Search

Limitations of complex topography and lack of basic meteorological and hydrological information make the hydrological early warning and forecasting technology in small-scale mountainous watersheds weak.Using high-resolution radar observations to drive distributed hydrological models is one of the effective ways to improve the flood forecasting capabilities in small mountainous watersheds.Focusing on the Erhe river basin located in central Chongqing as the study area， a study of WRF-Hydro model flash flood simulation based on radar-estimated rainfall data is carried out to evaluate the hydrological application effect of radar-estimated rainfall and the applicability of WRF-Hydro model in small mountainous watersheds.The typical storm flood process in the watershed was selected， and the WRF-Hydro model was driven by the estimated rainfall data from S-band Doppler weather radar， and further compared with the XAJ model to analyze the simulation effects.The results show that： （1） the WRF-Hydro model driven by radar-estimated rainfall data provides a better simulation of the flood process， flood flow and peak-to-peak time in the Second River Basin， with the Nash efficiency coefficient above 0.65， the Kling-Gupta efficiency coefficient above 0.50 and the correlation coefficient above 0.85.（2） Comparing the WRF-Hydro model with the XAJ model， the simulation effectiveness of the WRF-Hydro model is superior to that of the XAJ model in the Erhe River basin， with a difference of 0.03 in the Nash coefficient and 0.04 in the correlation coefficient， further indicating the superior flood simulation performance of the WRF-Hydro model in small mountainous basins.Overall， the WRF-Hydro model based on radar-estimated rainfall data exhibited satisfactory flood simulation performance in the Erhe basin， and can be further applied in similar small-scale mountainous basins.

Taking two large runoff events and three small runoff events in the Qingjiang River Basin from 2016 to 2017 as the research objects, we analyze the spatial and temporal distributions of precipitation from the three products of CMORPH satellite-gauge-radar merged precipitation product (CMPAS), China global atmospheric reanalysis product (CRA), and rain gauge precipitation (Gauge). Then, based on the characteristics of runoff events and different precipitation products, the two experiments are designed, and the runoff simulations of WRF-Hydro model driven by the precipitation data from the three precipitation products are analyzed. Finally, combined with the spatiotemporal variations of precipitation, we explore the effectiveness of the three precipitation products in the runoff simulations. The main results are as follow. (1) The rainfall centers and the rainband locations and orientations detected with the three precipitation products are similar for the five runoff events, and the temporal trends of rainfall averaged over the Qingjiang River Basin are consistent. (2) All three precipitation products can produce the runoff events in both experiments. For large runoff events, CMPAS performs best, with correlation coefficients above 0.76 and Nash efficiency coefficients above 0.63. For small runoff events, Gauge performs best, with correlation coefficients above 0.75 and Nash efficiency coefficients above 0.48. CRA has a relatively poor performance for both large and small runoff events, but its simulations are significantly improved after calibrating parameters. (3) After calibrating the parameters in the second experiment, the simulation results of runoff are improved to different extents for the peaking, rising, and falling periods of each event. For the small runoff events, compared with the rising and falling periods, the simulations driven by each precipitation product for the peaking period is significantly improved, while, for the large runoff events, the simulation results driven by the three precipitation products for each period is not significantly improved.

Abstract Fostering human–wildlife coexistence necessitates a thorough and nuanced grasp of local attitudes toward wildlife. Attitudes can vary substantially based on the sociodemographic backgrounds of individuals within a society. This study examines Tibetan attitudes toward large carnivores, emphasizing the importance of contextualization in discerning the effects of sociodemographic factors on attitudes. We began by analyzing existing research on Tibetan attitudes toward wildlife in China, identifying previously studied sociodemographic variables. We then executed an online survey to evaluate the affective, behavioral, cognitive, and overall attitudes of ethnic Tibetans in China toward snow leopards (Panthera uncia), gray wolves (Canis lupus), and brown bears (Ursus arctos). Our findings show that while factors such as gender, age, religious identity, and level of education shape these attitudes, their influence differs depending on the specific attitude component and the target animal under examination. Therefore, making broad generalizations about sociodemographic differences in attitudes can be misleading. It is imperative for attitude research to clearly define the attitude component (what type of attitude), object (attitude toward what), and circumstance (attitude in which situation) being studied. Conducting ethnographic fieldwork in collaboration with local cultural experts can deepen our understanding of local perspectives and the ways sociodemographic factors influence attitudes. Such insights are pivotal for developing conservation strategies attuned to local sociocultural contexts.

Abstract Understanding local encounters with large carnivores is important for promoting sustainable coexistence. The use of smartphones and social media in geographically remote areas offers a novel avenue to study human–wildlife encounters from a local perspective. We conducted a content analysis of mobile videos on social media (n = 207) to characterize human encounters with snow leopards, gray wolves, and brown bears on the Tibetan Plateau in China. We also used ethnographic interviews to understand the backgrounds and motivations of videographers. Results show large carnivore encounters are not necessarily conflictual. Over half of encounters are neutral without observable interference between people and predators. The likelihood of a “negative encounter” is significantly associated with the target species, the distance between the videographer and the animal, the level of human influence in the surroundings, and the presence of other animals (i.e., dogs and livestock). Local Tibetan herders document unusual encounters with carnivores using videos for various reasons, but what is deemed unusual is context‐dependent and fluid. Our study demonstrates that social media videos can provide valuable insights into the diversity and complexity of human–wildlife interactions. We encourage conservationists to develop visual participatory programs to better engage local people in conservation knowledge production.

BackgroundSelf-regulated learning helps to improve academic performance and is an important strategy for the sustainable development of Master of Nursing Specialists. Consequently, it is relevant to identify the factors that affect self-regulated learning and analyze the correlation between them.ObjectiveThis study examined the status quo of self-regulated learning, the relationship between self-regulated learning, mindful agency, and psychological resilience, and explored whether mindful agency and psychological resilience influence self-regulation learning.MethodsChinese Master of Nursing Specialists were recruited to participate in an online survey from March to November 2022. Self-regulated learning, mindful agency, and psychological resilience were measured using three questionnaires, including the Self-Regulated Learning Scale for Clinical Nursing Practice Scale (SRLS-CNP), the Mindful Agency Scale, and the 10-item Connor-Davidson Resilience Scale (CD-RISC-10). The data were processed and analyzed using SPSS26.0. The statistical methods included descriptive statistics, Pearson’s correlation analyses, and multiple linear regression.ResultsSelf-regulated learning of Chinese Master of Nursing Specialists was medium level (59.24 ± 9.33 scores). Mindful agency and psychological resilience were positively correlated with self-regulated learning (p < 0.01) and important predictors of self-regulated learning of Master of Nursing Specialists, explaining 44.6% of the variation.ConclusionMindful agency and psychological resilience affected the self-regulated learning level of Master of Nursing Specialists in clinical practice. These results will enable clinical educators to pay more attention to the personal psychological factors of Master of Nursing Specialists to improve their self-regulated learning ability through mindful agency and psychological resilience.

Myocardial infarction (MI) is one of the most common illnesses seriously harmful to human health. Notwithstanding, the systems of its pathogenesis are as yet not totally demonstrated. CircRNA is one sort of non-coding RNA, and late distributed information proposes that circRNAs assume a significant part in heart diseases; however, their expression profiles in the peripheral blood of patients with MI are not yet totally characterized. Therefore, RNAs from peripheral blood were recruited for high-throughput RNA-seq analysis. A total of 3,862 circRNAs were distinguished to be remarkably different, including 2,738 circRNAs being upregulated and 1,124 circRNAs being downregulated. circTMEM165, circUBAC2, circZNF609, circANKRD12, and circSLC8A1 were reconfirmed by RT-qPCR in the cell model. ROC curves uncovered that they have great sensitivity and specificity in the determination of MI. Besides, circRNAs are associated with cell metabolism and function by directing complex networks among circular RNAs, microRNAs, and messenger RNAs. In outline, our study portrayed the specific articulation profiles of circular RNA in patients with MI. The outcomes showed that circRNAs might fill in as a sort of ideal biomarkers for MI diagnosis. Further exploration of these circRNAs may enrich our understanding of MI etiology and progression.

Inflammatory bowel disease (IBD) is a chronic intestinal disease associated with the inflammatory gastrointestinal tract and microbiome dysbiosis. Probiotics are a promising intervention, and several probiotics have been reported to positively affect IBD remission and prevention, particularly on ulcerative colitis (UC). However, there is still a limitation in the knowledge of effectiveness and safety of probiotics therapies for IBD. Exploring more potential probiotics helps to find extensive evidence for probiotic intervention. This study established a rapid method for probiotics candidate screening and finally screened out one strain with the best protective effect. Forty strains isolated from four different sources were used for this screening. Hemolysis tests and acute toxic test evaluated strain safety. Zebrafish were first treated with dextran sodium sulfate (DSS) for colitis induction, and every bacteria were individually added to the fish water subsequently. Results showed eight strains could lower the larvae mortality within 3 days under a 0.6% DSS concentration, including Lacticaseibacillus rhamnosus GG, L. rhamnosus NBRC3425, Bacillus smithii DSM4216, B. smithii XY1, Bacillus coagulans NBRC12583, Bacillus coagulans XY2, Lactobacillus parafarraginis XYRR2, and Bacillus licheniformis XYT3. Among eight, B. smithii XY1 was the only strain having the equal ability to alleviate neutrophil infiltration in the larvae intestine with that ability of prednisolone under a 0.5% DSS concentration. Bacillus smithii XY1 restored intestinal epithelial cell integrity after DSS damage, as well as regulated the gene expression inflammation-related factors, indicating its bio-function of inflammatory response alleviation.

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer’s approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices’ transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

Aortic dissection (AD) is a fatal cardiovascular acute disease with high incidence and mortality, and it seriously threatens patients’ lives and health. The pathogenesis of AD mainly includes vascular inflammation, extracellular matrix degradation, and phenotypic conversion as well as apoptosis of vascular smooth muscle cells (VSMCs); however, its detailed mechanisms are still not fully elucidated. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are an emerging class of RNA molecules without protein-coding ability, and they play crucial roles in the progression of many diseases, including AD. A growing number of studies have shown that the dysregulation of ncRNAs contributes to the occurrence and development of AD by modulating the expression of specific target genes or the activity of related proteins. In addition, some ncRNAs exhibit great potential as promising biomarkers and therapeutic targets in AD treatment. In this review, we systematically summarize the recent findings on the underlying mechanism of ncRNA involved in AD regulation and highlight their clinical application as biomarkers and therapeutic targets in AD treatment. The information reviewed here will be of great benefit to the development of ncRNA-based therapeutic strategies for AD patients.

There are numerous varieties of mulberry, and each has high medicinal value and is regarded as a promising source of traditional medicines and functional foods. Nevertheless, the nutrients and uses of mulberry differ from species (Morus alba L., Morus nigra L. and Morus rubra L.). Phenolic compounds are prominent among the biologically active ingredients in mulberry, especially flavonoids, anthocyanins and phenolic acids. Epidemiologic studies suggest that mulberry contains a rich, effective chemical composition and a wide range of biological activity, such as antioxidant, anti-inflammatory, anti-tumor and so on. However, compared with other berries, there has been a lack of systematic research on mulberry, and this hinders its further expansion as a functional fruit. The main purpose of this review is to provide the latest data regarding the effective chemical constituents and pharmacological effects of mulberry to support its further therapeutic potential and health functions.

Objective The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods. Materials and Methods In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COPD samples) and known COPD-related genes (candidate genes) were downloaded from the Gene Expression Omnibus (GEO) and the Online Mendelian Inheritance in Man (OMIM) databases respectively. Based on the expression values of the candidate genes, COPD samples were divided into molecular subtypes through hierarchical clustering analysis. Candidate genes were accordingly allocated into the defined molecular subtypes and functional enrichment analysis was undertaken. Pathway deviation scores were then analyzed, followed by the analysis of clinical indicators (FEV1, FEV1/FVC, age and gender) of COPD patients in each subtype, and prediction models were constructed. Furthermore, the gene expression dataset GSE71220 was used to bioinformatically validate our results. Results A total of 213 COPD-related genes were identified, which divided samples into three subtypes based on the gene expression values. After intersection analysis, 160 common genes including transforming growth factor β1 (TGFB1), epidermal growth factor receptor (EGFR) and interleukin 13 (IL13) were obtained. Functional enrichment analysis identified 22 pathways such as ‘hsa04060: cytokine-cytokine receptor interaction pathways, ‘hsa04110: cell cycle’ and ‘hsa05222: small cell lung cancer’. Pathways in subtype 2 had higher deviation scores. Furthermore, three receiver operating characteristic (ROC) curves (accuracies >80%) were constructed. The three subtypes in COPD samples were also identified in the validation dataset GSE71220. Conclusion COPD may be further subdivided into several molecular subtypes, which may be useful in improving COPD therapy based on the molecular subtype of a patient.

Abstract Background Lens dislocation is a common disease in ophthalmology, which leads to vision loss, while the lens dislocation caused by vomiting has not been reported yet. We report a case of lens dislocation caused by simple vomiting. This case further implicated for the pathogenesis of lens dislocation. Case presentation A 51-year-old male who complained about “dizziness, vomiting, and the vision decreased for 4 h in right eye”, after the eye examination, he was been diagnosed with “lens dislocation induced by simply vomiting “. Surgery was performed successfully.We highlight the pathogenesis and development of the lens dislocation in this rare condition. Conclusion Lens dislocation could be induced by simple vomitting, which increased the vitrous cavity presure to shock the zonular fiber and push the lens into the anterior chamber.

Cardiovascular disease is a major threat to human health. The study on the pathogenesis and prevention of cardiovascular disease has received special attention. In this paper, we have contributed to the derivation of a mathematical model for the nonlinear waves in an artery. From the Navier–Stokes equations and continuity equation, the vorticity equation satisfied by the blood flow is established. And based on the multiscale analysis and perturbation method, a new model of the Boussinesq equation with viscous term is derived to describe the propagation of a viscous fluid through a thin tube. In order to be more consistent with the flow of the fluid, the time-fractional Boussinesq equation with viscous term is deduced by employing the semi-inverse method and the fractional variational principle. Moreover, the approximate analytical solution of the fractional equation is obtained, and the effect of viscosity on the amplitude and width of the wave is studied. Finally, the effects of the fractional order parameters and vessel radius on blood flow volume are discussed and analyzed.

The present study investigated the anti-obesity effects of lycopene (LC), amaranth (AR), and sorghum red (SR) in high-fat diet-induced C57BL/6J mice. Compared with untreated mice on a high-fat diet for 12 weeks, LC, AR, and SR reduced the body weight by 17.9%, 23.5%, and 26.5%, respectively. They markedly reversed glucose tolerance and the serum levels of triglycerides, total cholesterol, and low-density lipoprotein cholesterol, observably improved the liver profiles of TG and TC, and effectively ameliorated lipid metabolism. After the administration of LC, AR, and SR, the intestinal microbial environment improved in the sense that bacterial diversity increased and Clostridium abundance decreased. LC and SR promoted the proliferation of Akkermansia, whereas AR and SR impeded the proliferation of Prevotella. Furthermore, LC, AR, and SR reduced the TMAO level. In conclusion, LC, AR, and SR may protect against high-diet induced obesity and modulate the gut microbiota of mice.

Accurate and long leading time flood forecasting is very important for flood disaster mitigation. It is an effective method to couple the Quantitative Precipitation Forecast (QPF) products provided by Numerical Weather Prediction (NWP) models to a distributed hydrological model with the goal of extending the leading time for flood forecasting. However, the QPF products contain a certain degree of uncertainty and would affect the accuracy of flood forecasting, especially in the mountainous regions. Radar data assimilation plays an important role in improving the quality of QPF and further improves flood forecasting. In this paper, radar data assimilation was applied in order to construct a high-resolution atmospheric-hydrological coupling model based on the WRF and WRF-Hydro models. Four experiments with conventional observational and radar data assimilation were conducted to evaluate the flood forecasting capability of this coupled model in a small-medium sized basin based on eight typical flood events. The results show that the flood forecast skills are highly QPF-dependent. The QPF from the WRF model is improved by assimilating radar data and further increasing the accuracy of flood forecasting, although both precipitation and flood are slightly over-forecasted. However, the improvements by assimilating conventional observational data are not obvious. In general, radar data assimilation can improve flood forecasting effectively in a small-medium sized basin based on the atmospheric-hydrological coupling model.

Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general.

There are two different approaches on how to formulate adjoint numerical model (ANM). Aiming at the disputes arising from the construction methods of ANM, the differences between nonlinear shallow water equation and its adjoint equation are analyzed; the hyperbolicity and homogeneity of the adjoint equation are discussed. Then, based on unstructured meshes and finite volume method, a new adjoint model was advanced by getting numerical model of the adjoint equations directly. Using a gradient check, the correctness of the adjoint model was verified. The results of twin experiments to invert the bottom friction coefficient (Manning’s roughness coefficient) indicate that the adjoint model can extract the observation information and produce good quality inversion. The reason of disputes about construction methods of ANM is also discussed in the paper.