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Abstract Necrotizing fasciitis (NF) is a rare and life-threatening serious infectious disease, characterized by acute onset and rapid progress, leading to extensive necrosis of skin, soft tissue as well as fascia by a variety of aerobic and anaerobic bacteria, localized on external genitalia, scrotum, groin and perianal areas in males. There exist numerous common etiologies for NF, yet NF induced by malignant neoplasms is exceedingly rare. Several studies have reported that NF may be associated with tumor site (rectal/sigmoid colon cancer) and blood supply dysfunction caused by targeted therapy drugs (bevacizumab, aflibercept, ramucirumab). The perforation of colorectal cancer poses a unique risk factor for NF. However, in our two cases, the patient with rectal cancer received CapeOX (oxaliplatin + capecitabine) + bevacizumab + tislelizumab for 3 cycles without perforation but did develop NF. One month after debridement, the patient continued immunotherapy with tislelizumab alone for the fourth cycle and maintained for an additional 3 cycles without any recurrence of NF. Therefore, does the occurrence of NF correlate with the tumor site (rectum) and targeted immunotherapy? Another patient with hepatocellular carcinoma also developed NF after receiving 2 cycles of lenvatinib + sintilimab treatment. The third cycle of sintilimab immunotherapy was administered on the 13th day after operation, which was subsequently maintained for an additional 2 cycles without recurrence of NF. The absence of a direct correlation between hepatocellular carcinoma and rectal tumor location as well as immunotherapy, suggests that NF may be closely linked to targeted therapy.

Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 106 t, 700.33 × 106 t, 697.22 × 106 t, 696.03 × 106 t, and 691.21 × 106 t, respectively. This represents changes of 3.69 × 106 t, 1.69 × 106 t, −1.49 × 106 t, −2.68 × 106 t, and −7.43 × 106 t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. This research provides new insights into land redistribution, economic strategies, and achieving carbon neutrality.

The incidence of colorectal cancer is relatively high in our country, with the majority of patients being diagnosed at an advanced stage. For individuals with advanced-stage colorectal cancer, conversion or neoadjuvant therapy is frequently necessitated to facilitate surgical intervention and achieve a curative effect. And about 10% to 30% of colon cancer patients are complicated with intestinal obstruction. Surgical intervention remains the primary treatment for managing intestinal obstructions, albeit with a considerable risk of perioperative mortality and an increased likelihood of postoperative complications. PDT, as a neoadjuvant treatment for colon cancer, can shrink the local tumor and relieve obstruction, and is effective in colon cancer combined with obstruction. Robotic surgery has the advantages of high stability and low trauma, and compared with laparoscopic colon cancer surgery, robotic surgery can achieve better results. Fluorescent laparoscopic clarifies the location and size of the tumor lesion, allowing for greater precision when removing colon cancer lesions in robotic surgery. Therefore, in the treatment of colon cancer, PDT can offer an opportunity for surgery after relieving obstruction in patients with obstructive colon cancer. Additionally, when combined with fluorescent laparoscopic robotic colon cancer surgery, it provides a novel treatment approach for patients with obstructive colon cancer. Preoperative photodynamic neoadjuvant therapy combined with robotic colon cancer surgery has not yet been reported. Here, we report a case of colon cancer with obstruction, preoperative TNM stage was T4N1, and the lesion had caused intestinal stenosis. After four sessions of PDT, the patient’s intestinal lumen was unobstructed and the lesion had regressed. After evaluation, fluorescent laparoscopic localization and visualization of lymph nodes combined with robotic colon cancer resection were performed. Postoperative pathology showed that the patient’s tumor regression grade was grade 1. The patient’s tumor was completely resected with good resection effect. No tumor invasion was found on both sides of the resection margin, and the patient did not relapse after surgery.

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings in feature selection, hyperparameter tuning, underlying surface heterogeneity, and explainability. Addressing these challenges, this study leveraged extensive FVC field data from the Qinghai-Tibet Plateau. Initially, a feature selection algorithm combining genetic algorithms and XGBoost was proposed. This algorithm was integrated with the Optuna tuning method, forming the GA-OP combination to optimize feature selection and hyperparameter tuning in machine learning. Furthermore, comparative analyses of various machine learning models for FVC inversion in alpine grassland were conducted, followed by an investigation into the impact of the underlying surface heterogeneity on inversion performance using the NDVI Coefficient of Variation (NDVI-CV). Lastly, the SHAP (Shapley Additive exPlanations) method was employed for both global and local interpretations of the optimal model. The results indicated that: (1) GA-OP combination exhibited favorable performance in terms of computational cost and inversion accuracy, with Optuna demonstrating significant potential in hyperparameter tuning. (2) Stacking model achieved optimal performance in FVC inversion for alpine grassland among the seven models (R2 = 0.867, RMSE = 0.12, RPD = 2.552, BIAS = −0.0005, VAR = 0.014), with the performance ranking as follows: Stacking > CatBoost > XGBoost > LightGBM > RFR > KNN > SVR. (3) NDVI-CV enhanced inversion performance and result reliability by excluding data from highly heterogeneous regions that tended to be either overestimated or underestimated. (4) SHAP revealed the decision-making processes of the Stacking and CatBoost models from both global and local perspectives. This allowed for a deeper exploration of the causality between features and targets. This study developed a high-precision FVC inversion scheme, successfully achieving accurate FVC inversion on the Qinghai-Tibet Plateau. The proposed approach provides valuable references for other ecological parameter inversions.

Abstract Immunotherapy has now garnered significant attention as an essential component in cancer therapy during this new era. However, due to immune tolerance, immunosuppressive environment, tumor heterogeneity, immune escape, and other factors, the efficacy of tumor immunotherapy has been limited with its application to very small population size. Energy metabolism not only affects tumor progression but also plays a crucial role in immune escape. Tumor cells are more metabolically active and need more energy and nutrients to maintain their growth, which causes the surrounding immune cells to lack glucose, oxygen, and other nutrients, with the result of decreased immune cell activity and increased immunosuppressive cells. On the other hand, immune cells need to utilize multiple metabolic pathways, for instance, cellular respiration, and oxidative phosphorylation pathways to maintain their activity and normal function. Studies have shown that there is a significant difference in the energy expenditure of immune cells in the resting and activated states. Notably, competitive uptake of glucose is the main cause of impaired T cell function. Conversely, glutamine competition often affects the activation of most immune cells and the transformation of CD4+T cells into inflammatory subtypes. Excessive metabolite lactate often impairs the function of NK cells. Furthermore, the metabolite PGE2 also often inhibits the immune response by inhibiting Th1 differentiation, B cell function, and T cell activation. Additionally, the transformation of tumor-suppressive M1 macrophages into cancer-promoting M2 macrophages is influenced by energy metabolism. Therefore, energy metabolism is a vital factor and component involved in the reconstruction of the tumor immune microenvironment. Noteworthy and vital is that not only does the metabolic program of tumor cells affect the antigen presentation and recognition of immune cells, but also the metabolic program of immune cells affects their own functions, ultimately leading to changes in tumor immune function. Metabolic intervention can not only improve the response of immune cells to tumors, but also increase the immunogenicity of tumors, thereby expanding the population who benefit from immunotherapy. Consequently, identifying metabolic crosstalk molecules that link tumor energy metabolism and immune microenvironment would be a promising anti-tumor immune strategy. AMPK (AMP-activated protein kinase) is a ubiquitous serine/threonine kinase in eukaryotes, serving as the central regulator of metabolic pathways. The sequential activation of AMPK and its associated signaling cascades profoundly impacts the dynamic alterations in tumor cell bioenergetics. By modulating energy metabolism and inflammatory responses, AMPK exerts significant influence on tumor cell development, while also playing a pivotal role in tumor immunotherapy by regulating immune cell activity and function. Furthermore, AMPK-mediated inflammatory response facilitates the recruitment of immune cells to the tumor microenvironment (TIME), thereby impeding tumorigenesis, progression, and metastasis. AMPK, as the link between cell energy homeostasis, tumor bioenergetics, and anti-tumor immunity, will have a significant impact on the treatment and management of oncology patients. That being summarized, the main objective of this review is to pinpoint the efficacy of tumor immunotherapy by regulating the energy metabolism of the tumor immune microenvironment and to provide guidance for the development of new immunotherapy strategies.

Triclosan (TCS) is a broad-spectrum antibiotic widely used in various personal care products. Research has found that exposure to TCS can cause toxic effects on organisms including neurotoxicity, cardiotoxicity, disorders of lipid metabolism, and abnormal vascular development, and the corresponding toxic mechanisms are gradually delving into the level of abnormal expression of miRNA regulating gene expression. Although the downstream mechanism of TCS targeting miRNA abnormal expression to induce toxicity is gradually improving, its upstream mechanism is still in a fog. Starting from the abnormal expression data of circRNA in zebrafish larvae induced by TCS, this study conducted a hierarchical analysis of the expression levels of all circRNAs, differential circRNAs, and trend circRNAs, and identified 29 key circRNA events regulating miRNA abnormal expression. In combination with GO and KEGG, the effects of TCS exposure were analyzed from the function and signaling pathway of the corresponding circRNA host gene. Furthermore, based on existing literature evidence about the biological toxicity induced by TCS targeting miRNA as data support, a competing endogenous RNAs (ceRNA) network characterizing the regulatory relationship between circRNA and miRNA was constructed and optimized. Finally, a comprehensive Adverse Outcome Pathway (AOP) framework of multiple levels of events including circRNA, miRNA, mRNA, pathway, and toxicity endpoints was established to systematically elucidate the toxic mechanism of TCS. Moreover, the rationality of the AOP framework was verified from the expression level of miRNA and adverse outcomes such as neurotoxicity, cardiotoxicity, oxidative stress, and inflammatory response by knockdown of circRNA48. This paper not only provides the key circRNA events for exploring the upstream mechanism of miRNA regulating gene expression but also provides an AOP framework for comprehensively demonstrating the toxicity mechanism of TCS on zebrafish, which is a theoretical basis for subsequent hazard assessment and prevention and control of TCS.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high-precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of high-precision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking-DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen’s slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking-DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high-precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.

Abstract The assembly of oligopeptide and polypeptide molecules can reconstruct various ordered advanced structures through intermolecular interactions to achieve protein-like biofunction. Here, we develop a “molecular velcro”-inspired peptide and gelatin co-assembly strategy, in which amphiphilic supramolecular tripeptides are attached to the molecular chain of gelatin methacryloyl via intra-/intermolecular interactions. We perform molecular docking and dynamics simulations to demonstrate the feasibility of this strategy and reveal the advanced structural transition of the co-assembled hydrogel, which brings more ordered β-sheet content and 10-fold or more compressive strength improvement. We conduct transcriptome analysis to reveal the role of co-assembled hydrogel in promoting cell proliferation and chondrogenic differentiation. Subcutaneous implantation evaluation confirms considerably reduced inflammatory responses and immunogenicity in comparison with type I collagen. We demonstrate that bone mesenchymal stem cells-laden co-assembled hydrogel can be stably fixed in rabbit knee joint defects by photocuring, which significantly facilitates hyaline cartilage regeneration after three months. This co-assembly strategy provides an approach for developing cartilage regenerative biomaterials.

In this work, flexible Cu2−xS films on nylon membranes are prepared by combining a simple hydrothermal synthesis and vacuum filtration followed by hot pressing. The films consist of Cu2S and Cu1.96S two phases with grain sizes from nano to submicron. Doping Se on the S site not only increases the Cu1.96S content in the Cu2−xS to increase carrier concentration but also modifies electronic structure, thereby greatly improves the electrical properties of the Cu2−xS. Specifically, an optimal composite film with a nominal composition of Cu2−xS0.98Se0.02 exhibits a high power factor of ~150.1 μW m−1 K−2 at 300 K, which increases by ~138% compared to that of the pristine Cu2−xS film. Meanwhile, the composite film shows outstanding flexibility (~97.2% of the original electrical conductivity is maintained after 1500 bending cycles with a bending radius of 4 mm). A four-leg flexible thermoelectric (TE) generator assembled with the optimal film generates a maximum power of 329.6 nW (corresponding power density of 1.70 W m−2) at a temperature difference of 31.1 K. This work provides a simple route to the preparation of high TE performance Cu2−xS-based films.

The preservation of biodiversity is crucial for maintaining ecological balance and promoting the harmonious development of mankind and nature. To formulate a biodiversity conservation plan for Guilin in China and accurately evaluate the impact of conservation measures on regional biodiversity, this study combined the InVEST model (v 3.7.0), the GeoSOS-FLUS model (v 2.3), and the landscape pattern index, analyzing the spatiotemporal changes in biodiversity from 2000 to 2020 in Guilin and simulating biodiversity under different development scenarios in 2040. The results showed the following: (1) The biodiversity index in Guilin displayed a declining trend, with the average annual value decreasing from 0.875 in 2000 to 0.870 in 2020. The area experiencing a reduction in biodiversity was primarily concentrated around the city. (2) The biodiversity level of each district and county had a large spatiotemporal difference, with the overall distribution characteristics of “higher in the northwest, southwest, and east, and lower in the northeast, southeast and central”. (3) The biodiversity hotspots were identified as priority areas for conservation; woodland and wetland were effectively protected, and the expansion of construction land and arable land was limited under the ecological protection scenario compared with the natural development scenario. (4) The annual average value of the biodiversity index of Guilin could reach 0.872 in 2040 after the adoption of ecological conservation measures, which is 0.013 higher than the natural development scenario. The results of this study can provide guidance for the construction of a national sustainable development model city and land use planning in Guilin, as well as a scientific reference for the creation of biodiversity conservation policies.

Guanosine pentaphosphate and guanosine tetraphosphate are collectively called (p)ppGpp (Guanosine tetraphosphate and pentaphosphate). (p)ppGpp content in plants is affected by conditions such as light, salt, pH, UV light, and environmental phytohormones. The synthesis and hydrolysis of (p)ppGpp in plants is accomplished by a class of proteins called RSH (RelA/SpoT homologs). To date, a systematic and comprehensive genome-wide analysis of the RSH gene family in wheat and its closely related species has not been conducted. In this study, 15, 14, 12, and 8 members of RSH were identified in wheat (Triticum aestivum), Triticum dicoccoides, Triticum urartu and Aegilops tauschii respectively. Based on the conserved structural domains of the RSH genes, the TaRSHs have been categorized into TaRSH and TaCRSH. The gene duplications in the TaRSH gene family were all identified as segmental duplications indicating that the TaRSH family plays a significant role in expansion and that segmental duplications maintain a degree of genetic stability. Through the analysis of transcriptome data and RT-qPCR experiments, it was observed that the expression levels of TaRSHs were upregulated in response to abiotic stress. This upregulation suggests that TaRSHs play a crucial role in enhancing the resilience of wheat to adverse environmental conditions during its growth and development. Their increased expression likely contributes to the acquisition of stress tolerance mechanisms in wheat. Especially under NaCl stress, the expression levels increased most significantly. The more detailed systematic analysis provided in this article will help us understand the role of TaRSHs and provide a reference for further research on its molecular biological functions in wheat.

Abstract Background Silica nanoparticles (SiNPs) have been demonstrated to have beneficial effects on plant growth and development, especially under biotic and abiotic stresses. However, the mechanisms of SiNPs-mediated plant growth strengthening are still unclear, especially under field condition. In this study, we evaluated the effect of SiNPs on the growth and sugar and hormone metabolisms of wheat in the field. Results SiNPs increased tillers and elongated internodes by 66.7% and 27.4%, respectively, resulting in a larger biomass. SiNPs can increase the net photosynthetic rate by increasing total chlorophyll contents. We speculated that SiNPs can regulate the growth of leaves and stems, partly by regulating the metabolisms of plant hormones and soluble sugar. Specifically, SiNPs can increase auxin (IAA) and fructose contents, which can promote wheat growth directly or indirectly. Furthermore, SiNPs increased the expression levels of key pathway genes related to soluble sugars (SPS, SUS, and α-glucosidase), chlorophyll (CHLH, CAO, and POR), IAA (TIR1), and abscisic acid (ABA) (PYR/PYL, PP2C, SnRK2, and ABF), whereas the expression levels of genes related to CTKs (IPT) was decreased after SiNPs treatment. Conclusions This study shows that SiNPs can promote wheat growth and provides a theoretical foundation for the application of SiNPs in field conditions.

This review examines major bodies of literature, interrelated but usually considered separately, focused on work trajectories and their intersections with family dynamics through the life course. It begins with a consideration of the life course paradigm, which draws attention to the temporal dimensions of human lives, and recently developed analytic techniques that are well-suited to empirical investigation of life course transitions and trajectories over time. The review proceeds to examine empirical research on work career mobility (including both inter- and intra-generational mobility) measured as either trajectories of continuous outcomes or sequences of categorical outcomes, and their long-term consequences for socioeconomic attainment. Work-family trajectories are then addressed, focusing on the impacts of family on work, notably expressed in the motherhood wage penalty, and how family structure and processes affect long-term labor market outcomes. Research documents considerable heterogeneity in work-family dynamics over the life course across social groups with unequal resources. The review concludes with an assessment of the interplay of work and family trajectories studied longitudinally and makes recommendations for future research. It is argued that while extant studies of the work-family interface are compatible with, and sometimes deliberately reflect, a life course perspective, these bodies of research would benefit from more fully incorporating the life course principles of “agency” and “time and place”.

Abstract Background Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products. Methods This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated. Results (1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm). Conclusion This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

DUF668s, a plant-specific gene family, encode proteins containing domain of unknown function (DUF) domains. Despite their essential functions, there is a lack of insight into Triticum aestivum TaDUF668s. Here, 31 TaDUF668s were identified from the wheat genome; according to phylogenetic relationships, they were named TaDUF668-01 to TaDUF668-31. All TaDUF668s were hydrophilic and unstable proteins. There were 22 TaDUF668s that showed subcellular localization in nucleus. Evolutionary analysis demonstrated that TaDUF668s had undergone strong purifying selection, and fragment duplication plays major role in TaDUF668 family expansion. Cis-element prediction displayed that over 90% of TaDUF668 promoter regions contain the growth and abiotic responsiveness element. Consistently, expression profiling showed that TaDUF668s were highly induced in five wheat growth and development stages, seven main different tissues, five abiotic stresses, and five pathogenic stresses. In total, 12 TaDUF668s were targeted by 20 miRNAs through the inhibition of translation and cleavage patterns. RT-qPCR results confirmed that the expression of six TaDUF668s was significantly regulated by NaCl, PEG, F. graminearum, and P. striiformis; nevertheless, the regulation patterns were different. In summary, through systematic identification, characterization, evolutionary analysis, and expression profiling, a comprehensive understanding of TaDUF668 has been obtained, which lays a foundation for further functional studies of TaDUF668.

Long-time series global fractional vegetation cover (FVC) products have received widespread international publication, and they supply the essential data required for eco-monitoring and simulation study, assisting in the understanding of global warming and preservation of ecosystem stability. However, due to the insufficiency of high-precision FVC ground-measured data, the accuracy of these FVC products in some regions (such as the Qinghai–Tibet Plateau) is still unknown, which brings a certain impact on eco-environment monitoring and simulation. Here, based on current international mainstream FVC products (including GEOV1 and GEOV2 at Copernicus Global Land Services, GLASS from Beijing Normal University, and MuSyQ from National Earth System Science Data Center), the study of the dynamic change of vegetation cover and its influence factors were conducted in the three-rivers source region, one of the core regions on the Qinghai–Tibet Plateau, via the methods of trend analysis and partial correlation analysis, respectively. Our results found that: (1) The discrepancy in the eco-environment assessment results caused by the inconsistency of FVC products is reflected in the statistical value and the spatial distribution. (2) About 70% of alpine grassland in the three-rivers source region changing trend is controversial. (3) The limiting or driving factors of the alpine grassland change explained via different FVC products were significantly discrepant. Thus, before conducting these studies in the future, the uncertainties of the FVC products utilized should be validated first to acquire the fitness of the FVC products if the accuracy information of these products is unavailable within the study area. In addition, more high-precision FVC ground-measured data should be collected, helping us to validate FVC product uncertainty.

Analysis and prediction of urban ecological risk are crucial means for resolving the dichotomy between ecological preservation and economic development, thereby enhancing regional ecological security and fostering sustainable development. This study uses Nanning, a Chinese landscape garden city, as an example. Based on spatial granularity and extent perspectives, using 30 m land use data, the optimal scale for an ecological risk assessment (ERA) and prediction is confirmed. This study also explores the patterns of spatial and temporal changes in ecological risk in Nanning on the optimal scale. At the same time, the Patch-generating Land Use Simulation model is used to predict Nanning’s ecological risk in 2036 under two scenarios and to propose ecological conservation recommendations in light of the study results. The study results show that: a spatial granularity of 120 m and a spatial extent of 7 km are the best scales for ERA and prediction in Nanning. Although the spatial distribution of ecological risk levels is obviously different, the overall ecological risk is relatively low, and under the scenario of ecological protection in 2036, the area of high ecological risk in Nanning is small. The results can provide theoretical support for ERA and the prediction of landscape cities and ecological civilization construction.

When employing remote sensing images, it is challenging to classify vegetation species and ground objects due to the abundance of wetland vegetation species and the high fragmentation of ground objects. Remote sensing images are classified primarily according to their spatial resolution, which significantly impacts the classification accuracy of vegetation species and ground objects. However, there are still some areas for improvement in the study of the effects of spatial resolution and resampling on the classification results. The study area in this paper was the core zone of the Huixian Karst National Wetland Park in Guilin, Guangxi, China. The aerial images (Am) with different spatial resolutions were obtained by utilizing the UAV platform, and resampled images (An) with different spatial resolutions were obtained by utilizing the pixel aggregation method. In order to evaluate the impact of spatial resolutions and resampling on the classification accuracy, the Am and the An were utilized for the classification of vegetation species and ground objects based on the geographic object-based image analysis (GEOBIA) method in addition to various machine learning classifiers. The results showed that: (1) In multi-scale images, both the optimal scale parameter (SP) and the processing time decreased as the spatial resolution diminished in the multi-resolution segmentation process. At the same spatial resolution, the SP of the An was greater than that of the Am. (2) In the case of the Am and the An, the appropriate feature variables were different, and the spectral and texture features in the An were more significant than those in the Am. (3) The classification results of various classifiers in the case of the Am and the An exhibited similar trends for spatial resolutions ranging from 1.2 to 5.9 cm, where the overall classification accuracy increased and then decreased in accordance with the decrease in spatial resolution. Moreover, the classification accuracy of the Am was higher than that of the An. (4) When vegetation species and ground objects were classified at different spatial scales, the classification accuracy differed between the Am and the An.

Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC products (GEOV3 and MuSyQ) in alpine grassland ecosystems were validated through direct validation and multi-scale validation. Based on the heterogeneity of the underlying surface (HUS) of the monitoring plots, the impact of the HUS of the monitoring plots on the product validation was analyzed. The results showed that: (1) the measured data directly validated that the GEOV3 FVC product performed better than the MuSyQ FVC product; (2) the multi-scale validation method based on high-resolution reference FVC map of Sentienl-2 satellite images validated the accuracy of these two FVC products, which was higher than the accuracy directly validated by FVC measured data, leading to overestimation of the validation results; and (3) the HUS of the monitored plots has a significant impact on the FVC product validation. By quantifying the HUS of the monitored plots and removing the heterogeneous monitoring plots, the uncertainty of the validation results can be reduced. It is necessary to consider the impact of validation methods and the HUS on the validation results in future product validation.

Circular RNAs (circRNAs) are covalently closed RNA molecules, and have been identified in many crops. However, there are few datasets for circRNA junctions from common wheat during Fusarium head blight disease. In the present study, we used RNA-seq to determine the changes in circRNAs among the control (CK) and 1, 3, and 5 days post-Fusarium graminearum inoculation (dpi) samples. More than one billion reads were produced from 12 libraries, and 99.99% of the reads were successfully mapped to a wheat reference genome. In total, 2091 high-confidence circRNAs—which had two or more junction reads and were supported by at least two circRNA identification algorithms—were detected. The completed expression profiling revealed a distinct expression pattern of circRNAs among the CK, 1dpi, 3dpi and 5dpi samples. This study provides a valuable resource for identifying F. graminearum infection-responsive circRNAs in wheat and for further functional characterization of circRNAs that participated in the Fusarium head blight disease response of wheat.

Background: The world faced crises of prevention and control and shortage of medical resources during the COVID-19 (Corona Virus Disease 2019) outbreak. The establishment of temporary integrated isolation wards in hospitals, which is universal and representative in China, is one of the most-effective strategies in solving these problems according to China's experiences.Aim: To conduct a preliminary study on the establishment of a temporary integrated isolation ward during the outbreak of COVID-19 and to evaluate related impact.Methods: SWOT analysis was used to analyze the advantages, disadvantages, opportunities, and risks in the establishment of the temporary integrated isolation ward, and corresponding corrective measures were made according to the analysis results.Findings: The ward has formulated more than 10 related work procedures and prevention and control measures. A total of 93 patients with 18 critically ill patients were admitted for treatment and isolation. They were all evaluated based on established procedures and protocols. Twenty-four supplementary nucleic acid tests were ordered and conducted. One new patient with COVID-19 was confirmed and was successfully transferred to the designated COVID-19 infectious control hospital. There were no missed diagnosis or misdiagnosis, no cross-infection of patients, no cluster outbreak, and no infection of medical workers during the entire process.Conclusion: SWOT analysis is helpful in guiding the establishment of a temporary integrated isolation ward and the formulation of prevention and control measures in Hebei General Hospital during the COVID-19 outbreak. It provides the guidance and reference of significance for the establishment of similar types of wards in the future.

Alternanthera philoxeroides is a notorious invasive weed worldwide, but it still lacks a genome information currently. In this study, we collected 4 groups of A. philoxeroides Illumina RNA-seq data (62.5 Gb) and performed a comprehensive de novo assembling. Totally, 421,372 unigenes were obtained with a total length of 230,842,460 bp, with 43,430 (10.31%) unigenes longer than 1000 bp. Then 119,222 (28.3%) unigenes were functional annotated and 235,885 (56.0%) were grouped into reliable lncRNAs reservoir. Besides, 534 tRNA and 234 rRNAs were identified in assembly sequences. Additionally, 131,624 microsatellites were characterized in 106,761 sequences. Then SSR primers were developed for the amplification of 40,752 microsatellites in 36,329 sequences. The miRNAs are key post-transcriptional regulators, about 86 candidate miRNA sequences were detected from A. philoxeroides assembly, and miRNA target genes prediction revealed possible functions of them in growth and development as well as stress responding processes. These results provide a vital basis for sequence-based studies of A. philoxeroides in the future, especially gene function analysis.

The objective of this study was to investigate the between autophagy activity and local immune response in hypopharyngeal squamous cell carcinoma (HSCC). Herein, we observed the expression of autophagy marker microtubule-associated protein light chain 3B (MAP1LC3B), CD8 cytotoxic T lymphocytes (CTLs), CD39 (regulatory T cells Tregs) and CD163 (tumor-associated macrophages TAMs) in HSCC, and determined the prognostic roles of CD8 + /CD39 + and CD8 + /CD163 + in patients with HSCC. The expression of light chain 3B (LC3B) and CD8 + /CD39 + was found to be significantly lower in HSCC tissues than in adjacent non-tumor mucosa tissue samples; LC3B expression was positively correlated with the infiltration rate of CD8 + /CD39 + in HSCC. Further studies revealed that the ratio of CD8 + /CD39 + immune cells was negatively correlated with tumor lymph node metastasis and TNM classification, while the ratio of CD8 + /CD163 + immune cells was negatively correlated with TNM classification. Moreover, the expression of LC3B was analyzed and the patients were grouped according to their immune infiltration characteristics. The 5-year cumulative survival rates of LC3B + , CD8 + /CD39 + , and CD8 + /CD163 + patients were significantly higher than those of other group patients. Collectively, our studies indicated that the expression of LC3B in HSCC was correlated with the infiltration ratio of immune cells, and a change in autophagy activity may affect the cellular immunity in HSCC. The ratios of tCD8 + /CD39 + and tCD8 + /CD163 + may serve as prognostic factors for HSCC.

Abstract Disorder‐induced Griffiths singularity of quantum phase transition (QPT) is a crucial issue in 2D superconductors (2DSC). In a superconducting system, the strength of disorder is found to be associated with the vortex pinning energy, which is closely related to the quantum Griffiths singularity; however, a direct study to elucidate the role of vortex pinning energy on the quantum Griffiths singularity in 2DSC remains to be undertaken. Here, an artificial 2DSC system is designed by randomly depositing superconducting nanoislands on 2Delectron gas (2DEG). Quantum Griffiths singularity is present in a graphene/Pb‐islands‐array hybrid, where the superconducting behavior transits to weakly localized metallic behavior induced by the vertical magnetic field and exhibits critical behavior with a diverging dynamical critical exponent approaching zero temperature. Compared to the study of graphene/Sn‐islands‐array hybrid where the sharp QPT is observed, the vortex pinning energy acquired from the Arrhenius plot analysis is greater in graphene/Pb‐islands‐array hybrid, which may contribute to the presence of the quantum Griffiths singularity. This work may provide a comprehensive interpretation of the QPT in 2DSC.

The source region of the Yellow River (SRYR) is an important water conservation and animal husbandry resource in China. It is of great significance to understand the patch pattern and ecological risk of alpine grassland in the SRYR for ecological environment management. This study first used 12 unmanned aerial vehicle (UAV) aerial images and eight moderate resolution imaging spectroradiometer (MODIS) vegetation index product MOD13Q1 images from July to August in 2019 to extract alpine grassland patch patterns in the SRYR, then constructed an ecological risk model based on the landscape vulnerability index and landscape disturbance index, and finally combined spatial self-reliance correlation and semi-variance analysis methods to explore the spatial distribution of ecological risks. The results showed that the patch fragmentation degree (Pi), area weighted shape index (AWMSI), and separation degree (Si) of the four grassland types in the SRYR are ordered as follows: alpine steppe > degraded meadow > alpine meadow > swamp meadow. Moreover, the greater the fractional vegetation cover (FVC), the greater the landscape dominance index (DOi), and the better the ecosystem stability. The spatial difference of ecological risk in the SRYR shows a situation of low risk in the east (ERImin=1.5355) and high risk in the west (ERImax = 70.6429). High FVC was found in low and mild low risk areas where the vegetation types are mainly swamp meadow and shrub, while low FVC was found in high and mild high-risk areas where the vegetation types are mainly alpine steppe and degraded meadow. The spatial distribution of ecological risk of the SRYR has obvious positive spatial correlation (Moran's I = 0.863), the spatial aggregation distribution is distinct, and the local space has significant high-high aggregation and low–low aggregation phenomena. The results of this study reveal that patch characteristics have good indicative significance for alpine grassland ecological protection and should be considered in future studies. In addition, the ecological risk in the SRYR is relatively high, especially in the western region, which should be taken seriously in future ecological management and governance.