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Abstract The chiral edge current is the boundary manifestation of the Chern number of a quantum anomalous Hall (QAH) insulator. The van der Waals antiferromagnet MnBi2Te4 is theorized to be a QAH in odd-layers but has shown Hall resistivity below the quantization value at zero magnetic field. Here, we perform scanning superconducting quantum interference device (sSQUID) microscopy on these seemingly failed QAH insulators to image their current distribution. When gated to the charge neutral point, our device exhibits edge current, which flows unidirectionally on the odd-layer boundary both with vacuum and with the even-layers. The edge current chirality reverses with the magnetization of the bulk. Surprisingly, we find the edge channels coexist with finite bulk conduction even though the bulk chemical potential is in the band gap, suggesting their robustness under significant edge–bulk scattering. Our result establishes the existence of chiral edge currents in a topological antiferromagnet and offers an alternative for identifying QAH states.

Geosynchronous-Earth-orbit (GEO) synthetic aperture radar (SAR) is able to achieve targeted observation, thanks to its flexibility in antenna steering, while focusing GEO SAR data is challenging due to the highly dynamic imaging geometry. Its processing algorithm is required to be able to deal with different view angles at different orbit segments, and the non-Euclidean scene geometry must be considered in the algorithm model and processing chain. This article focuses on developing the processing algorithm applicable to the GEO SAR, especially at high squint. The analytic expressions of the squint-mode wavenumber support and point spreading function are derived. Then, we propose a generalized fast Cartesian factorized back-projection algorithm, which can deal with high-squint data, and the scene coordinate system can be built with high flexibility. Especially, the role of the scene coordinate system in achieving high imaging performance is studied, and the optimal scene coordinate system is exploited. Finally, the processing results of simulated point target data are presented to validate the proposed algorithm.

Abstract Background Upper urinary tract urothelial carcinoma (UTUC) is a rare and highly aggressive malignancy characterized by poor prognosis, making the accurate identification of high-grade (HG) UTUC essential for subsequent treatment strategies. This study aims to develop and validate a nomogram model using computed tomography urography (CTU) images to predict HG UTUC. Methods A retrospective cohort study was conducted to include patients with UTUC who underwent radical nephroureterectomy and received a CTU examination prior to surgery. In the CTU images, tumor lesions located in the renal calyces, renal pelvis and ureter were segmented, and radiomics features from the unenhanced, medullary, and excretory phases were extracted. The maximum relevance minimum redundancy algorithm, least absolute shrinkage and selection operator, and various machine learning (ML) algorithms—including random forest, support vector machine, and eXtreme gradient boosting—were employed to select radiomics features and calculate radiomics scores. Logistic regression (LR) analysis was performed to identify the independent influencing factors of clinical baseline characteristics. Multiple datasets of radiomics features were constructed by integrating single-phase radiomics features with the most significant independent factor. Both LR and ML algorithms were utilized to develop predictive models. The area under the receiver operating characteristic curve (AUC values), accuracy, sensitivity, and specificity were assessed for model performance evaluation. Decision curve analysis was conducted to evaluate the clinical net benefits. Results A total of 167 patients were enrolled in this study. Among them, 56 were diagnosed with low-grade UTUC (including papillary urothelial neoplasms with low malignant potential and low-grade urothelial carcinoma) as confirmed by postoperative pathological examination results, and 111 were of HG. These patients were randomly allocated to the training set and the validation set at a ratio of 7:3. The training set comprised 116 patients with a mean age of 63.5 ± 9.38 years and 38 males. The validation set comprised 51 patients with a mean age of 65.6 ± 11.1 years and 18 males. Hydronephrosis was identified as the most significant independent factor in the clinical baseline features. Models that include mixed-phase development achieve better performance compared to models that rely simply on single-phase development. The nomogram model had excellent predictive ability for HG UTUC, with AUC values of 0.844 and an accuracy of 0.793 in the validation sets. The nomogram model can enhance accuracy by 14.1% (79.3% vs. 65.2%) and sensitivity by 32.8% (93.2% vs. 60.4%) compared to urinary cytology. Conclusions This study developed a nomogram model, which significantly improved the diagnostic ability for HG UTUC compared to urinary cytology. Furthermore, the results of the decision curve analysis showed that the model had a net benefit and could provide a non-invasive and potentially diagnostic reference tool for HG UTUC.

Abstract Nickel-rich layered oxides are one of the most promising positive electrode active materials for high-energy Li-ion batteries. Unfortunately, the practical performance is inevitably circumscribed by the structural deterioration deriving from the Ni/Li antisite disorder, leading to severe capacity loss and life attenuation. Herein, we propose an economical and facile rejuvenation strategy by employing the magneto-electrochemical synergistic activation targeting the positive electrode in assembled Li-ion batteries. This approach induces a transition of Ni3+ from high-spin to low-spin, reducing the super-exchange interaction of Ni-O-transition metal (TM). Meanwhile, electrochemical reaction drives Li+ from the host material and promotes Ni3+ to reoccupy TM layer, recovering intrinsic Li site and extending cycle life. The strategy demonstrates that low-quality positive electrodes can be converted to high-quality ones. Notably, the method can revitalize an aged Li-ion pouch cell (SiC||NCM811, 8 Ah nominal capacity) via optimizing cation occupancy and increase its capacity by 10% from 6.49 to 7.14 Ah at 1 C, illustrating the benefits of the upcycling process.

Abstract Objective To discuss the differences in the effectiveness and security of kidney stones in overweight or obese patients by mini percutaneous nephrolithotomy (MiniPCNL) and retrograde intrarenal surgery (RIRS). Materials and methods We exhaustively searched numerous databases, including PubMed, Embase, Web of Science, Cochrane Library, and CNKI, covering all records from their initiation date until September 2023. This included controlled trials focusing on the use of MiniPCNL and RIRS in the treatment of kidney stones in overweight or obese patients. The gathered data was then analyzed using the Review Manager 5.4 software. Results 9 studies including 1122 patients were included. Meta-analysis showed that: The MiniPCNL group had higher overall complications, grade I complications, length of hospital stay(LOS), first stone-free rate (SFR), and final SFR in obese patients, with no significant difference between the two groups in terms of operative time(OT), hemoglobin drop, and grade II complication rate. There were more overall complications, grade I complications, final SFR, and LOS with MiniPCNL in patients with stones > 2 cm compared to no significant difference in grade II complications. MiniPCNL performed in the prone position had higher final SFR, less OT, hemoglobin drop, and no statistically significant difference in overall complications or LOS. Sheaths using > 14 F had higher overall complication rates, final SFR, and LOS, and no statistical differences in OT and first SFR between the two modalities. In the MiniPCNL subgroup aged ≤ 50 years, there were higher first SFR, final SFR, and shorter OT, and in the MiniPCNL subgroup aged > 50 years, there were more OT, LOS, and hemoglobin drop, with no statistical difference in overall complications between the two groups. Conclusion Our study showed that MiniPCNL in obese patients had higher initial SFR and final SFR, fewer procedures, but more postoperative complications, LOS, and grade I complications compared with RIRS. Similar results were seen in patients in the prone position, with stones > 2 cm and age ≤ 50 years. Systematic review registration [ https://www.crd.york.ac.uk/PROSPERO/ ], identifier PROSPERO (CRD42023467284).

Abstract Nonlinear charge transport, such as nonreciprocal longitudinal resistance and nonlinear Hall effect, has attracted considerable interest in probing the symmetries and topological properties of new materials. Recent research has revealed significant nonreciprocal longitudinal resistance and nonlinear Hall effect in MnBi2Te4, an intrinsic magnetic topological insulator, induced by the quantum metric dipole. However, the inconsistent response with charge density and conflicting C3z symmetry requirement necessitate a thorough understanding of factors affecting the nonlinear transport measurement. This study uncovers an experimental factor leading to significant nonlinear transport signals in MnBi2Te4, attributed to gate voltage oscillation from the application of large alternating current. Additionally, a methodology is proposed to suppress this effect by individually grounding the voltage electrodes during second-harmonic measurements. The investigation underscores the critical importance of assessing gate voltage oscillation’s impact before determining the intrinsic nature of nonlinear transport in 2D material devices with an electrically connected operative gate electrode.

Abstract Recovering 3D human meshes from monocular images is an inherently ill‐posed and challenging task due to depth ambiguity, joint occlusion, and truncation. However, most existing approaches do not model such uncertainties, typically yielding a single reconstruction for one input. In contrast, the ambiguity of the reconstruction is embraced and the problem is considered as an inverse problem for which multiple feasible solutions exist. To address these issues, the authors propose a multi‐hypothesis approach, multi‐hypothesis human mesh recovery (MH‐HMR), to efficiently model the multi‐hypothesis representation and build strong relationships among the hypothetical features. Specifically, the task is decomposed into three stages: (1) generating a reasonable set of initial recovery results (i.e., multiple hypotheses) given a single colour image; (2) modelling intra‐hypothesis refinement to enhance every single‐hypothesis feature; and (3) establishing inter‐hypothesis communication and regressing the final human meshes. Meanwhile, the authors take further advantage of multiple hypotheses and the recovery process to achieve human mesh recovery from multiple uncalibrated views. Compared with state‐of‐the‐art methods, the MH‐HMR approach achieves superior performance and recovers more accurate human meshes on challenging benchmark datasets, such as Human3.6M and 3DPW, while demonstrating the effectiveness across a variety of settings. The code will be publicly available at https://cic.tju.edu.cn/faculty/likun/projects/MH‐HMR.

Abstract As the power industry becomes increasingly complex, Digital Twin (DT) technology has emerged as a crucial tool for enhancing grid resilience and operational efficiency by creating dynamic digital replicas of physical systems. These replicas enable accurate simulations and proactive management, but the vast amount of data generated by DT systems poses significant challenges for processing and analysis. Cloud computing offers a flexible solution by offloading these computational tasks to distributed resources, allowing for real-time analysis and scalable operations. However, this approach introduces complexities in task distribution and maintaining quality of service. Recent efforts have applied Deep Reinforcement Learning (DRL) to address these challenges, primarily using single-agent methods. However, these methods struggle with scalability and performance in increasingly complex cloud environments. To overcome these limitations, we propose an efficient task scheduling framework based on Multi-Agent Deep Q-Network (MADQN) principles, specifically designed to optimize both response times and operational costs. We provide a comprehensive design overview of our approach and conduct a thorough evaluation of its performance. The experimental results clearly indicate that our approach can considerably reduce response times amd lower operational costs compared to current methods, including state-of-the-art single-agent approaches.

IntroductionPerformance pressure refers to employees' subjective perception of the necessity to achieve expected goals, accompanied by a sense of urgency and tension. This study explores how employees cope with performance pressure, focusing on two contrasting strategies: the “rat race” (proactive work behavior) or “lying flat” (work withdrawal). Grounded in the transactional theory of stress and affective event theory, this research aims to uncover the mechanisms through which performance pressure influences work behavior.MethodsA moderated dual-mediation model was developed to examine the dual pathways of challenge appraisal and workplace anxiety as mediators in the relationship between performance pressure and work behavior. Data were collected through a two-stage survey involving 356 employees from various industries. Statistical analyses, including structural equation modeling, were used to test the hypothesized relationships.ResultsThe findings reveal that performance pressure has a dual effect: it simultaneously stimulates challenge appraisal, promoting proactive work behavior, and induces workplace anxiety, leading to work withdrawal behavior. Additionally, learning goal orientation moderates these effects. Specifically, it strengthens the positive relationship between performance pressure and challenge appraisal while weakening the link between performance pressure and workplace anxiety.DiscussionThis study highlights the complex and dual nature of performance pressure in influencing employee behavior. By identifying learning goal orientation as a critical moderator, organizations can better understand how to harness the positive aspects of performance pressure while mitigating its negative effects. These insights provide practical guidance for managing performance pressure and minimizing associated risks in the workplace.

Abstract Phacoemulsification combined with intraocular lens implantation is the primary treatment for cataract. Although this treatment strategy benefits patients with cataracts, posterior capsule opacification (PCO) remains a common complication that impairs vision and affects treatment outcomes. The pathogenesis of PCO is associated with the proliferation, migration, and fibrogenesis activity of residual lens epithelial cells, with epithelial–mesenchymal transition (EMT) serving as a key mechanism underlying the condition. Transforming growth factor‐beta 2 (TGF‐β2) is a major promotor of EMT, thereby driving PCO development. Most studies have shown that drugs and miRNAs mitigate EMT by inhibiting, clearing, or eliminating LECs. In addition, targeting EMT–related signaling pathways in TGF‐β2–stimulated LECs has garnered attention as a research focus. This review highlights potential treatments for PCO and details the mechanisms by which drugs and miRNAs counter EMT.

Abstract Background APRI and FIB-4 scores are used to exclude clinically significant fibrosis (defined as stage ≥ F2) in patients with chronic viral hepatitis. However, the cut-offs for these scores (generated by Youden indices) vary between different patient cohorts. This study aimed to evaluate whether serum dithiothreitol-oxidizing capacity (DOC), i.e., a surrogate test of quiescin sulfhydryl oxidase-1, which is a matrix remodeling enzyme, could be used to non-invasively identify significant fibrosis in patients with various chronic liver diseases (CLDs). Methods Diagnostic performance of DOC was compared with APRI and FIB-4 for identifying significant fibrosis. ROC curve analyses were undertaken in: a) two chronic hepatitis B (CHB) cohorts, independently established from hospitals in Wenzhou (n = 208) and Hefei (n = 120); b) a MASLD cohort from Wenzhou hospital (n = 122); and c) a cohort with multiple CLD etiologies (except CHB and MASLD; n = 102), which was identified from patients in both hospitals. Cut-offs were calculated using the Youden index. All CLD patients (n = 552) were then stratified by age for ROC curve analyses and cut-off calculations. Results Stratified by CLD etiology or age, ROC curve analyses consistently showed that the DOC test was superior to APRI and FIB-4 for discriminating between clinically significant fibrosis and no fibrosis, when APRI and FIB-4 showed poor/modest diagnostic performance (P 0.05 in 11 cohort comparisons). DOC had a significant advantage over APRI or FIB-4 scores for establishing a uniform cut-off independently of age and CLD etiology (coefficients of variation of DOC, APRI and FIB-4 cut-offs were 1.7%, 22.9% and 47.6% in cohorts stratified by CLD etiology, 2.0%, 26.7% and 29.5% in cohorts stratified by age, respectively). The uniform cut-off was 2.13, yielded from all patients examined. Surprisingly, the uniform cut-off was the same as the DOC upper limit of normal with a specificity of 99%, estimated from 275 healthy control individuals. Hence, the uniform cut-off should possess a high negative predictive value for excluding significant fibrosis in primary care settings. A high DOC cut-off with 97.5% specificity could be used for detecting significant fibrosis (≥ F2) with an acceptable positive predictive value (87.1%). Conclusions This proof-of-concept study suggests that the DOC test may efficiently rule out and rule in significant liver fibrosis, thereby reducing the numbers of unnecessary liver biopsies. Moreover, the DOC test may be helpful for clinicians to exclude significant liver fibrosis in the general population.

Abstract Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003–2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

ObjectiveThis study systematically evaluated Tai Chi’s effects on balance, fall prevention, and physical function in older adults.MethodsA comprehensive literature search of PubMed, Web of Science, and EMbase databases included randomized controlled trials published up to August 2024. The meta-analysis used RevMan 5.4 and applied the GRADE framework to assess evidence quality.ResultsTwenty-two studies met the criteria. Tai Chi significantly improved balance and physical function, particularly in the Berg Balance Scale (BBS), one-leg standing with eyes closed (OLS-C), and Timed up-and-go test (TUG). Fear of falling (FOF) also reduced, though changes in Tinetti Balance Test (TBT) and Short Physical Performance Battery (SPPB) were not statistically significant.ConclusionTai Chi effectively enhances balance and physical function in older adults, with notable potential for fall risk reduction. While some tests showed no significant effect, overall results support Tai Chi as a valuable intervention to promote older adults’ health.Systematic review registrationhttps://inplasy.com/inplasy-2024-8-0082/.

In China, absolute poverty has been effectively eliminated, but this does not signify the complete eradication of poverty. Instead, poverty persists in the forms of relative and secondary poverty. More concerningly, regions or households lifted out of poverty continue to face numerous risks of returning to poverty. In this context, measuring poverty solely based on monetary metrics is no longer adequate. Furthermore, the primary focus of grassroots governance has shifted from merely assessing poverty to accurately predicting the multifaceted risks associated with falling back into poverty. A multidimensional poverty indicator system is constructed to measure and predict the risk of multidimensional returning to poverty in China. Then, the Alkire–Forster counting method is applied to measure the risk of multidimensional poverty return and demonstrate the contribution of various indicators to multidimensional poverty return using tracking survey data from the China Family Panel Studies (CFPS). The results show that the multidimensional poverty return in China is mainly caused by the factors in two or three dimensions, where the social development capability dimension has the highest contribution to the multidimensional poverty return index with 43.12 %, followed by the health and education dimensions. Moreover, according to the finding that the identification of multidimensional poverty varies with the values of the poverty cut-off, a novel and practicable method is proposed to classify the risk into three levels, noted as the high, medium and low levels. Consequently, a hybrid model is constructed to predict the risk of multidimensional poverty return by integrating the Archimedes optimization algorithm (AOA), variational mode decomposition (VMD) and Bi-directional long short-term memory (BiLSTM) neural networks. Finally, the performance of the constructed model is validated with an accuracy up to 99.81 %. The constructed model's efforts outperform the traditional BiLSTM and several prevalent machine learning algorithms through extensive comparative experiments. These results illustrate that the proposed model can accurately and stably predict the potential risk of poverty return for multidimensional poverty groups and regions. In conclusion, drawing from the analysis of factors contributing to the risk of relapsing into poverty in this study, policy suggestions have been formulated focusing on education, social capacity enhancement, and healthcare system advancement. In summary, this paper provides new insights into the factors contributing to multidimensional poverty recurrence and the methods for assessing its risk levels, while also introducing a more precise approach to predicting these levels.

Vegetation phenology serves as an important indicator for climate change and plays a crucial role in affecting the terrestrial water, energy, and carbon cycles. The green chromatic coordinate (GCC) obtained from digital repeat photographs has been widely applied in estimating phenology from the perspective of greenness, while the performance of satellite derived GCC is not well understood. We used flux tower GPP from seven deciduous broadleaf forest (DBF) and three grassland (GRA) sites over the Northern Hemisphere. The aim was to compare phenological events with GCC (obtained from digital repeat photographs and satellite remote sensing (GCCMODIS)) and the enhanced vegetation index (EVI). Meanwhile, we also explored the performance of these three indices in simulating GPP utilizing the light use efficiency (LUE) model at the DBF and GRA sites. Phenology retrieved by GCC, GCCMODIS, and EVI was all significantly correlated with GPP-estimated values at all sites (P

Air pollution stands as the single largest environmental risk to human health globally, and negative air ions (NAIs) serve as essential indicators for measuring regional air quality. They play a crucial role in alleviating the pressure and risks posed by air pollution. NAIs are notably abundant in forests, where the photosynthesis of vegetation serves as a significant factor influencing their presence. However, a dearth of quantitative research exists regarding the relationship between vegetation photosynthesis and NAIs. Current literature only offers qualitative insights, suggesting that vegetation photosynthesis may foster NAIs production. The mechanisms through which the photosynthetic process influences NAIs and the potential contribution of forest vegetation to NAIs remain unclear, lacking quantitative investigation. In this study, Quercus variabilis, a typical tree species in the warm temperate zone, was chosen as the research subject. We examined the dynamic changes in NAIs and net ecosystem productivity (NEP) from 2019 to 2021. The results demonstrated a strong correlation between NAIs and NEP at the ecosystem scale during the primary vegetation growing season. NEP effectively tracked daily changes in NAIs and exhibited a good fit. Notably, the scatter plots of NAIs and NEP exhibited clear stratification under varying photosynthetically active radiation (PAR) intensities. The correlation between NAIs and NEP was significantly higher under high PAR conditions compared to low PAR conditions, with R2 values of 0.458 and 0.367, respectively. Consequently, NEP emerges as a superior indicator for tracking NAIs variation characteristics at the ecosystem scale compared to PAR. This finding offers a rapid and accurate evaluation method for discerning the spatial and temporal distribution patterns of NAIs within forest ecosystems. Such insights provide a theoretical foundation for further assessing the potential contribution of forest vegetation to NAIs.

Abstract The van der Waals antiferromagnetic topological insulator MnBi2Te4 represents a promising platform for exploring the layer-dependent magnetism and topological states of matter. Recently observed discrepancies between magnetic and transport properties have aroused controversies concerning the topological nature of MnBi2Te4 in the ground state. In this article, we demonstrate that fabrication can induce mismatched even-odd layer dependent magnetotransport in few-layer MnBi2Te4. We perform a comprehensive study of the magnetotransport properties in 6- and 7-septuple-layer MnBi2Te4, and reveal that both even- and odd-number-layer device can show zero Hall plateau phenomena in zero magnetic field. Importantly, a statistical survey of the optical contrast in more than 200 MnBi2Te4 flakes reveals that the zero Hall plateau in odd-number-layer devices arises from the reduction of the effective thickness during the fabrication, a factor that was rarely noticed in previous studies of 2D materials. Our finding not only provides an explanation to the controversies regarding the discrepancy of the even-odd layer dependent magnetotransport in MnBi2Te4, but also highlights the critical issues concerning the fabrication and characterization of 2D material devices.

During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849.

Abstract Background To characterize the current state of emergency medicine (EM) and the requirements for advancing EM clinical practice, education and research in China. Methods An anonymous electronic survey was conducted by Chinese Society of Emergency Medicine during September to October 2021. The survey contained 30 questions divided into 2 sections: the current state of EM development and the requirements for EM growth. Results 722 hospitals were included, of 487 were Level III and 235 were Level II hospitals. We found that after 40 years of development, EM had established a mature disciplinary system and refined sub-specialties including critical care, cardiopulmonary resuscitation, toxicology, disaster and emergency rescue. In Level III hospitals, 70.8% of EDs were standardized training centers for EM residents, but master’s degree program, Doctor Degree program and post-doctoral degree program was approved in only 37.8%, 8.4% and 2.9% of EDs respectively and postgraduate curriculum was available in 1/4 of EDs. Only 8% have national or provincial key laboratories. In addition to advance clinical practice, there was also a high demand to improve teaching and research capacities, mainly focusing on literature review, research design and delivery, paper writing, residency training. Conclusions EM has built a mature discipline system and refined sub-specialties in China. Teaching and research developed parallel with clinical practice. However, there was still a lack of EM master’s and doctoral programs and research capacities need to be improved. More outstanding clinical and academic training should be provided to promote the rapid growth of EM in China.

Intercropping with legume forages is recognized as an effective strategy for enhancing nitrogen levels in agroforestry, while mowing may influence nitrogen fixation capacity and yield. This study investigated the rooting, nitrogen fixation, nutritive value, and yield of alfalfa (Medicago sativa L.) under intercropping and varying mowing frequencies (CK, 2, and 3) from 2021 to 2023, using walnut (Juglans regia L.) and alfalfa as experimental subjects. The results indicated that intercropping suppressed root growth, whereas increased mowing frequency stimulated root development in the topsoil (0–20 cm). Specifically, the average root length density, root surface area, and root volume from the twice- and thrice-mowed treatments increased by 18.26, 17.45, and 4.15%, respectively, in comparison to the control. The δ15N values of the intercropped alfalfa were significantly lower than those of the monocropped alfalfa (p < 0.05), with the δ15N values of the mowing-thrice treatment increasing by an average of 38.61% compared to the control. Intercropping suppressed alfalfa yield but did not affect the total nitrogen content in the leaves or the nutritive value, and all mowing treatments resulted in land equivalent ratios (LERs) greater than 1. Furthermore, increased mowing frequency enhanced both the nutritive value and yield of alfalfa. Our study suggests that intercropping with walnut can improve biological nitrogen fixation in alfalfa, and that adopting a mowing-thrice regime can optimize yield and nutritive value.

Liver cancer is one of the most prevalent cancers worldwide. The first-line therapeutic drug sorafenib offers only a moderate improvement in patients’ conditions. Therefore, an approach to enhancing its therapeutic efficacy is urgently needed. It has been revealed that hepatocellular carcinoma (HCC) cells with heightened intracellular quiescin sulfhydryl oxidase 1 (QSOX1) exhibit increased sensitivity to sorafenib. QSOX1 is a secreted disulfide catalyst, and it is widely recognized that extracellular QSOX1 promotes the growth, invasion, and metastasis of cancer cells through its participation in the establishment of extracellular matrix. Inhibiting QSOX1 secretion can increase intracellular QSOX1 and decrease extracellular QSOX1. Such an approach would sensitize HCC cells to sorafenib but remains to be established. Since (−)-epigallocatechin-3-gallate (EGCG) has been demonstrated to be an effective inhibitor of α-fetal protein secretion from HCC cells, we screened QSOX1 secretion inhibition using polyphenolic compounds. We examined eight dietary polyphenols (EGCG, quercetin, fisetin, myricetin, caffeic acid, chlorogenic acid, resveratrol, and theaflavin) and found that EGCG and quercetin effectively inhibited QSOX1 secretion from human HCC cells (HepG2 or Huh7), resulting in high intracellular QSOX1 and low extracellular QSOX1. The combination of EGCG or quercetin, both of which change the cellular distribution of QSOX1, with sorafenib, which has no influence on the cellular distribution of QSOX1, exhibited multiple synergistic effects against the HCC cells, including the induction of apoptosis and inhibition of invasion and metastasis. In conclusion, our current results suggest that dietary EGCG and quercetin have the potential to be developed as adjuvants to sorafenib in the treatment of HCC by modulating the cellular distribution of QSOX1.

Diquat (DQ) is a commonly used bipyridine herbicide known for its toxic properties and adverse effects on individuals. However, the mechanism underlying DQ-induced damage remain elusive. Our research aimed to uncover the regulatory network involved in DQ-induced damage. We analyzed publicly accessible gene expression patterns and performed research using a DQ-induced damage animal model. The GSE153959 dataset from the Gene Expression Omnibus collection and the animal model of DQ-induced kidney injury were used to identify differentially expressed genes (DEGs). Pathways including the regulation of DNA-templated transcription in response to stress, RNA polymerase II transcription regulator complex and transcription coregulatory activity were shown to be enriched in 21 DEGs. We used least absolute shrinkage and selection operator (LASSO) regression analysis to find possible diagnostic biomarkers for DQ-induced damage. Then, we used an HK-2 cell model to confirm these results. Additionally, we confirmed that 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) was the major gene associated with DQ-induced damage using multi-omics screening. The sample validation strongly suggested that HMGCS2 has promise as a diagnostic marker and may provide new targets for therapy in the context of DQ-induced damage.

Abstract The TRIZ theory was used to accurately discover the problems to be solved in the design of roadway surrounding rock control technology. This paper tried to solve the complex issue of surrounding rock control in deep roadways from a new perspective. Based on the functional component analysis and causal axis analysis of the problem’s primary reason, simultaneously, the surrounding rock control technology was optimized through technical contradiction analysis, physical contradiction analysis, and substance and field model analysis. As a result, a fully enclosed wire-shell support technology was proposed. Finally, taking the typical soft rock roadway engineering of Pansan Coal Mine in Huainan Mining Area, Anhui Province, China, as the engineering background, the engineering application and effect evaluation were completed. This paper provides a reference for controlling the instability of deep soft rock roadways in coal mines. A new idea of optimizing roadway support engineering based on TRIZ theory was proposed.

Change detection is a crucial remote sensing (RS) application because it can locate the interesting changed regions and provide corresponding time-series information with multitemporal RS images acquired in the same region. Synthetic aperture radar (SAR), with the advantages of all-day and all-weather conditions, can achieve high-resolution imaging from long operation distances. Moreover, the coherence imaging characteristics of the SAR system cause the attained complex-value images to be more sensitive to ground surface features. Traditional intensity-based change detection methods merely use the intensity difference between multitemporal images; thus, the results only reflect the significant landmark changes, such as seismic disasters and flood disasters. In this article, we use the coherent imaging characteristics of the SAR complex images to detect very subtle changes, such as vehicle tracks and footprints. Specifically, coherent change detection based on amplitude and phase generates a difference image utilizing the repeat-pass repeat-geometry SAR images, while the global attention-based convolutional neural network achieves automatic subtle change track extraction from the difference images. The experimental results based on our measured airborne SAR data demonstrate that our proposed method reduces the sensitivity of the detectable changed region from the meter level to the centimeter level, further providing our method with the ability to detect very subtle changes, such as vehicle tire tracks or footprints by human activities. This subtle change detection capability can be used for the search and rescue of vehicles and personnel lost in the field.

RNA molecules serve a wide range of functions that are closely linked to their structures. The basic structural units of RNA consist of single- and double-stranded regions. In order to carry out advanced functions such as catalysis and ligand binding, certain types of RNAs can adopt higher-order structures. The analysis of RNA structures has progressed alongside advancements in structural biology techniques, but it comes with its own set of challenges and corresponding solutions. In this review, we will discuss recent advances in RNA structure analysis techniques, including structural probing methods, X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and small-angle X-ray scattering. Often, a combination of multiple techniques is employed for the integrated analysis of RNA structures. We also survey important RNA structures that have been recently determined using various techniques.

Abstract The use of exogenous mitochondria to replenish damaged mitochondria has been proposed as a strategy for the treatment of pulmonary fibrosis. However, the success of this strategy is partially restricted by the difficulty of supplying sufficient mitochondria to diseased cells. Herein, we report the generation of high-powered mesenchymal stem cells with promoted mitochondrial biogenesis and facilitated mitochondrial transfer to injured lung cells by the sequential treatment of pioglitazone and iron oxide nanoparticles. This highly efficient mitochondrial transfer is shown to not only restore mitochondrial homeostasis but also reactivate inhibited mitophagy, consequently recovering impaired cellular functions. We perform studies in mouse to show that these high-powered mesenchymal stem cells successfully mitigate fibrotic progression in a progressive fibrosis model, which was further verified in a humanized multicellular lung spheroid model. The present findings provide a potential strategy to overcome the current limitations in mitochondrial replenishment therapy, thereby promoting therapeutic applications for fibrotic intervention.

Abstract Purpose ​Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community. Patients and methods The nomogram prediction model was developed in a cohort of 7200 hypertensive patients at a community health service center in Hongshan District, Wuhan City. The data were collected from January 2022 to December 2022 and randomly divided into modeling and validation groups at a 7:3 ratio. The Lasso regression model was used for data dimensionality reduction, feature selection, and clinical test feature construction. Multivariate logistic regression analysis was used to build the prediction model. Results The application of the nomogram in the verification group showed good discrimination, with an AUC of 0.9205 (95% CI: 0.8471–0.9527) and a good calibration effect. Decision curve analysis demonstrated that the predictive model was clinically useful. Conclusion This study presents a nomogram prediction model that incorporates age, waist-height ratio and elevated density lipoprotein cholesterol (HDL-CHOLESTEROL), which can be used to predict the risk of codeveloping diabetes in hypertensive patients.

Abstract Objectives To build a nomogram prediction model, assess its predictive ability, and perform a survival decision analysis on patients with muscle‐invasive bladder cancer (MIBC) to study risk factors affecting overall survival (OS). Methods A retrospective analysis was performed on the clinical information of 262 patients with MIBC who underwent radical cystectomy (RC) at the Urology Department of the Second Affiliated Hospital of Kunming Medical University between July 2015 and August 2021. The final model variables that were included were chosen using single‐factor stepwise Cox regression, optimal subset regression, and LASSO regression + cross‐validation with the minimum AIC value. The next step was to do a multivariate Cox regression analysis. The establishment of a nomogram model by fitting and the screening out of independent risk factors impacting the survival of patients with MIBC having radical resection. Receiver Activity Characteristic curves, C‐index, and a calibration plot evaluated the prediction accuracy, validity, and clinical benefit of the model. The 1‐, 3‐, and 5‐year survival rates were then computed for each risk factor using a Kaplan–Meier survival analysis. Results 262 eligible patients in total were enrolled. With a median follow‐up of 32 months, the follow‐up period ranged from 2 to 83 months. 171 cases (65.27%) survived while 91 cases (34.73%) perished. Age (HR = 1.06 [1.04; 1.08], p = 0.001), preoperative hydronephrosis (HR = 0.69 [0.46, 1.05], p = 0.087), T stage (HR = 2.06 [1.09, 3.93], p = 0.027), lymphovascular invasion (LVI, HR = 1.73 [1.12, 2.67], p = 0.013), prognostic nutritional index (PNI, HR = 1.70 [1.09, 2.63], p = 0.018), and neutrophil‐to‐lymphocyte ratio (NLR, HR = 0.52 [0.29, 0.93)], p = 0.026) were independent risk factor for the survival of bladder cancer patients. Create a nomogram based on the aforementioned findings, and then draw the 1‐year, 3‐year, and 5‐year OS receiver operating characteristic curves by the nomogram. The AUC values were 0.811 (95% CI [0.752, 0.869]), 0.814 (95% CI [0.755, 0.873]), and 0.787 (95% CI [0.708, 0.865]), respectively, and the calibration plot matched the predicted value well. The 1‐year, 3‐year, and 5‐year decision curve analyses were higher than the ALL line and None line at threshold values of >5%, 5%–70%, and 20%–70% indicating that the model has good clinical applicability. The calibration plot for the Bootstrap 1000‐time resampled validation model was similar to the actual value. Patients with preoperative combination hydronephrosis, higher T‐stage, combined LVI, low PNI, and high NLR had worse survival, according to Kaplan–Meier survival analysis for each variable. Conclusions This study might conclude that PNI and NLR were separate risk factors that affect a patient's OS after RC for MIBC. The prognosis of bladder cancer may be predicted by PNI and NLR, but additional confirmation in randomized controlled trials is required.

Abstract Novel viewpoint image synthesis is very challenging, especially from sparse views, due to large changes in viewpoint and occlusion. Existing image-based methods fail to generate reasonable results for invisible regions, while geometry-based methods have difficulties in synthesizing detailed textures. In this paper, we propose STATE, an end-to-end deep neural network, for sparse view synthesis by learning structure and texture representations. Structure is encoded as a hybrid feature field to predict reasonable structures for invisible regions while maintaining original structures for visible regions, and texture is encoded as a deformed feature map to preserve detailed textures. We propose a hierarchical fusion scheme with intra-branch and inter-branch aggregation, in which spatio-view attention allows multi-view fusion at the feature level to adaptively select important information by regressing pixel-wise or voxel-wise confidence maps. By decoding the aggregated features, STATE is able to generate realistic images with reasonable structures and detailed textures. Experimental results demonstrate that our method achieves qualitatively and quantitatively better results than state-of-the-art methods. Our method also enables texture and structure editing applications benefiting from implicit disentanglement of structure and texture. Our code is available at http://cic.tju.edu.cn/faculty/likun/projects/STATE .

Abstract Background The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI. Methods This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm2) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm2) were processed with mono-exponential model to generate ADC and ADCuh, respectively. The performance of the ADCuh was compared with ADC in 3-year progression free survival (PFS) assessment using time-dependent ROC and Kaplan-Meier curve. Prognosis model was constructed with ADCuh, ADC and clinicopathologic factors using multivariate COX proportional hazard regression analysis. The prognosis model was assessed with time-dependent ROC, decision curve analysis (DCA) and calibration curve. Results A total of 112 patients with LARC (TNM-stage II-III) were evaluated. ADCuh performed better than ADC for 3-year PFS assessment (AUC = 0.754 and 0.586, respectively). Multivariate COX analysis showed that ADCuh and ADC were independent factors for 3-year PFS (P

Mixed forests often increase their stability and species richness in comparison to pure stands. However, a comprehensive understanding of the effects of mixed forests on soil properties, bacterial community diversity, and soil nitrogen cycling remains elusive. This study investigated soil samples from pure Robinia pseudoacacia stands, pure Quercus variabilis stands, and mixed stands of both species in the southern foothills of the Taihang Mountains. Utilizing high-throughput sequencing and real-time fluorescence quantitative PCR, this study analyzed the bacterial community structure and the abundance of nitrogen-cycling functional genes within soils from different stands. The results demonstrated that Proteobacteria, Acidobacteria, and Actinobacteria were the dominant bacterial groups across all three forest soil types. The mixed-forest soil exhibited a higher relative abundance of Firmicutes and Bacteroidetes, while Nitrospirae and Crenarchaeota were most abundant in the pure R. pseudoacacia stand soils. Employing FAPROTAX for predictive bacterial function analysis in various soil layers, this study found that nitrogen-cycling processes such as nitrification and denitrification were most prominent in pure R. pseudoacacia soils. Whether in surface or deeper soil layers, the abundance of AOB amoA, nirS, and nirK genes was typically highest in pure R. pseudoacacia stand soils. In conclusion, the mixed forest of R. pseudoacacia and Q. variabilis can moderate the intensity of nitrification and denitrification processes, consequently reducing soil nitrogen loss.

Plant community characteristics and soil properties are critical indicators for evaluating wetland degradation and restoration. In order to investigate how the degradation of inland salt marsh wetlands affects plants and soils as well as the response relationship between plants and soils under different degradation degrees, this paper selected inland salt marsh wetlands with different degradation degrees in the wetland area of western Songnen Plain, and analyzed the characteristics of plant communities, soils and their relationships. The results show that: that the important value of Gramineae plants decreased by about 21% with increasing degradation, while the vital value of Cyperaceae and weeds increased by 10% and 9%, respectively. Plant height decreased significantly, and coverage and density showed a decreasing and then increasing trend. Overall, species diversity shows a trend of increasing and then decreasing, indicating that the structure of plant communities tends to be more straightforward and more complex with increasing degradation. Soil electrical conductivity, total organic carbon (TOC) and total nitrogen (TN) content decreased significantly with the degree of degradation from ND (non degradation) to HD (heavy degradation) by 34.08%, 19.56%, and 35.82%, respectively. Soil C:N increased from 13.13 to 17.97. Soil N:P and C:P decreased from 5.56 to 3.28 and from 71.59 to 54.02, respectively. Soil factors affecting wetland plants were inconsistent at different stages of degradation. Changes in soil nitrogen and phosphorus content were correlated with changes in wetland vegetation community structure. Therefore, exploring how wetland degradation affects plants and soils can provide a scientific basis for wetland degradation assessment and wetland restoration.

What is the impact of environmental management system certification on green innovation performance, and is it a futile endeavor or a profitable one? Grounded in the principles of ecological civilization construction and green development, this study embarks on a comprehensive examination. Initially, it investigates the varying impacts of environmental management system certification on both traditional innovation performance and green innovation performance. Subsequently, it dissects the underlying mechanisms and moderating factors influencing the latter, including an exploration of intermediary effects. The empirical findings of this study are as follows: (i) Environmental management system certification emerges as a catalyst for innovation performance, with the primary impact observed in the realm of green innovation performance. (ii) Social responsibility disclosure is identified as a mediating factor in the relationship between environmental management system certification and green innovation performance. (iii) Larger enterprises, those equipped with robust equity incentives, and those operating in less competitive markets are more prone to benefit from the impact of environmental management system certification on social responsibility disclosure. This, in turn, amplifies the promotion of green innovation performance. However, the moderating effect of property rights on the mediating path remains statistically insignificant. (iv) Environmental management system certification exerts a more pronounced influence on green innovation performance in regions characterized by lower economic development. Moreover, it particularly stimulates exploratory green innovation performance, surpassing its impact on exploitative green innovation performance.

Abstract Objectives Development and validation of a computed tomography urography (CTU)‐based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC). Methods A total of 140 patients with UTUC who underwent CTU examination from January 2017 to August 2023 were retrospectively enrolled. Tumor lesions on the unenhanced, medullary, and excretory periods of CTU were used to extract Features, respectively. Feature selection was screened by the Pearson and Spearman correlation analysis, least absolute shrinkage and selection operator algorithm, random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). The logistic regression (LR) was used to screen for independent influencing factors of clinical baseline characteristics. Machine learning models based on different feature datasets were constructed and validated using algorithms such as LR, RF, SVM, and XGBoost. By computing the selected features, a radiomics score was generated, and a diverse feature dataset was constructed. Based on the training set, 16 ML models were created, and their performance was evaluated using the validation set for metrics including sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and others. Results The training set consisted of 98 patients (mean age: 64.5 ± 10.5 years; 30 males), whereas the validation set consisted of 42 patients (mean age: 65.3 ± 9.78 years; 17 males). Hydronephrosis was the best independent influence factor (p

The pressure analysis and flow characteristics during the flight of a high-speed bullet are significant for aerodynamic performance, flight trajectory control, safety and reliability, performance optimization, guidance and navigation. To accurately monitor the pressure variation for a high-speed bullet in the high-altitude flight, a novel local fluid domain method is introduced in current study. This method overcomes the limitations of conventional global fluid domain simulation methods, which often suffer from poor convergence and stability issues. On the basis, the force analysis is conducted and the mathematical model for the theoretical trajectory is established, which is applied to simulate the flight trajectory and construct fluid domain for the high-speed bullet. The findings reveal that monitoring points within the bullet hole are influenced by both cavity flow and the collision of forward airflows during the high-altitude flight. Furthermore, it is observed that the absolute total pressure and reference pressure at these monitoring points within the bullet hole are influenced by altitude variations. Subsequently, the factors affecting the absolute total pressure within the hole are explored, including the hole depth, the hole diameter, the hole position, and the bullet orientation. The results indicate that modifying the bullet structure to incorporate 45° angled hole, as opposed to standard holes, can lead to a 13.6% reduction in absolute total pressure variation. Additionally, mitigating the interaction between the external and internal fluids emerges as a critical factor in reducing absolute total pressure within the hole. This research not only presents a fresh perspective for pressure monitoring within the bullet hole, but also establishes a theoretical foundation for the precise guidance of high-altitude bullets.

In pursuit of stable operation in thermal hydraulic engineering, this paper researches two-phase flow through tube bundles by the lattice Boltzmann method. The single-relaxation-time lattice Boltzmann model and the multicomponent, multiphase pseudo-potential lattice Boltzmann model are adopted. Specifically, the two-phase flow past double-tandem circular cylinders, double-parallel circular cylinders, in-line tube bundles and staggered tube bundles with various spacing ratios is investigated. The vortex variable distribution and vortex shedding law under different working conditions are further compared to obtain the time-averaged drag, oscillating drag and Strouhal number. The variation law of lift coefficient and lift power spectrum with different spacing ratios is revealed. With increasing spacing ratio, the mutual interference between the double-tandem circular cylinders is weakened, the amplitude of the time-averaged drag on the double-parallel circular cylinders is reduced and the lift power spectrum changes from a double-peak distribution to a single-peak structure. The drag and lift of the center column of in-line tube bundles are lower than those on both sides, while the oscillating drag of the odd-row columns of staggered tube bundles is higher than that of even-row columns. These results contribute toward laying a solid foundation for future research on multiphase tube bundle flow issues in nuclear engineering.

Abstract Objective To compare the clinical efficacy of minimally invasive percutaneous treatment by ligamentotaxis with traditional open reduction and internal fixation in the treatment of Essex-Lopresti joint depression-type displaced intra-articular calcaneal fractures (DIACFs). Methods The medical records of patients with calcaneal fractures admitted to our department from January 2016 to December 2020 were retrospectively analyzed, and patients who met the inclusion criteria were finally included for analysis. Twenty-one patients underwent minimally invasive percutaneous treatment by ligamentotaxis (Group A), while eighteen patients were treated by traditional open reduction and internal fixation through an extended lateral approach (Group B). The preoperative waiting time, operative time, hospital stay, radiologic parameters (calcaneal height, width, length, Böhler angle and Gissane angle), American Foot and Ankle Surgery Association (AOFAS) hindfoot scores, Maryland Foot Score (MFS), visual analogue scale (VAS), and incidence of complications of the included patients were all recorded and analysed. Results Thirty-nine patients with Essex-Lopresti joint depression type DIACFs were finally included. According to the Sanders classification, 22 were type II, 12 were type III and 5 were type IV. The preoperative waiting time and the hospital stay of Group A were 3.7 ± 1.6 d and 7.2 ± 1.7 d, respectively, which were significantly shorter than those of Group B (6.9 ± 2.0 d and 12.4 ± 1.5 d) (P 0.05). No significant differences were shown in the radiological parameters (calcaneal height, width, length, Böhler angle and Gissane angle) or the satisfactory rate of joint surface reduction (SRJSR) of the two groups immediately postoperatively. All patients were followed up for 14 to 56 months [(30.2 ± 10.4) months]. All fractures healed. At the final follow-up, there were no significant differences in the radiological parameters or the SRJSR between the two groups (P > 0.05). No significant differences were shown in the AOFAS scores, MFS or VAS scores between the two groups [(89.5 ± 8.2) vs. (89.4 ± 9.0), P > 0.05; (87.5 ± 8.3) vs. (86.3 ± 8.9), P > 0.05; and (2.1 ± 1.2) vs. (2.2 ± 1.2), P > 0.05]. The excellent and good rates of the AOFAS scores and MFS were 90.5% and 85.7%, respectively, in Group A and 88.9% and 88.9%, respectively, in Group B (P > 0.05). Four patients experienced wound complications, including 1 superficial incision infection, 2 skin necrosis around the incision edge and 1 deep infection in Group B, while there were no wound complications in Group A (P 0.05). Conclusions In the assessment of Essex-Lopresti joint depression type DIACFs, minimally invasive percutaneous treatment by ligamentotaxis has similar clinical outcomes to traditional open reduction and internal fixation through an extended lateral approach. However, the former has the advantages of shorter preoperative waiting time and hospital stay, and lower incidence of incision complications.

Previous work has reported an axion insulator state in a layered topological antiferromagnet MnBi2Te4 evidenced by a zero Hall plateau. Here, in addition to the zero Hall plateau, the authors identify edge states in transport measurements at zero field which challenge the axion insulator interpretation.

One remarkable trend in applying synthetic aperture radar technology is the automatic interpretation of Synthetic Aperture Radar (SAR) images. The electromagnetic scattering characteristics have a robust correlation with the target structure, which provides key support for SAR image interpretation. Therefore, elucidating how to extract accurate electromagnetic characteristics and how to use these electromagnetic characteristics to retrieve target characteristics has been widely valued recently. This study discusses the research accomplishments, summarizes the key elements and ideas of electromagnetic characteristic extraction and electromagnetic-characteristic-based target recognition, and details the extension applications of the electromagnetic scattering mechanism in imaging and recognition. Finally, the future research direction of electromagnetic scattering characteristic extraction and application was proposed.

Abstract The cosmic‐ray neutron probe (CRNP) is a mesoscale and noninvasive method for measuring soil moisture and has been widely studied and applied. However, studies of its applicability in rocky mountainous areas are still challenging in complex topography and high gravel content. In this study, a field experiment was carried out to assess the applicability of the CRNP for measuring soil moisture in rocky areas of Taihang Mountains of North China. The results showed that the Pearson correlation coefficient and the root mean square error between the soil moisture from CRNP and the drying method are 0.911 and 0.025 m3 m−3, respectively, indicating that the CRNP can estimate the average soil moisture well in the study area. Compared with the capacitive sensor, the CRNP overestimated soil moisture when small rainfall events occurred, which was caused by the interception of canopy and litter. The nonlinear weighting method performed better than the linear weighting method in representing average soil moisture within the CRNP footprint. The high gravel content that contained high lattice water content reduced the penetration depth of CRNP. Biomass reduces the accuracy of the CRNP by affecting the neutron intensity. In summary, CRNP can measure soil moisture accurately in rocky areas of the Taihang Mountains, especially in dry environments with low biomass.

Objective: Defining the regulators of cell metabolism and signaling is essential to design new therapeutic strategies in obesity and NAFLD/NASH. E3 ubiquitin ligases control diverse cellular functions by ubiquitination-mediated regulation of protein targets, and thus their functional aberration is associated with many diseases. The E3 ligase Ube4A has been implicated in human obesity, inflammation, and cancer. However, its in vivo function is unknown, and no animal models are available to study this novel protein. Methods: A whole-body Ube4A knockout (UKO) mouse model was generated, and various metabolic parameters were compared in chow- and high fat diet (HFD)-fed WT and UKO mice, and in their liver, adipose tissue, and serum. Lipidomics and RNA-Seq studies were performed in the liver samples of HFD-fed WT and UKO mice. Proteomic studies were conducted to identify Ube4A's targets in metabolism. Furthermore, a mechanism by which Ube4A regulates metabolism was identified. Results: Although the body weight and composition of young, chow-fed WT and UKO mice are similar, the knockouts exhibit mild hyperinsulinemia and insulin resistance. HFD feeding substantially augments obesity, hyperinsulinemia, and insulin resistance in both sexes of UKO mice. HFD-fed white and brown adipose tissue depots of UKO mice have increased insulin resistance and inflammation and reduced energy metabolism. Moreover, Ube4A deletion exacerbates hepatic steatosis, inflammation, and liver injury in HFD-fed mice with increased lipid uptake and lipogenesis in hepatocytes. Acute insulin treatment resulted in impaired activation of the insulin effector protein kinase Akt in liver and adipose tissue of chow-fed UKO mice. We identified the Akt activator protein APPL1 as a Ube4A interactor. The K63-linked ubiquitination (K63-Ub) of Akt and APPL1, known to facilitate insulin-induced Akt activation, is impaired in UKO mice. Furthermore, Ube4A K63-ubiquitinates Akt in vitro. Conclusion: Ube4A is a novel regulator of obesity, insulin resistance, adipose tissue dysfunction and NAFLD, and preventing its downregulation may ameliorate these diseases.

Abstract Antigen delivery based on non‐virus‐like particle self‐associating protein nanoscffolds, such as Aquifex aeolicus lumazine synthase (AaLS), is limited due to the immunotoxicity and/or premature clearance of antigen‐scaffold complex resulted from triggering unregulated innate immune responses. Here, using rational immunoinformatics prediction and computational modeling, we screen the T epitope peptides from thermophilic nanoproteins with the same spatial structure as hyperthermophilic icosahedral AaLS, and reassemble them into a novel thermostable self‐assembling nanoscaffold RPT that can specifically activate T cell‐mediated immunity. Tumor model antigen ovalbumin T epitopes and the severe acute respiratory syndrome coronavirus 2 receptor‐binding domain are loaded onto the scaffold surface through the SpyCather/SpyTag system to construct nanovaccines. Compared to AaLS, RPT‐constructed nanovaccines elicit more potent cytotoxic T cell and CD4+ T helper 1 (Th1)‐biased immune responses, and generate less anti‐scaffold antibody. Moreover, RPT significantly upregulate the expression of transcription factors and cytokines related to the differentiation of type‐1 conventional dendritic cells, promoting the cross‐presentation of antigens to CD8+ T cells and Th1 polarization of CD4+ T cells. RPT confers antigens with increased stability against heating, freeze‐thawing, and lyophilization with almost no antigenicity loss. This novel nanoscaffold offers a simple, safe, and robust strategy for boosting T‐cell immunity‐dependent vaccine development.

Electronic Health Records (EHRs) contain unprecedented volumes of data related to health such as diagnosis and treatment information. Based on the technologies of named entity recognition (NER), EHRs mining has become a research focus in health domain. Nevertheless, the complex medical entities are challenging to be recognized, especially in Chinese EHRs. In this paper, a Glyph and Word Boundary-based Named Entity Recognition (GWBNER) method is presented, which takes into account both the Chinese character glyph and word boundary features in Chinese EHRs. Specifically, the character glyphs are utilized to capture the character-level global structural features, and the word boundaries are adopted to extract the word-level local structural features in Chinese. Therefore, the medical entities are fully recognized with rich semantic features from diverse perspectives in Chinese EHR texts. Finally, we conduct extensive experiments to evaluate the novel approach, and the F1 scores of 0.879, 0.998 and 0.962 are achieved on three EHR datasets, respectively. The experimental results demonstrate the optimal performance of the GWBNER method compared with the state-of-the-art models in NER community.

The authors of this Comment are longstanding selenium investigators with a total of 200 or more published articles on selenium; the corresponding author (Margaret P [...]

Monitoring drought stress is crucial for estimating productivity and assessing the health status of forest ecosystems under global climate change. Solar-induced chlorophyll fluorescence (SIF) is mechanistically coupled to photosynthesis and has advantages over greenness-based vegetation indices in detecting drought. In recent years, SIF has commonly been used in monitoring drought stress in crop ecosystems. However, the response of tower-based SIF to drought stress in forest ecosystems remains unclear. In this study, we investigated the potential of tower-based SIF to monitor drought, which was quantified using the plant water stress index (PWSI) in a Chinese cork oak plantation. The results show the negative effect of drought on SIF, and afternoon depression of SIF emission under drought stress was observed. Canopy SIF (F) exhibited a nonlinear relationship with PWSI, while the quantum yield of SIF (ΦF) exhibited a significant linear relationship with PWSI at 687 nm and 760 nm (ΦF687: R2 = 0.90; ΦF760: R2 = 0.85). Incident radiation (PAR) and canopy structure affected the response of SIF to drought stress, with PAR as the main factor causing the nonlinear relationship between F and PWSI. Afternoon depression was described as the afternoon/morning ratio (AMR). AMRF and AMRΦF exhibited a negative linear response to PWSI. AMRF was less affected than F by PAR and canopy structures, and AMRΦF was more physiologically representative than ΦF. Moreover, AMRΦF was sensitive to VPD and REW, and it might be a good indicator of drought. Red SIF was more sensitive to drought than far-red SIF, as the R2 of PWSI with AMRΦF687 (R2 = 0.89) was higher than that with AMRΦF687 (R2 = 0.84). These results highlight the potential of tower-based SIF, especially red SIF, for drought monitoring in a plantation, and consideration of the physiological diurnal variation in SIF under drought stress is crucial for improving the accuracy of drought stress monitoring.

Smoking is a pathogenic factor for pulmonary hypertension (PH). Our previous study showed that serum miR-21 levels are elevated in smokers. miR-21 is considered as engaged in the PH process; however, its mechanisms remain unclear. In this investigation, we found that in the lung tissue of smoking-induced PH patients, the levels of miR-21 and aging markers (p21 and p16) were upregulated, and the function of pulmonary vascular endothelial cells was also impaired. Exposure of mice to cigarette smoke (CS) for four months caused similar changes in lung tissues and increased pulmonary arterial pressure, which were attenuated by knockout of miR-21. Further, human umbilical vein endothelial cells (HUVECs) exposed to cigarette smoke extract (CSE) revealed upregulation of miR-21 levels, depression of PTEN, activation of PI3K/AKT/mTOR signaling, an increase in senescence indexes, and enhanced dysfunction. Inhibiting miR-21 overexpression reversed the PTEN-mTOR signaling pathway and prevented senescence and dysfunction of HUVECs. In sum, our data indicate that miR-21-mediated endothelial senescence and dysfunction are involved in CS-induced PH through the activation of PI3K/AKT/mTOR signaling, which suggests that selective miR-21 inhibition offers the potential to attenuate PH.

Point cloud registration is an important task in computer vision and robotics which is widely used in 3D reconstruction, target recognition, and other fields. At present, many registration methods based on deep learning have better registration accuracy in complete point cloud registration, but partial registration accuracy is poor. Therefore, a partial point cloud registration network, HALNet, is proposed. Firstly, a feature extraction network consisting mainly of adaptive graph convolution (AGConv), two-dimensional convolution, and convolution block attention (CBAM) is used to learn the features of the initial point cloud. Then the overlapping estimation is used to remove the non-overlapping points of the two point clouds, and the hybrid attention mechanism composed of self-attention and cross-attention is used to fuse the geometric information of the two point clouds. Finally, the rigid transformation is obtained by using the fully connected layer. Five methods with excellent registration performance were selected for comparison. Compared with SCANet, which has the best registration performance among the five methods, the RMSE(R) and MAE(R) of HALNet are reduced by 10.67% and 12.05%. In addition, the results of the ablation experiment verify that the hybrid attention mechanism and fully connected layer are conducive to improving registration performance.