimum follow-up period was 24 months, with a mean of 30.3 months (range 24.0–45.0 months). Statistical significant was defined as P

Background: Survival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients’ survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. Results: AdFed was tested on different cancer datasets that contain the patients’ information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC = 0.605) than the compared federated-learning-based methods (average AUC = 0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. Conclusions: The results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

BackgroundThe management of femoral fractures in children aged two to six years is still controversial. The purpose of this study was to assess the results of closed reduction and elastic stable intramedullary nail (ESIN) fixation in completely displaced fractures of the femoral diaphysis in children in this age group.MethodsA retrospective review of all children with acute completely displaced fractures of the femoral diaphysis in children aged 2–6 years treated from 2013 to 2020 was performed. A total of 34 patients were treated who met the inclusion criteria: Group 1: 21 fractures (transverse and short oblique); Group 2: 13 fractures (long oblique and spiral) that underwent closed reduction and elastic stable intramedullary nail (ESIN) fixation. No differences existed between the 2 groups with respect to age, extremity, sex, time to treatment, mechanism of injury, or fracture displacement. Demographic characteristics and radiographs were reviewed, and the following parameters were documented: surgery time, time to union, return to activities, range of motion of knee joints, and complications. Major complications were defined as those with presumptive long-term side effects or those requiring a reoperation. No major complications were observed in the two groups. All included fractures were treated by a single senior paediatric surgeon. The mean follow-up period was 28.4 months (range 24–45 months). The level of significance was set at p

Natural products are a main source of new chemical entities for use in drug and pesticide discovery. In order to discover lead compounds with high herbicidal activity, a series of new pyrido[2,3-d] pyrimidine derivatives were designed and synthesized using 2-chloronicotinic acid as the starting material. Their structures were characterized with 1H NMR, 13C NMR and HRMS, and the herbicidal activities against dicotyledonous lettuce (Lactuca sativa), field mustard (Brassica campestris), monocotyledonous bentgrass (Agrostis stolonifera) and wheat (Triticum aestivum) were determined. The results indicated that most of the pyrido[2,3-d] pyrimidine derivatives had no marked inhibitory effect on lettuce at 1 mM. However, most of the pyrido[2,3-d] pyrimidine derivatives possessed good activity against bentgrass at 1 mM. Among them, the most active compound, 3-methyl-1-(2,3,4-trifluorophenyl)pyrido[2,3-d]pyrimidine-2,4(1H,3H)-dione (2o), was as active as the positive controls, the commercial herbicides clomazone and flumioxazin. Molecular simulation was performed with molecular docking and DFT calculations. The docking studies provided strong evidence that 2o acts as an herbicide by inhibition of protoporphyrinogen oxidase. However, the physiological results indicate that it does not act on this target in vivo, implying that it could be metabolically converted to a compound with a different molecular target.

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.

Abstract Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.

Montmorillonite has been widely used in remediation of toxic metals. However, for the safety of the soil ecosystem, the impact of this technology on microorganisms is still unclear. Here, the influence of nano-montmorillonite on immobilization, accumulation of cadmium, and microbial activity in a soil–plant system was investigated. The results revealed that the nano-montmorillonite treatments reduced cadmium bioavailability and the uptake by oilseed rape. It was found that the addition of NMMT reduced the toxicity of Cd on soil microorganisms and improved enzyme activity. The nano-montmorillonite stimulated the relative abundance of Proteobacteria that was closely related to nutrient turnover in soil. In addition, the treatment with 0.5% addition of nano-montmorillonite significantly improved the microbial alpha diversity index and enhanced the relative abundance of Bacteroidetes and Planctomycetes. Thus, certain soil quality recovery occurred after the application of nano-montmorillonite. To successfully combine remediation and microbial effects, 0.5% nano-montmorillonite was recommended to be added to cadmium-contaminated soil to minimize the additional impact on the soil ecosystem.

A series of new fluorinated quinoline analogs were synthesized using Tebufloquin as the lead compound, 2-fluoroaniline, ethyl 2-methylacetoacetate, and substituted benzoic acid as raw materials. Their structures were confirmed by 1H NMR, 13C NMR, and HRMS. The compound 8-fluoro-2,3-dimethylquinolin-4-yl 4-(tert-butyl)benzoate (2b) was further determined by X-ray single-crystal diffraction. The antifungal activity was tested at 50 μg/mL, and the bioassay results showed that these quinoline derivatives had good antifungal activity. Among them, compounds 2b, 2e, 2f, 2k, and 2n exhibited good activity (>80%) against S. sclerotiorum, and compound 2g displayed good activity (80.8%) against R. solani.

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30–90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

Transmembrane Channel-like (TMC) genes are critical in the carcinogenesis, proliferation, and cell cycle of human cancers. However, the multi-omics features of TMCs and their role in the prognosis and immunotherapeutic response of human cancer have not been explored. We discovered that TMCs 4-8 were commonly deregulated and correlated with patient survival in a variety of cancers. For example, TMC5 and TMC8 were correlated with the relapse and overall survival rates of breast cancer and skin melanoma, respectively. These results were validated by multiple independent cohorts. TMCs were regulated by DNA methylation and somatic alterations, such as TMC5 amplification in breast cancer (523/1062, 49.2%). Six algorithms concordantly uncovered the critical role of TMCs in the tumor microenvironment, potentially regulating immune cell toxicity and lymphocytes infiltration. Moreover, TMCs 4-8 were correlated with tumor mutation burden and expression of PD-1/PD-L1/CTLA4 in 33 cancers. Thus, we established an immunotherapy response prediction (IRP) score based on the signature of TMCs 4-8. Patients with higher IRP scores showed higher immunotherapeutic responses in five cohorts of skin melanoma (area under curve [AUC] = 0.90 in the training cohort, AUCs range from 0.70 to 0.83 in the validation cohorts). Together, our study highlights the great potential of TMCs as biomarkers for prognosis and immunotherapeutic response, which can pave the way for further investigation of the tumor-infiltrating mechanisms and therapeutic potentials of TMCs in cancer.

It is well recognized that hypoxia-inducible factor 1 alpha (HIF-1α) is involved in cancer metastasis, chemotherapy and poor prognosis. We previously found that deferoxamine, a hypoxia-mimetic agent, induces epithelial-mesenchymal transition (EMT) in colorectal cancer. Therefore, here we explored a new molecular mechanism for HIF-1α contributing to EMT and cancer metastasis through binding to ZEB1. In this study, we showed that overexpression of HIF-1α with adenovirus infection promoted EMT, cell invasion and migration in vitro and in vivo. On a molecular level, HIF-1α directly binding to the proximal promoter of ZEB1 via hypoxia response element (HRE) sites thus increasing the transactivity and expression of ZEB1. In addition, inhibition of ZEB1 was able to abrogate the HIF-1α-induced EMT and cell invasion. HIF-1α expression was highly correlated with the expression of ZEB1 in normal colorectal epithelium, primary and metastatic CRC tissues. Interestingly, both HIF-1α and ZEB1 were positively associated with Vimentin, an important mesenchymal marker of EMT, whereas negatively associated with E-cadherin expression. These findings suggest that HIF-1α enhances EMT and cancer metastasis by binding to ZEB1 promoter in CRC. HIF-1α and ZEB1 are both widely considered as tumor-initiating factors, but our results demonstrate that ZEB1 is a direct downstream of HIF-1α, suggesting a novel molecular mechanism for HIF-1α-inducing EMT and cancer metastasis.

Purpose: This study explores the potential of Omilancor in treating Intervertebral Disc Degeneration (IDD) through MAP2K6 targeting., Methods: We analyzed mRNA microarray datasets to pinpoint MAP2K6 as a key regulator implicated in IDD progression. Follow-up studies demonstrated that cisplatin (DDP) could prompt cellular senescence in vitro by upregulating MAP2K6 expression. Through molecular docking and other analyses, we identified Omilancor as a compound capable of binding to MAP2K6. This interaction effectively impeded the cellular senescence induced by DDP., Results: We further showed that administration of Omilancor could significantly alleviate the degeneration of IVDs in annulus fibrosus puncture-induced rat model., Conclusions: Omilancor shows promise as a treatment for IDD by targeting MAP2K6-mediated cellular senescence.