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Abstract Babesia spp. and Theileria spp. are tick-borne protozoan parasites with veterinary importance. In China, epidemiological and genetic investigations on many Babesia and Theileria species were still absent in many areas and many tick species. From Aug 2021 to May 2023, 645 ticks were collected from the body surface of domestic animals (camels, goats, sheep, and cattle) using tweezers in seven counties in three provinces including Xinjiang (Qitai, Mulei, Hutubi, and Shihezi counties), Chongqing (Youyang and Yunyang counties), and Qinghai (Huangzhong county). Three tick species were morphologically and molecularly identified (334 Hyalomma asiaticum from Xinjiang, 245 Rhipicephalus microplus from Chongqing, and 66 Haemaphysalis qinghaiensis from Qinghai). A total of three Babesia species and two Theileria species were detected targeting the 18S gene. The COI and cytb sequences were also recovered from Babesia strains for further identification. In R. microplus from Chongqing, Babesia bigemina, the agent of bovine babesiosis, was detected. Notably, in H. asiaticum ticks from Xinjiang, a putative novel genotype of Babesia caballi was identified (0.90%, 3/334), whose COI and cytb genes have as low as 85.82% and 90.64–90.91% nucleotide identities to currently available sequences. It is noteworthy whether the sequence differences of its cytb contribute to the drug resistance of this variant due to the involvement of cytb in the drug resistance of Babesia. In addition, Theileria orientalis and Theileria annulata were detected in R. microplus from Chongqing (12.20%, 31/245) and H. asiaticum from Xinjiang (1.50%, 5/334), respectively. These results suggest that these protozoan parasites may be circulating in domestic animals in these areas. The pathogenicity of the novel genotype of B. caballi also warrants further investigation.

Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies. Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area. Although various methods, such as index-based methods, curve similarity-based methods and machine learning-based methods, have been developed for winter wheat mapping based on remote sensing, the former two often require satellite data with high temporal resolution, which are unsuitable for Landsat data with sparse time-series. Machine learning is an effective method for crop classification using Landsat data. Yet, applying machine learning for winter wheat mapping in the North China Plain encounters two main issues: 1) the lack of adequate and accurate samples for classifier training; and 2) the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area. To address these two issues, we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data, with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps. Then, we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion, which divided the study area into six subzones with uniform classification features. For each subzone, a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data. Field sample validation confirmed the high accuracy of the produced maps, with an average overall accuracy of 91.1% and an average kappa coefficient of 0.810 across different years. The derived winter wheat area also has a good correlation (R2 = 0.949) with census area at the provincial level. The results underscore the reliability of the produced annual winter wheat maps. Additional experiments demonstrate that our proposed optimal zoning method outperforms other zoning methods, including Köppen climate zoning, wheat planting zoning and non-zoning methods, in enhancing wheat mapping accuracy. It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

We report a case of tibial osteochondroma in a 25-year-old female who presented with a palpable calf mass. This mass was associated with a thick cartilaginous cap on cross-sectional imaging, suggesting chondrosarcoma. A CT-guided biopsy was performed, and histology, however, was consistent with osteochondroma. Orthopedic oncology recommended surgical excision due to the potential high sampling error with chondroid lesions. The patient underwent surgical resection, resulting in a final diagnosis of osteochondroma. No post-surgical complications occurred, and a 12-month follow-up showed no evidence of local recurrence. This case highlights the atypical imaging feature of a thick cartilaginous cap in a benign etiology without malignant transformation.

Abstract More than 70% of the potash fertilizer globally is produced by the froth flotation process, in which 4-dodecylmorpholine (DMP) serves as a reverse flotation agent. As the potash fertilizer production rapidly rises, the increased DMP levels in discharged brine pose a threat to the production of high-value chemicals. In this paper, composite particles of basic magnesium sulfate@TiO2 (BMS@TiO2) were prepared using a simple and mild loading method. These particles were utilized for the adsorption and photocatalytic degradation of DMP in brine. Compared with normal powdered materials, the granular BMS@TiO2 in this study can be easily separated from liquid, and the degradation intermediates will not enter the brine without causing secondary pollution. BMS@TiO2 consists of 5·1·7 phase (5Mg(OH)2·MgSO4·7H2O) whisker clusters embedding 2.3% TiO2. The adsorption equilibrium of DMP on BMS@TiO2 particles was achieved through hydrogen bonding and pore interception with the adsorption capacity of approximately 5 mg g−1 after 6 h. The photodegradation efficiency of DMP adsorbed on BMS@TiO2 reached about 92% within 16 h, which is compared with that of pure TiO2 nanoparticles. Additionally, excellent stability and recyclability of BMS@TiO2 were also observed in five cycle tests of adsorption and photocatalytic degradation of DMP, and the possible photocatalytic degradation pathways and mechanism of DMP are proposed following molecular electrostatic potential analysis. This work provides a sustainable and environmentally friendly approach for eliminating organic micropollutants from water environments.

Abstract Background Hyalomma ticks are widely distributed in semi-arid zones in Northwest China. They have been reported to harbor a large number of zoonotic pathogens. Methods In this study, a total of 334 Hyalomma asiaticum ticks infesting domestic animals were collected from four locations in Xinjiang, Northwest China, and the bacterial agents in them were investigated. Results A putative novel Borrelia species was identified in ticks from all four locations, with an overall positive rate of 6.59%. Rickettsia sibirica subsp. mongolitimonae, a human pathogen frequently reported in Europe, was detected for the second time in China. Two Ehrlichia species (Ehrlichia minasensis and Ehrlichia sp.) were identified. Furthermore, two Anaplasma species were characterized in this study: Candidatus Anaplasma camelii and Anaplasma sp. closely related to Candidatus Anaplasma boleense. It is the first report of Candidatus Anaplasma camelii in China. Conclusions Six bacterial agents were reported in this study, many of which are possible or validated pathogens for humans and animals. The presence of these bacterial agents may suggest a potential risk for One Health in this area. Graphical Abstract

N6-methyladenosine (m6A) is a chemical modification of RNA and has become a widely discussed topic among scientific researchers in recent years. It is distributed in various organisms, including eukaryotes and bacteria. It has been found that m6A is composed of writers, erasers and readers and is involved in biological functions such as splicing, transport and translation of RNA. The balance of the human immune microenvironment is important for human health abnormalities. Increasing studies have found that m6A affects the development of immune diseases such as inflammatory enteritis and systemic lupus erythematosus (SLE) by participating in the homeostatic regulation of the immune microenvironment in vivo. In this manuscript, we introduce the composition, biological function, regulation of m6A in the immune microenvironment and its progression in various immune diseases, providing new targets and directions for the treatment of immune diseases in clinical practice.

Study region: The Suzhou-Wuxi-Changzhou (SWC) metropolitan area, China. Study focus: Urban expansion is a natural product of growing cities at the expense of river systems. In this study, we evaluated river system changes that come with urban expansion on a grid scale. We also utilized the spatial analysis models to examine the impact of urban expansion on river system changes. New hydrological insights for the region: Our results indicate that the cities in the metropolitan area had a similar pattern of urban expansion from the 1960s to the 2010s, with Changzhou, Wuxi, and Suzhou losing 45.8%, 32.3%, and 42.2% of their rivers, respectively. A negative correlation was detected between the urban expansion area (UEA) and the river density variation (∆Rd). During the rapid urban expansion period, ∆Rd significantly decreased as UEA increased. We then identified the spatial dependence and spatial spillover effect between river system changes and urban expansion. In our analysis, the river density was degraded as a result of urban expansion both in the in-situ location and in the neighboring locations. The river system has seen significant changes due to the impervious areas and infrastructures caused by urban expansion; thus, an appropriate pattern of urban development is required to mitigate the negative effects of river loss.

Background: Heart failure with preserved ejection fraction (HFpEF) presents a serious risk to human health. The increased prevalence of sarcopenia in the HFpEF population has a negative impact on patient prognosis. Uric acid (UA) is the byproduct of purine metabolism and is harmful to the cardiovascular system. This study aims to establish the potential relationship between sarcopenia and serum UA in HFpEF patients. Methods: Data were obtained from 180 individuals (aged ≥60 years) with HFpEF admitted to the Geriatric Department of Jiangsu Province Hospital between January 2021 and December 2022. The UA values were grouped into 4 quartiles (Q1-Q4). Logistic generalized linear models and restricted cubic spline (RCS) regression were used to analyze the relationship between sarcopenia and UA. Subgroups based on gender were utilised for further analysis. Results: After adjusting for covariates, odds ratios (ORs) and 95 % confidence intervals (CIs) for sarcopenia prevalence in the 2nd, 3rd, and 4th quartiles were 2.56 (0.57–12.65), 4.94 (1.10–24.49), and 6.95 (1.30–44.25), respectively, unlike the 1st quartile (P for trend = 0.022). The RCS plot demonstrated a positive linear relationship between serum UA levels and sarcopenia (P for non-linearity = 0.190). A sex-based subgroup analysis revealed a statistically significant relationship between UA and sarcopenia in males (P

BackgroundRodents are recognized as the hosts of many vector-borne bacteria and protozoan parasites and play an important role in their transmission and maintenance. Intensive studies have focused on their infections in vectors, especially in ticks, however, vector-borne bacterial and protozoan infections in rodents are poorly understood although human cases presenting with fever may due to their infection have been found.MethodsFrom May to October 2019, 192 wild rodents were trapped in wild environment of Guangxi Province, and the spleen samples were collected to reveal the presence of vector-borne bacterial and protozoan infections in them. The microorganisms in rodents were identified by detecting their DNA using (semi-)nested PCR. All the PCR products of the expected size were subjected to sequencing, and then analyzed by BLASTn. Furthermore, all the recovered sequences were subjected to nucleotide identity and phylogenetic analyses.ResultsAs a result, 192 rodents representing seven species were captured, and Bandicota indica were the dominant species, followed by Rattus andamanensis. Based on the (semi-)nested PCR, our results suggested that Anaplasma bovis, Anaplasma capra, Anaplasma ovis, Anaplasma phagocytophilum, "Candidatus Neoehrlichia mikurensis", "Candidatus E. hainanensis", "Candidatus E. zunyiensis", three uncultured Ehrlichia spp., Bartonella coopersplainsensis, Bartonella tribocorum, Bartonella rattimassiliensis, Bartonella silvatica, two uncultured Bartonella spp., Babesia microti and diverse Hepatozoon were identified in six rodent species. More importantly, six species (including two Anaplasma, two Bartonella, "Ca. N. mikurensis" and Bab. microti) are zoonotic pathogens except Anaplasma bovis and Anaplasma ovis with zoonotic potential. Furthermore, dual infection was observed between different microorganisms, and the most common type of co-infection is between "Ca. N. mikurensis" and other microorganisms. Additionally, potential novel Bartonella species and Hepatozoon species demonstrated the presence of more diverse rodent-associated Bartonella and Hepatozoon.ConclusionsThe results in this work indicated great genetic diversity of vector-borne infections in wild rodents, and highlighted the potential risk of human pathogens transmitted from rodents to humans through vectors.

AbstractPrevious studies were mostly conducted based on two-dimensional feature space to monitor salinization, while studies on dense long-term salinization monitoring based on three-dimensional feature space have not been reported. Based on Landsat TM/ETM+/OLI images and three-dimensional feature space method, this study introduced six typical salinization surface parameters, including NDVI, salinity index, MSAVI, surface albedo, iron oxide index, wetness index to construct eight different feature space monitoring index. The optimal soil salinization monitoring index model was proposed base on field observed data and then the evolution process of salinization in Yellow River Delta (YRD) were analyzed and revealed during 1984–2022. The salinization monitoring index model of MSAVI-Albedo-IFe2O3 feature space had the highest accuracy with R2 = 0.93 and RMSE = 0.678g/kg. The spatial distribution of salinization in YRD showed an increasing trend from inland southwest to coastal northeast and the salinization intensity showed an increasing trend during 1984–2022 due to the implements of agricultural measures such as planting salt-tolerant crops, microbial remediation and fertility improvement. The rate of salinization deterioration in the northeast part was greater than others. Zones of salinization improvement were mainly located in cultivated land of the southwest parts.

Abstract A molecular surveillance of tick-borne diseases was performed in Hulunbuir City, Inner Mongolia. A total of 149 ticks including three species (Ixodes persulcatus, Haemaphysalis concinna, and Dermacentor silvarum) were collected. As many as 11 tick-borne bacterial pathogens were identified in them. Some of them have high positive rates. For example, Candidatus Rickettsia tarasevichiae was detected with a high prevalence of 72.48%, while Candidatus Lariskella sp. was detected in 31.54% of ticks. For both Rickettsia raoultii and Anaplasma phagocytophilum, two distinct genotypes were identified based on their phylogenetic trees based on 16S rRNA, gltA, and groEL sequences. Remarkable genetic diversity was also observed for 16S and flaB genes of Borreliella garinii, an agent of Lyme disease. Rickettsia heilongjiangensis causing Far-Eastern spotted fever (2.68%, 4/149), Ehrlichia muris causing human ehrlichiosis (4.70%, 7/149), Borrelia miyamotoi causing relapsing fever (2.01%, 3/149), and Borreliella afzelii causing Lyme disease (2.01%, 3/149) were also detected. Additionally, a previously uncharacterized Anaplasma species closely related to Anaplasma ovis was identified. Herein we name it “Candidatus Anaplasma mongolica”. Based on these results, we propose that Yakeshi City might be a potential hotspot of tick-borne diseases.

Japanese spotted fever (JSF) is caused by Rickettsia japonica, mainly vectored by hard ticks. However, whether R. japonica can be transmitted by other arthropods remains unknown. Moreover, it is of interest to investigate whether other Rickettsia species cause spotted fever in endemic areas. In this study, a survey of Rickettsia species was performed in hematophagous arthropods (mosquitoes, tabanids, and ticks) from endemic areas for JSF in Hubei Province, central China. The results showed that the diversity and prevalence of Rickettsia species in mosquitoes are low, suggesting that mosquitoes may not be the vector of zoonotic Rickettsia species. A novel Rickettsia species showed a high prevalence (16.31%, 23/141) in tabanids and was named “Candidatus Rickettsia tabanidii.” It is closely related to Rickettsia from fleas and mosquitoes; however, its pathogenicity in humans needs further investigation. Five Rickettsia species were identified in ticks. Rickettsia japonica, the agent of JSF, was detected only in Haemaphysalis longicornis and Haemaphysalis hystricis, suggesting that they may be the major vectors of R. japonica. Notably, two novel species were identified in H. hystricis ticks, one belonging to the spotted fever group and the other potentially belonging to the ancestral group. The latter one named “Candidatus Rickettsia hubeiensis” may provide valuable insight into the evolutionary history of Rickettsia.

ObjectiveGastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients.MethodsData of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People’s Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients’ clinical prognosis.ResultsLasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities.ConclusionMachine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.

Tick-borne microorganisms in many tick species and many areas of China are still not thoroughly investigated. In this study, 224 ticks including two species (Haemaphysalis longicornis and Haemaphysalis qinghaiensis) were collected from four cities in Hebei, Shandong, and Qinghai provinces, China. Ticks were screened for the presence of tick-borne bacterial microorganisms including Rickettsia, Anaplasmataceae (Anaplasma, Ehrlichia, Neoehrlichia, etc.), Coxiella, Borrelia, and Bartonella. Two Anaplasma species (Anaplasma ovis and Anaplasma capra) were detected in H. longicornis from Xingtai City of Hebei Province, with a positive rate of 3 % and 8 %, respectively. A Coxiella species was detected in H. longicornis ticks from all three locations in Hebei and Shandong provinces, with the positive rate ranging from 30 to 75 %. All the 16S and rpoB sequences were very similar (99.77–100 % identity) to Coxiella endosymbiont of Haemaphysalis ticks. An Ehrlichia species was detected in H. qinghaiensis (6/66, 9 %) from Xining City, Qinghai Province. The 16S and groEL sequences had 100 % and 97.40–97.85 % nucleotide identities to “Candidatus Ehrlichia pampeana” strains, respectively, suggesting that it may be a variant of “Candidatus Ehrlichia pampeana”. All the ticks were negative for Rickettsia, Borrelia, and Bartonella. Because all the ticks were removed from goats or humans and were partially or fully engorged, it is possible that the microorganisms were from the blood meal but not vectored by the ticks. Our results may provide some information on the diversity and distribution of tick-borne pathogens in China.

Monitoring and evaluation of soil ecological environments are very important to ensure saline–alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent need for high-performance prediction of soil properties using multi-dimensional data in the WebGIS system. To this end, this study aims to develop a saline–alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction, and data management. The system visually displays the statistical results of air quality, vegetation index, and soil properties in the study area. It provides users with ecological risk assessment functions to analyze heavy metal pollution in the soil. Specially, the system introduces a tree-structured Parzan estimator (TPE)-optimized machine learning model to achieve accurate prediction of soil salinity. The TPE–RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE–GBDT model, which exhibited a strong nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline–alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline–alkali soil ecological protection.

As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification.

Objectives This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice. Methods Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. Results Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive. Conclusion For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.

AbstractLandslides have a great impact on the normal traffic of highway, and maintaining the normal traffic of highway is the foundation of economic development, so landslide susceptibility mapping is very important. In this study, four counties, which locate in the central Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China, are taken as the research region. Based on the 190 historical landslide disaster points in the region, six factors-elevation, slope, aspect, plan curvature, profile curvature and TWI (Topographic Wetness Index) - are finally selected for calculation. A landslide disaster is evaluated by two single models of CF (Certainty Factors) and IV (Information Value) models and four coupling models of CF-AHP (Analytic Hierarchy Process), CF-LR (Logistic Regression), IV-AHP and IV-LR models. The accuracy of the six models is evaluated by the ROC (Receiver Operating Characteristic) curve and the Sridevi Jadi parameters. The IV-AHP model has the highest value of 0.9189, which indicates that the IV-AHP model is more appropriate for landslide disaster assessment in the whole region. In the Sridevi Jadi parameters, the IV model have the highest value of 0.8696, showing that the IV model have the highest accuracy in landslide susceptibility assessment in high- and very high-susceptibility regions.

Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) have emerged as a new mode of intercellular crosstalk and are responsible for many of the therapeutic effects of MSCs. To promote the application of MSC-EVs, recent studies have focused on the manipulation of MSCs to improve the production of EVs and EV-mediated activities. The current paper details an optimization method using non-invasive low-intensity pulsed ultrasound (LIPUS) as the stimulation for improving oral MSC-EV production and effectiveness. Stem cells from apical papilla (SCAP), a type of oral mesenchymal stem cell, displayed intensity-dependent pro-osteogenic and anti-inflammatory responses to LIPUS without significant cytotoxicity or apoptosis. The stimuli increased the secretion of EVs by promoting the expression of neutral sphingomyelinases in SCAP. In addition, EVs from LIPUS-induced SCAP exhibited stronger efficacy in promoting the osteogenic differentiation and anti-inflammation of periodontal ligament cells in vitro and alleviating oral inflammatory bone loss in vivo. In addition, LIPUS stimulation affected the physical characteristics and miRNA cargo of SCAP-EVs. Further investigations indicated that miR-935 is an important mediator of the pro-osteogenic and anti-inflammatory capabilities of LIPUS-induced SCAP-EVs. Taken together, these findings demonstrate that LIPUS is a simple and effective physical method to optimize SCAP-EV production and efficacy.

Objectives: From March to June 2021, the reported number of clinically diagnosed endemic typhus in Anhui and Hubei provinces of China nearly increased four-fold compared with the monthly average numbers in last 5 years. An etiological and epidemiological investigation was initiated. Methods: The clinical specimens from the reported patients and the potential vector ticks were collected for molecular and serological detection, as well as cell culturing assay to identify the potential pathogen. Results: Polymerase chain reaction and sequence analysis of rrs and groEL showed that the pathogen from these patients was Ehrlichia sp., isolated from Haemaphysalis longicornis attached to these patients. The phylogenetic analysis based on 39 Ehrlichia genomes suggested that it should be taxonomically classified as a novel species, tentatively named “Candidatus Ehrlichia erythraense”. A total of 19 of 106 cases were confirmed as Candidatus Ehrlichia erythraense infections by polymerase chain reaction, sequencing, and/or serological tests. The most frequent symptoms were fever (100%), rashes (100%), asthenia (100%), anorexia (100%), and myalgia (79%). Conclusion: The occurrence of the disease presenting with fever and rashes in Anhui and Hubei provinces was caused by a novel species of the genus Ehrlichia; physicians need to be aware of this newly-discovered pathogen to ensure appropriate testing, treatment, and regional surveillance.

In this paper, a delayed prey-predator eco-epidemiological model with the nonlinear media is considered. First, the positivity and boundedness of solutions are given. Then, the basic reproductive number is showed, and the local stability of the trivial equilibrium and the disease-free equilibrium are discussed. Next, by taking the infection delay as a parameter, the conditions of the stability switches are given due to stability switching criteria, which concludes that the delay can generate instability and oscillation of the population through Hopf bifurcation. Further, by using normal form theory and center manifold theory, some explicit expressions determining direction of Hopf bifurcation and stability of periodic solutions are obtained. What's more, the correctness of the theoretical analysis is verified by numerical simulation, and the biological explanations are also given. Last, the main conclusions are included in the end.

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors.

Revealing the spatial–temporal evolution of carbon storage and its driving mechanisms in the Qinghai–Tibet Plateau could provide support for decision making in the protection of regional ecosystems and the achievement of regional dual-carbon goals. In this study, the spatial–temporal evolution of carbon storage in the Qinghai–Tibet Plateau was analyzed under various scenarios using PLUS-InVEST and a gravity center model, and the driving mechanisms of carbon storage were clarified with Geodetector. The results are as follows: (1) During 2000–2020, the areas of coniferous forest, evergreen broad-leaved forest, closed shrub, temperate shrub desert, multi-tree grassland, and grassland showed an increasing trend, while the areas of deciduous broad-leaved forest and mixed forest showed a decreasing trend. (2) During 2030–2060, there was a decreasing trend in the total carbon storage of the Qinghai–Tibet Plateau under three different scenarios. (3) During 2030–2060, the area of the Qinghai–Tibet Plateau was mostly represented by carbon balance (56%), while the areas of carbon sources and carbon sinks showed a scattered distribution. (4) The precipitation and topographic factors with a q value of 0.888 played a dominant role in affecting the spatio-temporal variations in carbon storage in the Qinghai–Tibet Plateau. (5) In future ecological protection and restoration efforts, more high-quality farmlands should be protected and constructed, which could contribute to the achievement of dual-carbon goals. In addition, the hydrothermal conditions should be improved to aid the carbon cycle process in the Qinghai–Tibet Plateau.

IntroductionChilling injury is one of the most common meteorological disasters affecting cucumber production. For implementing remedial measures as soon as possible to minimize production loss, a timely and precise assessment of chilling injury is crucial.MethodsTo evaluate the possibility of detecting cucumber chilling injury using chlorophyll fluorescence (ChlF) technology, we investigated the continuous changes in ChlF parameters under various low-temperature conditions and created the criteria for evaluating chilling injury. The ChlF induction curves were first collected before low-temperature as unstressed samples and daily 1 to 5 days after low-temperature as chilling injury samples. Principal component analysis was employed to investigate the public information on ChlF parameters and evaluate the differences between samples with different degrees of chilling injury. The parameters (Fv/Fm, Y(NO), qP, and Fo) accounted for a large proportion in the principal components and could characterize chilling injury. Uniform manifold approximation and projection method was employed to extract new features (Feature 1, Feature 2, Feature 3, and Feature 4) from ChlF parameters for subsequent classification model. Taking four features as input, a classification model based on the Fuzzy C-means clustering algorithm was constructed in order to identify the chilling injury classes of cucumber seedlings. The cucumber seedlings with different chilling injury classes were analyzed for ChlF images, rapid light curves, and malondialdehyde content.Results and discussionThe results demonstrated that the variations in these indicators among the different chilling injury classes supported the validity of the classification model. Our findings provide a better understanding of the relationship between ChlF parameters and the impact of low-temperature treatment on cucumber seedlings. This finding offers an additional perspective that can be used to evaluate the responses and damage that plants experience under stress.

Background: The blood-feeding behavior of kissing bugs (subfamily Triatominae, family Reduviidae, order Hemiptera) means they are potential vectors of multiple humans pathogens. However, investigations of vector-borne pathogens harbored by kissing bugs are rare. Methods: In the current study, 22 adult kissing bugs (Triatoma rubrofasciata) were captured in Huizhou City, Guangdong Province, south China. The presence of vector-borne pathogens in the kissing bugs was tested, and the genetic diversity of these potential pathogens was investigated. Results: All the kissing bugs were negative for Anaplasmataceae bacteria, Rickettsia, and Coxiella. Bartonella DNA was detected in 36.4% (8/22) of the kissing bugs. The sequences of the Bartonella gltA genes divided into two clades in a phylogenetic tree, with close relationships to B. tribocorum and uncultured Bartonella sp. clone MYR-283, respectively. All the groEL sequences were closely related to those of B. kosoyi (identity 98.75%–100%). The ftsZ and rpoB sequences were most closely related to those of B. elizabethae, a recognized human pathogen, with nucleotide similarities of 98.70%–100% and 99.45%–100%, respectively. Conclusions: We report the detection of Bartonella DNA in Triatoma kissing bugs in southern China. Although the sample size is limited, the high positive rate of detection of Bartonella DNA, the close relationship of the gene sequences to those of zoonotic Bartonella species, and the distribution of the kissing bugs near human residences, hint at a risk to public health.

Abstract Periodontitis is the most widespread oral disease and is closely related to the oral microbiota. The oral microbiota is adversely affected by some pharmacologic treatments. Systemic antibiotics are widely used for infectious diseases but can lead to gut dysbiosis, causing negative effects on the human body. Whether systemic antibiotic-induced gut dysbiosis can affect the oral microbiota or even periodontitis has not yet been addressed. In this research, mice were exposed to drinking water containing a cocktail of four antibiotics to explore how systemic antibiotics affect microbiota pathogenicity and oral bone loss. The results demonstrated, for the first time, that gut dysbiosis caused by long-term use of antibiotics can disturb the oral microbiota and aggravate periodontitis. Moreover, the expression of cytokines related to Th17 was increased while transcription factors and cytokines related to Treg were decreased in the periodontal tissue. Fecal microbiota transplantation with normal mice feces restored the gut microbiota and barrier, decreased the pathogenicity of the oral microbiota, reversed the Th17/Treg imbalance in periodontal tissue, and alleviated alveolar bone loss. This study highlights the potential adverse effects of long-term systemic antibiotics-induced gut dysbiosis on the oral microbiota and periodontitis. A Th17/Treg imbalance might be related to this relationship. Importantly, these results reveal that the periodontal condition of patients should be assessed regularly when using systemic antibiotics in clinical practice.

Freeze–thaw erosion is one of the three major soil erosions in China, including wind erosion and hydraulic erosion, which leads to the destruction of the natural environment, the imbalance of economic development, a threat to personal safety, and irreversible disaster to the country and people. China’s permafrost area accounts for about one-fifth of the country’s land area, and the seasonal permafrost area accounts for half of China’s land area, mainly concentrated in the Qinghai–Tibet Plateau, Xinjiang Province, Heilongjiang Province, Gansu Province, and other regions. In order to establish an evaluation system for the ecological function reserve of the Greater Hinggan Mountains, nine evaluation indicators were selected from the perspectives of topography, climate, soil, and vegetation conditions. GIS technology, a multiple collinearity test, and principal component analysis were used to comprehensively evaluate the freeze–thaw erosion in the ecological function reserve of the Greater Hinggan Mountains. This study categorized the evaluation results into five intensity levels, from micro to severe. Finally, the degree of influence of different influencing factors on freeze–thaw erosion and the interactions between the factors were analyzed using a geographic detector. The results showed the following: (1) The intensity of freeze–thaw erosion in the study area gradually increased from west to east, and the comprehensive evaluation results were −0.2552 to 0.7581. Overall, moderate, severe, and mild erosion accounted for 29.83%, 25.9%, and 21.54% of the total area of the freeze–thaw zone, respectively. (2) Soil moisture content and the annual range in temperature were the main factors contributing to freeze–thaw erosion. The degree of influence of the two effects on freeze–thaw erosion (q = 0.5997) was better than that of the single-factor effect.

Trapped air bubbles in pipelines significantly affect the safety and efficiency of water supply systems, potentially inducing water hammer and leading to pipeline explosions and interruptions. This study investigates the sweeping velocity of air bubbles in horizontal pipelines by analyzing the forces acting on a single air bubble and deriving a theoretical expression for calculating the sweeping velocity. Physical model experiments and numerical simulations are conducted to examine the variations in sweeping velocity with respect to bubble volume and dimensionless bubble volume. Results indicate that the sweeping velocity increases with bubble volume, and the diameter of the pipeline significantly affects the movement of air bubbles. These findings contribute to a better understanding of air bubble behavior in horizontal pipelines and can inform strategies for mitigating risks associated with trapped air bubbles, ultimately improving the safety and reliability of water supply systems.

Abstract Background Cuproptosis (copper death) is a recently found cell death type produced by copper iron; nonetheless, the properties of cuproptosis molecular subtypes and possible involvement of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) in ovarian cancer (OC) remain unknown. Methods CRG changes were characterized at the genomic and transcriptional levels in 656 OC samples, and their expression patterns were investigated using three different datasets. Results We identified three distinct molecular subtypes, and discovered that variations in molecular subtypes were linked to patient prognosis, TME cell infiltration characteristics, malignancy, and immune-related pathways. Then, in order to predict overall survival (OS), we created a risk score and tested its predictive potential in OC patients. As a result, we created a very accurate nomogram to increase risk score clinical applicability. Better OS, younger age, early stage, and immune activity were all associated with a low risk score. The hallmarks of a high-risk score are older age, advanced stage, immunosuppression, and a bad prognosis. Furthermore, risk score was linked to immune checkpoint expression (including PD-L1, CTLA4), targeted therapy gene expression (PARP, PDGFRA), cancer stem cell (CSC), chemotherapy and targeted medication sensitivity. Conclusions Our comprehensive analysis of CRGs in OC showed their potential role in TME, clinicopathological characteristics, chemotherapy and targeted drug screening and prognosis. These discoveries could help us better understand CRGs in OC, as well as pave the path for novel ways to assess prognosis and design more effective immunotherapy strategies.

Floods have become increasingly frequent and pose more significant risks in delta plains due to rapid urbanization. While previous studies have primarily focused on urban flood management, there has been a limited exploration of coordinated flood control strategies that encompass cities, regions, and basins holistically. In response to this gap, our research aims to quantitatively assess flood control coordination under various scheduling rules and propose optimized strategies to enhance this coordination. Using the Wu-Cheng-Xi-Yu region as our case study, we observed that flood characteristics under different flood control coordination schemes varied slightly, especially the FI. Moreover, the effectiveness of different scheduling optimization schemes varied across different levels. Specifically, at the basin–region, basin–city, and region–city levels, Schemes S8, S7, and S5 demonstrated the highest coordination coefficients, with values of 0.80, 0.67, and 0.68, respectively. In comparison to the current scheduling Scheme (S0), these schemes resulted in significant improvements in flood coordination, with enhancements of 0.68, 0.37, and 0.22, respectively. Furthermore, our findings indicate that the most effective approach to strengthening flood control coordination involves implementing scheduling measures that reduce urban drainage while simultaneously improving the discharge capacity of the basin. Our results could help to alleviate the contradictions of flood control at different levels and provide a solid guarantee for water security.

Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator–machine learning–ordinary kriging (TPE–ML–OK)) to predict spatial variability of soil NH3 from Sentinel-2 remote sensing image and air quality data. In TPE–ML–OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters’ high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH3 flux. We found a weak linear correlation between soil NH3 flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE–XGB–OK method exhibited the highest predicted accuracy (R2 = 85.97%) for soil NH3 flux in comparison with other models. The spatial mapping results based on TPE–ML–OK methods showed that the high fluxes of soil NH3 were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH3 were O3, SO2, PM10, CO, and NDWI. The above results demonstrate the powerful linear–nonlinear interpretation ability between soil NH3 and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality.

Haemaphysalis longicornis is one of the most dominant and widespread tick species in China. This species mainly infests wild animals and occasionally attacks humans, and has been associated with the transmission of a variety of zoonotic pathogens including spotted fever group Rickettsia (SFGR), severe fever with thrombocytopenia syndrome virus (SFTSV), Anaplasma phagocytophilum, Babesia spp. and Theileria spp.. Although there are increasing reports of various pathogens associated with H. longicornis, some neglected pathogens in certain areas still need to be studied. In this study, a total of 171 H. longicornis ticks were collected from goats in three locations of Shandong Province, Eastern China (Zibo, Linyi, and Qingdao cities), and subsequently screened for the presence of Rickettsia, Anaplasma, and Ehrlichia bacteria. A total of four bacterial species were identified and characterized. “Candidatus Rickettsia jingxinensis” was detected in one tick specimen from Zibo city. Of 98 ticks from Linyi city, 63.27% (62/98) were tested positive for Anaplasma capra and 5.10% (5/98) were positive for Anaplasma bovis. Interestingly, a novel Anaplasma species was detected and characterized in one tick specimen from Zibo and one other from Linyi, respectively. Genetic and phylogenetic analysis based on the 16S, gltA, groEL, and msp4 genes indicated that it was divergent from all known Anaplasma species but mostly related to A. phagocytophilum and “Cadidatus Anaplasma boleense”. Based on where it was first detected, we named it “Candidatus Anaplasma shandongensis”.

BackgroundCardiovascular disease (CVD) poses a tremendous threat to global health, giving rise to exceedingly high morbidity and mortality among patients. A migraine is a common neurological disorder characterized by recurrent attacks of severe headache, while its cardiovascular burden remains unclear. Therefore, this study aims to investigate whether migraine is associated with CVD.MethodsThe cross-sectional data of 5,692 subjects aged 20 or above was obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2004. To determine whether migraine is associated with CVD, weighted logistic regression models were used. In a subsequent subgroup analysis, several confounding factors were also explored to investigate the association between migraine and CVD.ResultsIn total, 5,692 subjects were enrolled in this study, with the prevalence of CVD being 13.3%. Participants with CVD tended to be older, male, non-Hispanic whites, more educated, former smokers, and alcohol drinkers, and had a higher waist circumference, less physical activity, a higher level of triglyceride and creatinine as well as a lower level of high-density lipoprotein cholesterol (HDL-C) and estimated glomerular filtration rate (eGFR) (all P < 0.05). Considering all potential confounders, migraine was associated with a higher risk of CVD [odds ratios (ORs) 2.77; 95% confidence intervals (CIs): 1.56–4.90]. Subgroup analysis showed a higher risk of CVD in females, those older than 60 years, with a lower body mass index (BMI) level (≤ 30 kg/m2), a higher level of eGFR (> 90 mL/min/1.73 m2), hypertension and hyperlipidemia and without diabetes.ConclusionIn summary, our study revealed a positive association between migraine with CVD in a nationally representative US adult population. Our findings highlighted that migraine should be considered an important risk factor for CVD.

BackgroundDiabetes is a critical contributor to the pathogenesis of cardiovascular diseases. Klotho is an anti−aging protein with cardiovascular-renal protective effects. However, the relationship between serum Klotho levels and diabetes remains poorly understood.ObjectivesThis study aimed to investigate the relationship between serum Klotho levels and diabetes in US adults.MethodsWe analyzed the cross-sectional data obtained from 13751 subjects aged 40-79 years in the National Health and Nutrition Examination Survey (NHANES) (2007–2016). Serum Klotho concentration was measured using an enzyme-linked immunosorbent assay (ELISA) and categorized into four quartiles (Q1-Q4). Multivariate logistic regression and restricted cubic spline (RCS) regression were conducted to explore the association between serum Klotho levels and the prevalence of diabetes.ResultsAs compared with quartile 1, serum Klotho levels in quartiles 2-4 yielded odds ratios (OR) (95% CI) of diabetes of 0.96 (0.80–1.15), 0.98 (0.82–1.18), and 1.25 (1.04–1.50), respectively, after covariate adjustment (P for trend = 0.018). The results implied an increased risk of diabetes. The RCS plot showed a U-shaped relationship linking serum Klotho levels with diabetes (P for nonlinearity = 0.003).ConclusionsIn summary, a nonlinear and positive association was found between serum Klotho levels and the prevalence of diabetes. Further study is needed to verify the causality of this association and elucidate the underlying mechanisms.

Aims Location Taxon Methods Results Main Conclusions Long‐term climate stability, contemporary climate and environmental heterogeneity have been linked to bird diversity patterns through their direct impacts on diversification rate, as well as through their indirect effects on plant species richness, which could also directly and indirectly affect bird diversity. This study aimed to quantitatively assess whether these potential drivers could indirectly affect bird species richness through their direct effects on the diversification rate in birds in eastern Asia.Mainland China.Birds.Using the distribution data of 1127 bird species across 320 prefecture cities in China and a phylogenetic tree of these bird species, we analysed the distribution patterns of bird species richness and diversification rate of all birds, passerine birds and non‐passerine birds, respectively. We also investigated their relationships with long‐term climate stability, contemporary climate, elevation range and plant diversity using ordinary least squares regression model and simultaneous autoregressive model. In addition, structural equation model was used to analyse whether these drivers could indirectly affect bird species richness through their direct effects on bird diversification rate.The results showed that bird species richness and diversification rate were highest in southwestern China. Additionally, diversification rate for passerine birds was higher than non‐passerine birds and all birds. Regression analyses revealed that plant species richness was the variable most associated with bird species richness and diversification rate across the three groups. Notably, results from structural equation model indicated that plant species richness, elevation range and glacial–interglacial climate change can indirectly influence bird species richness by directly affecting diversification rates.These findings emphasize the importance of considering diversification rate when understanding the geographic distribution patterns of bird diversity. The direct and indirect effects of plant species richness, elevation range and glacial–interglacial climate change on bird diversity highlight the crucial role of mountain regions with higher plant diversity and stable paleoclimate in forming and maintaining biodiversity. [ABSTRACT FROM AUTHOR]