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To elucidate the genetic factors contributing to melon resistance against Phelipanche aegyptiaca and comprehend the role of differentially resistant materials in responding to changes in P. aegyptiaca parasitisation, we investigated the P. aegyptiaca-resistant line K16 and the susceptible line K27. The parasitism rate of P. aegyptiaca was assessed at 25 days. Results revealed significant differences in parasitisation rates between K16 (15.35%) and K27 (34.2%). We compared inoculated K16 and K27 to their respective controls through transcriptome analysis and contrasted inoculated K16 with inoculated K27. Eight hundred eighteen genes exhibited differential expression across all comparisons. Gene ontology (GO) functional enrichment analysis revealed that differentially expressed genes were significantly enriched in nitrate transport and assimilation, cellular components, extracellular regions, binding and enzyme activities. KEGG pathway enrichment underscored the importance of phytohormone signaling, phenylpropanoid biosynthesis, linolenic acid and linoleic acid metabolism, cyanoamino acid metabolism and nitrogen metabolism in the interaction between melon and P. aegyptiaca. Nine genes potentially associated with P. aegyptiaca resistance were identified, encoding cytochrome protein P450, peroxidases, β-glucosidase, acyltransferase family proteins, histidine phosphotransfer protein, and D-type cyclins. This study aims to provide insights into the mechanism of P. aegyptiaca parasitism on melons and offers implications for breeding resistant varieties

Abstract Background The COVID-19 pandemic has caused a large mortality and morbidity burden globally. For individuals, a strong immune response is the most effective means to block SARS-CoV-2 infection. To inform clinical case management of COVID-19, development of improved vaccines, and public health policy, a better understanding of antibody response dynamics and duration following SARS-CoV-2 infection and after vaccination is imperatively needed. Methods We systematically analyzed antibody response rates in naturally infected COVID-19 patients and vaccinated individuals. Specifically, we searched all published and pre-published literature between 1 December 2019 and 31 July 2023 using MeSH terms and “all field” terms comprising “COVID-19” or “SARS-CoV-2,” and “antibody response” or “immunity response” or “humoral immune.” We included experimental and observational studies that provided antibody positivity rates following natural COVID-19 infection or vaccination. A total of 44 studies reporting antibody positivity rate changes over time were included. Results The meta-analysis showed that within the first week after COVID-19 symptom onset/diagnosis or vaccination, antibody response rates in vaccinated individuals were lower than those in infected patients (p

Abstract Salmonella enterica serovar Typhimurium (S. Typhimurium) is a major cause of salmonellosis, and the emergence of multidrug-resistant pathovariants has become a growing concern. Here, we investigate a distinct rough colony variant exhibiting a strong biofilm-forming ability isolated in China. Whole-genome sequencing on 2,212 Chinese isolates and 1,739 publicly available genomes reveals the population structure and evolutionary history of the rough colony variants. Characterized by macro, red, dry, and rough (mrdar) colonies, these variants demonstrate enhanced biofilm formation at 28 °C and 37 °C compared to typical rdar colonies. The mrdar variants exhibit extensive multidrug resistance, with significantly higher resistance to at least five classes of antimicrobial agents compared to non-mrdar variants. This resistance is primarily conferred by an IncHI2 plasmid harboring 19 antimicrobial resistance genes. Phylogenomic analysis divides the global collections into six lineages. The majority of mrdar variants belong to sublineage L6.5, which originated from Chinese smooth colony strains and possibly emerged circa 1977. Among the mrdar variants, upregulation of the csgDEFG operons is observed, probably due to a distinct point mutation (−44G > T) in the csgD gene promoter. Pangenome and genome-wide association analyses identify 87 specific accessory genes and 72 distinct single nucleotide polymorphisms associated with the mrdar morphotype.

Abstract Background The lumbar vertebra and paraspinal muscles play an important role in maintaining the stability of the lumbar spine. Therefore, the aim of this study was to investigate the relationship between paraspinal muscles fat infiltration and vertebral body related changes [vertebral bone quality (VBQ) score and Modic changes (MCs)] in patients with chronic low back pain (CLBP). Methods Patients with CLBP were prospectively collected in four hospitals and all patients underwent 3.0T magnetic resonance scanning. Basic clinical information was collected, including age, sex, course of disease (COD), and body mass index (BMI). MCs were divided into 3 types based on their signal intensity on T1 and T2-weighted imaging. VBQ was obtained by midsagittal T1-weighted imaging (T1WI) and calculated using the formula: SIL1−4/SICSF. The Proton density fat fraction (PDFF) values and cross-sectional area (CSA) of paraspinal muscles were measured on the fat fraction map from the iterative decomposition of water and fat with the echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) sequences and in/out phase images at the central level of the L4/5 and L5/S1 discs. Results This study included 476 patients with CLBP, including 189 males and 287 females. 69% had no Modic changes and 31% had Modic changes. There was no difference in CSA and PDFF for multifidus(MF) and erector spinae (ES) at both levels between Modic type I and type II, all P values>0.05. Spearman correlation analysis showed that VBQ was weakly negatively correlated with paraspinal muscles CSA (all r values

With the advancement of Fuel Cell Vehicles (FCVs), detecting hydrogen leaks is critically important in facilities such as hydrogen refilling stations. Despite its significance, the dynamic response performance of hydrogen sensors in confined spaces, particularly under ceilings, has not been comprehensively assessed. This study utilizes a catalytic combustion hydrogen sensor to monitor hydrogen leaks in a confined area. It examines the effects of leak size and placement height on the distribution of hydrogen concentrations beneath the ceiling. Results indicate that hydrogen concentration rapidly decreases within a 0.5–1.0 m range below the ceiling and declines more gradually from 1.0 to 2.0 m. The study further explores the attenuation pattern of hydrogen concentration radially from the hydrogen jet under the ceiling. By normalizing the radius and concentration, it was determined that the distribution conforms to a Gaussian model, akin to that observed in open space jet flows. Utilizing this Gaussian assumption, the model is refined by incorporating an impact reflux term, thereby enhancing the accuracy of the predictive formula.

Abstract The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1low/CAMSAP3low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1low/CAMSAP3low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients.

Abstract Background Acute coronary syndrome due to coronary artery embolism in the setting of ascending aortic thrombus is an uncommon condition, even rarer when there is no aortic pathology such as aneurysm, severe atherosclerosis, aortic dissection, or thrombophilia (whether inherited or acquired). Case presentation We report a case of a 58-year-old male presented with acute chest pain, electrocardiogram showing non-ST-elevation acute coronary syndrome. The computed tomography angiography of coronary artery revealed a mural thrombus in the proximal part of ascending aorta, located above the left coronary artery ostium, without any aortic pathologies. With the exception of hypertension and cigarette smoking, no other risk factors were identified in this patient that may increase the risk of thrombosis. Given the life-threatening risk of interventional therapy and surgery, the patient determinedly opted for anticoagulant and dual antiplatelet therapy. Then he experienced the reoccurrence of chest pain after 6-day treatment, progressed to anterior and inferior ST-segment elevation myocardial infarction. Coronary artery embolism originating from the ascending aortic thrombus was suspected. Considering the hemodynamic instability of the patient, the medical treatment was continued and bridged to warfarin and aspirin after discharge. Follow-up computed tomography angiography at 6 months showed no obstruction in coronary artery and complete resolution of the thrombus. No thromboembolic events occurred henceforward. Conclusions Acute coronary syndrome could be a manifestation of secondary coronary embolism due to ascending aortic thrombus. Currently, there is no standardized guideline for the treatment of aortic mural thrombus, individualized treatment is recommended. When surgical therapy is not applicable for the patient, anticoagulation and dual antiplatelet treatment are alternative treatments that may successfully lead to the resolution of the aortic thrombus.

Abstract Observational studies indicate that serum sex hormone-binding globulin (SHBG) levels are inversely correlated with blood lipid levels and coronary heart disease (CHD) risk. Given that dyslipidemia is an established risk factor for CHD, we aim to employ Mendelian randomization (MR) in conjunction with mediation analysis to confirm the mediating role of blood lipid levels in the association between SHBG and CHD. First, we assessed the causality between serum SHBG levels and five cardiovascular diseases using univariable MR. The results revealed causality between SHBG levels and reduced risk of CHD, myocardial infarction, as well as hypertension. Specifically, the most significant reduction was observed in CHD risk, with an odds ratio of 0.73 (95% CI 0.63–0.86) for each one-standard-deviation increase in SHBG. The summary-level data of serum SHBG levels and CHD are derived from a sex-specific genome-wide association study (GWAS) conducted by UK Biobank (sample size = 368,929) and a large-scale GWAS meta-analysis (60,801 cases and 123,504 controls), respectively. Subsequently, we further investigated the mediating role of blood lipid level in the association between SHBG and CHD. Mediation analysis clarified the mediation proportions for four mediators: high cholesterol (48%), very low-density lipoprotein cholesterol (25.1%), low-density lipoprotein cholesterol (18.5%), and triglycerides (44.3%). Summary-level data for each mediator were sourced from the UK Biobank and publicly available GWAS. The above results confirm negative causality between serum SHBG levels and the risk of CHD, myocardial infarction, and hypertension, with the causal effect on reducing CHD risk largely mediated by the improvement of blood lipid profiles.

Summary: Clinical research indicates that SARS-CoV-2 infection is linked to several neurological consequences, and the virus is still spreading despite the availability of vaccinations and antiviral medications. To determine how hosts respond to SARS-CoV-2 infection, we employed LC-MS/MS to perform ubiquitinome and proteome analyses of the brain cortexes from K18-hACE2 mice in the presence and absence of SARS-CoV-2 infection. A total of 8,024 quantifiable proteins and 5,220 quantifiable lysine ubiquitination (Kub) sites in 2023 proteins were found. Glutamatergic synapse, calcium signaling pathway, and long-term potentiation may all play roles in the neurological consequences of SARS-CoV-2 infection. Then, we observed possible interactions between 26 SARS-CoV-2 proteins/E3 ubiquitin-protein ligases/deubiquitinases and several differentially expressed mouse proteins or Kub sites. We present the first description of the brain cortex ubiquitinome in K18-hACE2 mice, laying the groundwork for further investigation into the pathogenic processes and treatment options for neurological dysfunction following SARS-CoV-2 infection.

Abstract Anaerobic digestion (AD) was initially evaluated as a potential preprocessing method for preparing biomass-based carbon electrocatalysts in this study. The AD pretreatment succeeded in the structural depolymerization and nitrogen enrichment of Hybrid Pennisetum, which provided favorable conditions to achieve efficient and homogeneous nitrogen introduction due to microorganism community enrichment and provided a porous structure by degradation of the biodegradable components. The resulted biochar exhibited improved physiochemical properties including higher specific surface areas, nitrogen content and graphitization degree than that obtained from pyrolyzing raw biomass. These improvements were positively correlated with the AD time and showed to have enhanced the performance in oxygen reduction reaction and practical microbial fuel cell applications. Amongst the investigated samples, the obtained biochar pretreated by AD for 15 days exhibited the most excellent performance with an onset potential of 0.17 V (VS. saturated calomel electrode) and the maximal power density of 543.2 mW cm−2 assembled in microbial fuel cells. This study suggested applying AD as a new biological pretreatment in the preparation of biomass-based electrocatalysts, and provided a unique pathway for fabricating high-performance biochar-based catalysts by structure optimization and N-containing active sites construction via gentle biological method, thereby providing a cost-effective method to fabricate metal-free catalysts for oxygen reduction reaction. Graphical Abstract

Point cloud registration is a necessary prerequisite for conducting precise, large-scale forest surveys and management. This paper focuses on providing a systematic overview and summary of the work on forest point cloud registration over the past 20 years. The developmental process of forest point cloud registration methods, spanning from the early reliance on manual markers to the subsequent evolution towards automatic registration based on feature matching, and then to the advanced technology based on deep learning were reviewed. Furthermore, the paper offered detailed discussions on the registration between different point cloud platforms: ground platforms, between ground platforms and aerial platforms, and between aerial platforms. Additionally, the paper delved into mainstream datasets and evaluation metrics in the domain of forest point cloud registration. Finally, the paper summarized the current state of research in this area, highlighted challenges, and provided future research outlooks. This review aims to provide researchers with a comprehensive understanding of forest point cloud registration, and to promote the advancement of point cloud technology, hopefully inspiring further applications in the field.

Abstract Previous intervention studies have shown some benefits of dark chocolate for the cardiovascular system, but it has not been established whether dark chocolate intake is associated with the risk of cardiovascular diseases (CVDs). To investigate the causality between dark chocolate intake and the risk of CVDs, a Mendelian randomization (MR) study was conducted. We obtained summary-level data on dark chocolate intake and CVDs from publicly available genome-wide association studies. In this MR study, the main approach was to use a fixed-effect model with inverse variance weighted (IVW) and evaluate the robustness of the results via sensitivity analysis. We found that dark chocolate intake was significantly associated with the reduction of the risk of essential hypertension (EH) (OR = 0.73; 95% CI 0.60–0.88; p = 1.06 × 10−3), as well as with the suggestive association to the reduced risk of venous thromboembolism (OR = 0.69; 95% CI 0.50–0.96; p = 2.81 × 10−2). However, no association was found between dark chocolate intake and the other ten CVDs. Our study provides evidence for a causality between dark chocolate intake and a reduced risk of EH, which has important implications for the prevention of EH in the population.

Abstract This study investigates the importance of preconditioning in Pt‐alloy catalysts for oxygen–reduction reactions. Previous research indicated that slower scanning rates during preconditioning initially boost activity, but this is followed by a rapid decline. The study reveals the required number of cycles to achieve the first constant steady state activity in PtNiMo/C catalysts when using slower scanning rates during preconditioning. It also highlights the resulting activity differences. Remarkably, a catalyst preconditioned with 150 slow cycles showed an activity of approximately 1.25 mA cm−2 at 0.90 VRHE, significantly higher than one preconditioned at a fast rate (0.82 mA cm−2). Both ex–situ and in–situ analyses revealed that Pt, along with Ni, was leached during pretreatment. At slower scan rates of 20 mV s−1, the dissolved Pt redeposited as highly active, small‐sized clusters or single atoms. Fast scan rates of of 500 mV s−1, in contrast, resulted in fewer such clusters. Accelerated stress tests up to 1.10 VRHE confirmed the high stability of these clusters, demonstrating a substantial activity increase even after 24,000 cycles.

Tillage practices significantly influence crop yield and soil quality. This study investigated the impact of rotary tillage (RT) and deep tillage (DT) on soil properties, microbial diversity, and melon (Cucumis melo L.) root growth and yield. RT involved breaking up the topsoil to a depth of 15 cm using a rotary tiller, while DT employed a rotary tiller followed by a moldboard plow to turn the soil layer over to a depth of 35 cm. The melon variety “Nasimi” was used as the material. Our findings revealed a remarkable response of soil phosphorus to tillage practices. High-throughput sequencing results revealed a significant impact of tillage practices on the soil fungal composition, richness, and diversity but little impact on the bacterial communities. Compared to RT, DT markedly enhanced melon root length, root surface area, root volume, and mean root diameter by 47.42%, 56.70%, 58.83%, and 27.28%, respectively. Additionally, DT treatments significantly increased melon yield (53.46%) compared to RT. The results indicate that DT improves soil nutrient availability, affects soil fungal community characteristics, and optimizes root distribution in soil, thereby improving melon yield. The findings offer valuable theoretical insights for the implementation of effective tillage practices in open-field melon cultivation.

Understanding public preferences concerning vaccination is critical to inform pandemic response strategies. To investigate Chinese adults' preferences regarding COVID-19 vaccine attributes, we conducted a cross-sectional online survey in 12,000 Chinese adults in June-July, 2021. Participants were requested to answer a series of discrete choice questions related to hypothetical COVID-19 vaccines. Using mixed logit models, our analysis revealed that participants had a higher preference for COVID-19 vaccines with longer duration of protection (coefficient: 1.272, 95% confidence interval [1.016 to 1.529]) and higher efficacy (coefficient: 1.063, [0.840, 1.287]). Conversely, participants demonstrated a lower preference associated with higher risk of rare but serious side-effects (coefficient: -1.158, [-1.359, -0.958]), oral administration (coefficient: -0.211, [-0.377, -0.046]), more doses (coefficient: -0.148, [-0.296, 0.000]) and imported origin (coefficient: -0.653, [-0.864, -0.443]). Moreover, preferences were heterogeneous by individual factors: highly educated participants were more sensitive to the negative vaccine attributes including price (coefficient -0.312, [-0.370, -0.253]) and imported vaccine (coefficient -0.941, [-1.186, -0.697]); there was also substantial heterogeneity in vaccine preferences with respect to age group, marital status, work status, income, chronic diagnosis history, COVID-19 vaccination history and geographic regions. As the first study of examining the public preferences for COVID-19 vaccine in China with a large nationwide sample of 12,000 adults, our results indicate that future vaccine should pose lower risk, possess longer protection period, have higher efficacy, be domestically produced, and have lower costs to increase the COVID-19 vaccination coverage. Our current study findings from this study provide insights and recommendations for not only COVID-19 vaccine design but also vaccine attribute preferences to increase vaccine uptake in potential future pandemics.

The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson’s product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

BackgroundIvabradine improves cardiac function in patients with heart failure, but its effect on dilated cardiomyopathy (DCM) remains unclear. We performed a systematic review and meta-analysis to study the efficacy and potential mechanisms of ivabradine's effect on cardiac function and prognosis in patients with DCM.MethodsWe searched PubMed, Cochrane Library, Embase, Web of Science, and four registers through September 28, 2022. All controlled trials of ivabradine for the treatment of DCM with congestive heart failure were included. Articles were limited to English, with the full text and necessary data available. We performed random- or fixed effects meta-analyses for all included outcome measures and compared the effect sizes for outcomes in patients treated with and without ivabradine. The quality of the studies was assessed using the Cochrane risk-of-bias tool for randomized trials (RoB2.0).FindingsFive trials with 357 participants were included. The pooled risk ratio was 0.48 [95% confidence interval (CI) (0.18, 1.25)] for all-cause mortality and 0.38 [95% CI (0.12, 1.23)] for cardiac mortality. The pooled mean difference was −15.95 [95% CI (−19.97, −11.92)] for resting heart rate, 3.96 [95% CI (0.99, 6.93)] for systolic blood pressure, 2.93 [95% CI (2.09, 3.77)] for left ventricular ejection fraction, −5.90 [95% CI (−9.36, −2.44)] for left ventricular end-systolic diameter, −3.41 [95% CI (−5.24, −1.58)] for left ventricular end-diastolic diameter, −0.81 [95% CI (−1.00, −0.62)] for left ventricular end-systolic volume, −0.67 [95% CI (−0.86, −0.48)] for left ventricular end-diastolic volume, −11.01 [95% CI (−19.66, −2.35)] for Minnesota Living with Heart Failure score, and −0.52 [95% CI (−0.73, −0.31)] for New York Heart Association class.InterpretationIvabradine reduces heart rate and ventricular volume, and improves cardiac function in patients with DCM, but showed no significant effect on the prognosis of patients.

Abstract The profile of antibodies against antigenic epitopes of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) during neutralizing antibody (NAb) decay has not been clarified. Using a SARS‐CoV‐2 proteome microarray that contained viral antigenic peptides, we analyzed the characteristics of the humoral response in patients with coronavirus disease 19 (COVID‐19) in a longitudinal study. A total of 89 patients were recruited, and 226 plasma samples were serially collected in 2020. In the antigenic peptide microarray, the level of immunoglobulin G (IgG) antibodies against peptides within the S2 subunit (S‐82) and a conserved gene region in variants of interest, open reading frame protein 10 (ORF10‐3), were closely associated with the presence of SARS‐CoV‐2 NAbs. In an independent evaluation cohort of 232 plasma samples collected from 116 COVID‐19 cases in 2020, S82‐IgG titers were higher in NAbs‐positive samples (p = 0.002) than in NAbs‐negative samples using enzyme‐linked immunosorbent assay. We further collected 66 plasma samples from another cohort infected by Omicron BA.1 virus in 2022. Compared with the samples with lower S82‐IgG titers, NAb titers were significantly higher in the samples with higher S82‐IgG titers (p = 0.04). Our findings provide insights into the understanding of the decay‐associated signatures of SARS‐CoV‐2 NAbs.

The semantic segmentation of light detection and ranging (LiDAR) point clouds plays an important role in 3-D scene intelligent perception and semantic modeling. The unstructured, sparse and uneven characteristics of point clouds pose great challenges to the representation of the local geometric shapes, which degrades semantic segmentation performance. To address the challenges of describing local geometric shapes due to unstructured and sparse 3-D point clouds, this article proposes a category-contrastive-guided graph convolutional network (CGGC-Net) for the semantic segmentation of LiDAR point clouds. First, a detailed geometric structure of the raw point clouds is encoded to represent the inherent geometric pattern within the local neighborhood. At the same time, the geometric structures information is transmitted across multiple layers, so that the geometric structure encoding information containing different receptive fields and richer neighborhood spatial structure can be aggregated. Following this, the graph convolution neural network uses the edge convolution layer to adaptively describe the semantic correlation between the query point and its neighboring points, and combines the attention mechanism to gather the surrounding feature information to the query point. As a result, the graph convolution neural network and attention mechanism are iteratively stacked for the aggregation and fusion of spatial context semantic information, to generate highly discriminative semantic feature representation. Finally, the superparameters of the model are learned through a multitask optimization strategy guided by category-aware contrastive loss and cross-entropy loss. Experiments are conducted on the public SemanticKITTI dataset and the Stanford large-scale 3-D Indoor Spaces dataset to demonstrate the effectiveness and reliability of the proposed CGGC-Net from both quantitative and qualitative perspectives. The results indicate its capability of automatically classifying LiDAR point clouds, with a mean intersection-over-union of 58.4%. Moreover, multiple comparative experiments also demonstrate the superior performance of the proposed method, exceeding state-of-the-art methods.

Shigella sonnei is a major contributor to the global burden of dysenteric diarrhoea in developed countries. In this work, authors report a S. sonnei clone which has caused numerous waterborne shigellosis outbreaks in China from 2015 to 2020.

In order to shorten the response time of forest fire (FF) rescue and improve the efficiency of forest fire prevention and control, this paper proposes a fire station spatial layout location method based on the spatial distribution of FF comprehensive risk. A comprehensive forest fire risk assessment model has been developed by three perspectives, namely the risk factors of forest fires, exposure degree of the disaster-bearing body, and vulnerability of the disaster-bearing body. The location-assignment model was introduced to scientifically plan the spatial layout of FF stations from different perspectives. Zhuhai in Guangdong Province, China, a typical “city surrounded by mountains,” was chosen as the research site for case analysis. The results show that the high-risk areas of Zhuhai are mainly concentrated in the forests of Xiangzhou District and Gaoxin District; the medium-risk areas mainly lie in the forests in the north of Gaoxin District, the forests of Hengqin Zone and some forests of Doumen District. Through a combination of the minimized facility point model and the maximized coverage model, it is calculated that 9 FF stations are the optimal choice, which can meet Zhuhai's demand for FF prevention and control with an acceptable construction cost and good coverage.

Abstract Pt‐based alloy catalysts supported on carbon are commonly characterized for oxygen reduction reaction (ORR) activity using the rotating disk electrode technique (RDE). Within this study, we show exemplarily for PtNiMo/C catalysts that the applied pretreatment influences strongly the determined activity. The classically employed descriptor of unchanged cyclic voltammetry response is insufficient to portrait completed surface restructuring, and gives an incorrect impression that stable activity can be determined. This might be one of the reasons for the strongly deviating activities reported in literature. Following the changes in activity during pretreatment also with in‐situ FTIR and online dissolution measurements gives insights to an up to now largely overseen high activity of the trimetallic catalysts. A maximum activity of 0.57 mA cmPt−2 at 0.95 VRHE is reached quickly during the first six cycles and decreases slowly subsequently. The maximum activity and change of activity over the cycle number is affected by the scan rate and electrolyte refreshing, while the gas atmosphere plays only a minor role. This exemplary study might be important for Pt alloy catalysts in general.

ABSTRACT Influenza A virus (IAV)–methicillin-resistant Staphylococcus aureus (MRSA) coinfection causes severe respiratory infections. The host microbiome plays an important role in respiratory tract infections. However, the relationships among the immune responses, metabolic characteristics, and respiratory microbial characteristics of IAV-MRSA coinfection have not been fully studied. We used specific-pathogen-free (SPF) C57BL/6N mice infected with IAV and MRSA to build a nonlethal model of IAV-MRSA coinfection and characterized the upper respiratory tract (URT) and lower respiratory tract (LRT) microbiomes at 4 and 13 days postinfection by full-length 16S rRNA gene sequencing. Immune response and plasma metabolism profile analyses were performed at 4 days postinfection by flow cytometry and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The relationships among the LRT microbiota, the immune response, and the plasma metabolism profile were analyzed by Spearman’s correlation analysis. IAV-MRSA coinfection showed significant weight loss and lung injury and significantly increased loads of IAV and MRSA in bronchoalveolar lavage fluid (BALF). Microbiome data showed that coinfection significantly increased the relative abundances of Enterococcus faecalis, Enterobacter hormaechei, Citrobacter freundii, and Klebsiella pneumoniae and decreased the relative abundances of Lactobacillus reuteri and Lactobacillus murinus. The percentages of CD4+/CD8+ T cells and B cells in the spleen; the levels of interleukin-9 (IL-9), interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), IL-6, and IL-8 in the lung; and the level of mevalonolactone in plasma were increased in IAV-MRSA-coinfected mice. L. murinus was positively correlated with lung macrophages and natural killer (NK) cells, negatively correlated with spleen B cells and CD4+/CD8+ T cells, and correlated with multiple plasma metabolites. Future research is needed to clarify whether L. murinus mediates or alters the severity of IAV-MRSA coinfection. IMPORTANCE The respiratory microbiome plays an important role in respiratory tract infections. In this study, we characterized the URT and LRT microbiota, the host immune response, and plasma metabolic profiles during IAV-MRSA coinfection and evaluated their correlations. We observed that IAV-MRSA coinfection induced severe lung injury and dysregulated host immunity and plasma metabolic profiles, as evidenced by the aggravation of lung pathological damage, the reduction of innate immune cells, the strong adaptation of the immune response, and the upregulation of mevalonolactone in plasma. L. murinus was strongly correlated with immune cells and plasma metabolites. Our findings contribute to a better understanding of the role of the host microbiome in respiratory tract infections and identified a key bacterial species, L. murinus, that may provide important references for the development of probiotic therapies.

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

IntroductionApproximately 40% of patients with acute low back pain (LBP) develop chronic low back pain, which significantly increases the risk of poor prognosis. To reduce the risk of acute LBP becoming chronic, effective preventive strategies are needed. Early identification of risk factors for the development of chronic LBP can help clinicians choose appropriate treatment options and improve patient outcomes. However, previous screening tools have not considered medical imaging findings. The aim of this study is to identify factors that can predict the risk of acute LBP becoming chronic based on clinical information, pain and disability assessment, and MRI imaging findings. This protocol describes the methodology and plan for investigating multidimensional risk factors for acute LBP becoming chronic, in order to better understand the development of acute LBP and prevent chronic LBP.MethodsThis is a prospective multicenter study. We plan to recruit 1,000 adult patients with acute low back pain from four centers. In order to select four representative centers, we find the larger hospitals from different regions in Yunnan Province. The study will use a longitudinal cohort design. Patients will undergo baseline assessments upon admission and will be followed up for 5 years to collect the time of chronicity and associated risk factors. Upon admission, patients will be collected detailed demographic information, subjective and objective pain scores, disability scale, and lumbar spine MRI scanning. In addition, patient’s medical history, lifestyle, psychological factors will be collected. Patients will be followed up at 3 months, 6 months, 1 year, 2 years and up for 5 years after admission to collect the time of chronicity and associated factors. Multivariate analysis will be used to explore the multidimensional risk factors affecting the chronicity of acute LBP patients (such as age, gender, BMI, degree of intervertebral disc degeneration, etc.), and survival analysis will be performed to explore the impact of each factor on the time of chronicity.Ethics and disseminationThe study has been approved by the institutional research ethics committee of each study center (main center number: 2022-L-305). Results will be disseminated through scientific conferences and peer-reviewed publications, as well as meetings with stakeholders.

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19—rather than to recovery—among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04–1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24–2.01, p

Abstract Silicosis is the most prevalent and fatal occupational disease with no effective therapeutics, and currently used drugs cannot reverse the disease progress. Worse still, there are still challenges to be addressed to fully decipher the intricated pathogenesis. Thus, specifying the essential mechanisms and targets in silicosis progression then exploring anti-silicosis pharmacuticals are desperately needed. In this work, multi-omics atlas was constructed to depict the pivotal abnormalities of silicosis and develop targeted agents. By utilizing an unbiased and time-resolved analysis of the transcriptome, proteome and phosphoproteome of a silicosis mouse model, we have verified the significant differences in transcript, protein, kinase activity and signaling pathway level during silicosis progression, in which the importance of essential biological processes such as macrophage activation, chemotaxis, immune cell recruitment and chronic inflammation were emphasized. Notably, the phosphorylation of EGFR (p-EGFR) and SYK (p-SYK) were identified as potential therapeutic targets in the progression of silicosis. To inhibit and validate these targets, we tested fostamatinib (targeting SYK) and Gefitinib (targeting EGFR), and both drugs effectively ameliorated pulmonary dysfunction and inhibited the progression of inflammation and fibrosis. Overall, our drug discovery with multi-omics approach provides novel and viable therapeutic strategies for the treatment of silicosis.

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system.

To optimize the extraction technology and study antioxidant activity of polysaccharide from coffee flower (ACP), ultrasonic temperature, ultrasonic time, ratio of fluid-solid, ultrasonic power, sample immersion time and volume percentage of ethanol were investigated by using polysaccharide extraction yield as indicator. Then ultrasonic temperature, ultrasonic time and ultrasonic power were used as mainly influence factors, the extraction technology was optimized by response surface method. Antioxidant activity of ACP was investigated by DPPH· and ABTS+· scavenging effects, and FRAP assays. The results demonstrated that the ultrasonic extraction optimum technological conditions were: Ultrasonic temperature 69.5 ℃, ultrasonic time 93 min, ultrasonic power 175 W, ratio of fluid-solid 10:1 mL/g, sample immersion time 30 min, and volume percentage of ethanol 80%. Under the the yield of polysaccharides was 2.292%. The results indicated that the value of IC50 based on DPPH· scavenging effect of ACP was 3.844 mg·mL−1, the ABTS+· scavenging activity was 0.921 mmol Trolox/g ACP. The FRAP values of ACP by FRAP assay was 0.0565 mmol Fe2+/g ACP, which showed that ACP had weak antioxidant activity. This study would provide a theoretical basis for the comprehensive utilization and development of coffee by-products.

Abstract Background Science-driven storytelling and entertainment-education (E-E) media demonstrate potential for promoting improved attitudes and behavioral intent towards health-related practices. Months after the outbreak of coronavirus disease 2019 (COVID-19), emerging research highlights the essential role of interventions to improve public confidence in the COVID-19 vaccine. To improve vaccine confidence, we designed three short, animated videos employing three research-informed pedagogical strategies. These can be distributed globally through social media platforms, because of their wordless and culturally accessible design. However, the effectiveness of short, animated storytelling videos, deploying various pedagogic strategies, needs to be explored across different global regions. Methods/design The present study is a multi-site, parallel group, randomized controlled trial (RCT) comparing the effectiveness of (i) a storytelling-instructional-humor approach, (ii) a storytelling-analogy approach, (iii) a storytelling-emotion-focused approach, and (iv) no video. For our primary outcomes, we will measure vaccine hesitancy, and for secondary outcomes, we will measure behavioral intent to seek vaccination and hope. Using online platforms, we will recruit 12,000 participants (aged 18–59 years) from the USA and China, respectively, yielding a total sample size of 24,000. Discussion This trial uses innovative online technology, reliable randomization algorithms, validated survey instruments, and list experiments to establish the effectiveness of three short, animated videos employing various research-informed pedagogical strategies. Results will be used to scientifically support the broader distribution of these short, animated video as well as informing the design of future videos for rapid, global public health communication. Trial registration German Clinical Trials Register DRKS #00023650 . Date of registration: 2021/02/09.

IntroductionMusculoskeletal system gradually degenerates with aging, and a hypoxia environment at a high altitude may accelerate this process. However, the comprehensive effects of high-altitude environments on bones and muscles remain unclear. This study aims to compare the differences in bones and muscles at different altitudes, and to explore the mechanism and influencing factors of the high-altitude environment on the skeletal muscle system.MethodsThis is a prospective, multicenter, cohort study, which will recruit a total of 4000 participants over 50 years from 12 research centers with different altitudes (50m~3500m). The study will consist of a baseline assessment and a 5-year follow-up. Participants will undergo assessments of demographic information, anthropomorphic measures, self-reported questionnaires, handgrip muscle strength assessment (HGS), short physical performance battery (SPPB), blood sample analysis, and imaging assessments (QCT and/or DXA, US) within a time frame of 3 days after inclusion. A 5-year follow-up will be conducted to evaluate the changes in muscle size, density, and fat infiltration in different muscles; the muscle function impairment; the decrease in BMD; and the osteoporotic fracture incidence. Statistical analyses will be used to compare the research results between different altitudes. Multiple linear, logistic regression and classification tree analyses will be conducted to calculate the effects of various factors (e.g., altitude, age, and physical activity) on the skeletal muscle system in a high-altitude environment. Finally, a provisional cut-off point for the diagnosis of sarcopenia in adults at different altitudes will be calculated.Ethics and disseminationThe study has been approved by the institutional research ethics committee of each study center (main center number: KHLL2021-KY056). Results will be disseminated through scientific conferences and peer-reviewed publications, as well as meetings with stakeholders.Clinical Trial registration numberhttp://www.chictr.org.cn/index.aspx, identifier ChiCTR2100052153.

Airborne Light Detection And Ranging (LiDAR) can provide high-quality three-dimensional information for the safety inspection of electricity corridors. However, the robust extraction of transmission lines from airborne point cloud data is still greatly challenging. Therefore, this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data. First, the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results. Then, on the basis of the block-and-slice-constraint Euclidean clustering, a linear structure recognition method based on RANdom SAmple Consensus (RANSAC) is proposed to produce the initial individual transmission line components. Finally, a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction. Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle (UAV) platform. Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors, with the average precision of approximately 98.1%, the average recall of approximately 95.9%, and the average quality of approximately 94.2%, respectively.

Context Selenium-containing protein from selenium-enriched Spirulina platensis (Se-SP) (syn. Arthrospira platensis [Microcoleaceae]) showed novel antioxidant activity. However, the protective effect of Se-SP against oxygen glucose deprivation (OGD)-induced neural apoptosis has not been reported yet. Objective To verify whether Se-SP can inhibit OGD-induced neural apoptosis and explore the underlying mechanism. Materials and methods Primary hippocampal neurons were separated from Sprague–Dawley (SD) rats. 95% N2 + 5% CO2 were employed to establish OGD model. Neurons were treated with 5 and 10 µg/mL Se-SP under OGD condition for 6 h. Neurons without treatment were the control group. Neural viability and apoptosis were detected by MTT, immunofluorescence and western blotting methods. Results Se-SP significantly improved neuronal viability (from 57.2% to 94.5%) and inhibited apoptosis in OGD-treated primary neurons (from 45.6% to 6.3%), followed by improved neuronal morphology and caspases activation. Se-SP co-treatment also effectively suppressed OGD-induced DNA damage by inhibiting ROS accumulation in neurons (from 225.6% to 106.3%). Additionally, mitochondrial dysfunction was also markedly improved by Se-SP co-treatment via balancing Bcl-2 family expression. Moreover, inhibition of mitochondrial permeability transition pore (MPTP) by CsA (an MPTP inhibitor) dramatically attenuated OGD-induced ROS generation (from 100% to 56.2%), oxidative damage, mitochondrial membrane potential (MPP) loss (from 7.5% to 44.3%), and eventually reversed the neuronal toxicity and apoptosis (from 57.4% to 79.6%). Discussion and conclusions Se-SP showed enhanced potential to inhibit OGD-induced neurotoxicity and apoptosis by inhibiting ROS-mediated oxidative damage through regulating MPTP opening, indicating that selenium-containing protein showed broad application in the chemoprevention and chemotherapy against human ischaemic brain injury.

BackgroundCOVID-19 vaccines are in short supply worldwide. China was among the first countries to pledge supplies of the COVID-19 vaccine as a global public product, and to date, the country has provided more than 600 million vaccines to more than 200 countries and regions with low COVID-19 vaccination rates. Understanding the public’s attitude in China toward the global distribution of COVID-19 vaccines could inform global and national decisions, policies, and debates. ObjectiveThe aim of this study was to determine the attitudes of adults living in China regarding the global allocation of COVID-19 vaccines developed in China and how these attitudes vary across provinces and by sociodemographic characteristics. MethodsWe conducted a cross-sectional online survey among adults registered with the survey company KuRunData. The survey asked participants 31 questions about their attitudes regarding the global allocation of COVID-19 vaccines developed in China. We disaggregated responses by province and sociodemographic characteristics. All analyses used survey sampling weights. ResultsA total of 10,000 participants completed the questionnaire. Participants generally favored providing COVID-19 vaccines to foreign countries before fulfilling domestic needs (75.6%, 95% CI 74.6%-76.5%). Women (3778/4921, 76.8%; odds ratio 1.18, 95% CI 1.07-1.32; P=.002) and those living in rural areas (3123/4065, 76.8%; odds ratio 1.13, 95% CI 1.01-1.27; P=.03) were especially likely to hold this opinion. Most respondents preferred providing financial support through international platforms rather than directly offering support to individual countries (72.1%, 95% CI 71%-73.1%), while for vaccine products they preferred direct provision to relevant countries instead of via a delivery platform such as COVAX (77.3%, 95% CI 76.3%-78.2%). ConclusionsAmong our survey sample, we found that adults are generally supportive of the international distribution of COVID-19 vaccines, which may encourage policy makers to support and implement the distribution of COVID-19 vaccines developed in China worldwide. Conducting similar surveys in other countries could help align policy makers’ actions on COVID-19 vaccine distribution with the preferences of their constituencies.

Rapid responses in the early stage of a new epidemic are crucial in outbreak control. Public holidays for outbreak control could provide a critical time window for a rapid rollout of social distancing and other control measures at a large population scale. The objective of our study was to explore the impact of the timing and duration of outbreak-control holidays on the coronavirus disease 2019 (COVID-19) epidemic spread during the early stage in China. We developed a compartment model to simulate the dynamic transmission of COVID-19 in China starting from January 2020. We projected and compared epidemic trajectories with and without an outbreak-control holiday that started during the Chinese Lunar New Year. We considered multiple scenarios of the outbreak-control holiday with different durations and starting times, and under different assumptions about viral transmission rates. We estimated the delays in days to reach certain thresholds of infections under different scenarios. Our results show that the outbreak-control holiday in China likely stalled the spread of COVID-19 for several days. The base case outbreak-control holiday (21 d for Hubei Province and 10 d for all other provinces) delayed the time to reach 100 000 confirmed infections by 7.54 d. A longer outbreak-control holiday would have had stronger effects. A nationwide outbreak-control holiday of 21 d would have delayed the time to 100 000 confirmed infections by nearly 10 d. Furthermore, we find that outbreak-control holidays that start earlier in the course of a new epidemic are more effective in stalling epidemic spread than later holidays and that additional control measures during the holidays can boost the holiday effect. In conclusion, an outbreak-control holiday can likely effectively delay the transmission of epidemics that spread through social contacts. The temporary delay in the epidemic trajectory buys time, which scientists can use to discover transmission routes and identify effective public health interventions and which governments can use to build physical infrastructure, organize medical supplies, and deploy human resources for long-term epidemic mitigation and control efforts.

Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from 3-D information loss due to the interpolation operation. Therefore, this article proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3-D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the principal component analysis defined local coordinate reference framework, a multidirectional 3-D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.

Objectives We aimed to determine (1) the prevalence of depression during the COVID-19 pandemic among Chinese adults and (2) how depression prevalence varied by province and sociodemographic characteristics.Design Cross-sectional study.Setting National online survey in China.Participants We conducted a cross-sectional online survey among adults registered with the survey company KuRunData from 8 May 2020 to 8 June 2020. We aimed to recruit 300–360 adults per province (n=14 493), with a similar distribution by sex and rural-urban residency as the general population within each of these provinces.Primary outcome Participants completed the Patient Health Questionaire-9 (PHQ-9). We calculated the prevalence of depression (defined as a PHQ-9 score ≥10) nationally and separately for each province.Analysis Covariate-unadjusted and covariate-adjusted logistic regression models were used to examine how the prevalence of depression varied by adults’ sociodemographic characteristics. All analyses used survey sampling weights.Results The survey was initiated by 14 493 participants, with 10 000 completing all survey questions and included in the analysis. The prevalence of depression in the national sample was 6.3% (95% CI 5.7% to 6.8%). A higher odds of depression was associated with living in an urban area (OR 1.50; 95% CI 1.18 to 1.90) and working as a nurse (OR 3.06; 95% CI 1.41 to 6.66). A lower odds of depression was associated with participants who had accurate knowledge of COVID-19 transmission prevention actions (OR 0.71; 95% CI 0.51 to 0.98), the knowledge that saliva is a main transmission route (OR 0.80; 95% CI 0.64 to 0.99) and awareness of COVID-19 symptoms (OR, 0.82; 95% CI 0.68 to 1.00).Conclusion Around one in 20 adults in our online survey sample had a PHQ-9 score suggestive of depression. Interventions and policies to prevent and treat depression during the COVID-19 pandemic in China may be particularly needed for nurses and those living in urban areas.

Objective: Osteoporosis is caused by the dysregulation of bone homeostasis which is synergistically mediated by osteoclasts and osteoblasts. MiR-27a-3p is a key inhibitor of bone formation. Hence, unearthing the downstream target gene of miR-27a-3p is of great significance to understand the molecular mechanism of osteoporosis.Methods: Bioinformatics analysis was utilized to find the downstream target gene of miR-27a-3p, and dual-luciferase reporter assay was conducted to validate the interplay of miR-27a-3p and GLP1R. Besides, qRT-PCR, Western blot, and enzyme-linked immunosorbent assay (ELISA) were employed to verify the impact of miR-27a-3p on GLP1R expression and the differentiation, autophagy, and inflammatory response of MC3T3-E1 pre-osteoblasts.Results: Dual-luciferase assay validated that miR-27a-3p directly targeted GLP1R. Additionally, posttreatment of MC3T3-E1 cells with miR-27a-3p mimics resulted in a remarkable decrease in expression levels of GLP1R, cell differentiation marker gene, autophagy marker gene, and AMPK. These results indicated that miR-27a-3p targeted GLP1R to inhibit AMPK signal activation and pre-osteoblast differentiation and autophagy, while promoting the release of inflammatory factors.Conclusion: The miR-27a-3p/GLP1R regulatory axis in pre-osteoblasts contributes to understanding the molecular mechanism of osteoporosis.

# Background China's long-term care insurance (LTCI) policy has been minimally evaluated. This systematic review aimed to assess the impact of China's LTCI pilot on beneficiaries and their caregivers. # Methods This review is based on a search of peer-reviewed studies in English (Embase, MEDLINE, Web of Science) and Chinese (China National Knowledge Infrastructure \[CNKI], VIP, Wanfang) databases from January 2016 through July 2020, with all studies published in English or Chinese included. We included quantitative analyses of beneficiary-level data that assessed the impact of LTCI on beneficiaries and their caregivers, with no restriction placed on the outcomes studied. # Results Nine studies met our inclusion criteria. One study was a randomised trial and two used quasi-experimental approaches. Four studies examined LTCI's effect on beneficiaries' quality of life, physical pain, and health service utilisation; one study reported the effect on beneficiaries' healthcare expenditures; and one study evaluated the impact on caregivers' care tasks. These studies generally found LTCI to be associated with an improvement in patients' quality of life (including decreased physical pain), a reduction in the number of outpatient visits and hospitalisations, decreased patient-level health expenditures (e.g. one study reported a reduction in the length of stay, inpatient expenditures, and health insurance expenditures in tertiary hospitals by 41.0%, 17.7%, and 11.4%, respectively), and reduced informal care tasks for caregivers. In addition, four out of four studies that evaluated this outcome found that beneficiaries' overall satisfaction with LTCI was high. # Conclusion The current evidence base for the effects of LTCI in China on beneficiaries and their caregivers is sparse. Nonetheless, the existing studies suggest that LTCI has positive effects on beneficiaries and their caregivers. Further rigorous research on the impacts of LTCI in China is needed to inform the future expansion of the program.

Currently, the street tree resource survey using Mobile laser scanning (MLS) represents a hot spot around the world. Refined trunk extraction is an essential step for 3D reconstruction of street trees. However, due to scanning errors and the effects of occlusion by various types of features in the urban environment, street tree point cloud data processing has the problem of excessive noise. For the noise points that are difficult to remove using statistical methods in close proximity to the tree trunk, we propose an adaptive trunk extraction and denoising method for street trees based on an improved iForest (Isolation Forest) algorithm. Firstly, to extract the individual tree trunk points, the trunk and the crown are distinguished from the individual tree point cloud through point cloud slicing. Next, the iForest algorithm is improved by conducting automatic calculation of the contamination and further used to denoise the tree trunk point cloud. Finally, the method is validated with five datasets of different scenes. The results indicate that our method is robust and effective in extracting and denoising tree trunks. Compared with the traditional Statistical Outlier Removal (SOR) filter and Radius filter denoising methods, the denoising accuracy of the proposed method can be improved by approximately 30% for noise points close to tree trunks. Compared to iForest, the proposed method automatically calculates the contamination, improving the automation of the algorithm. Our method can provide more precise trunk point clouds for 3D reconstruction of street trees.

Significance: Heart failure, a disease with extremely high incidence, is closely associated with inflammation and oxidative stress. The Toll-like receptor 4 (TLR4)/nuclear factor kappa-B (NF-κB) pathway plays an important role in the occurrence and development of heart failure. Recent advances: Previous studies have shown that TLR4/NF-κB causes heart failure by inducing oxidative stress and inflammation; damaging the endothelia; promoting fibrosis; and inducing myocardial hypertrophy, apoptosis, pyroptosis, and autophagy. Critical issues: Understanding the pathogenesis of heart failure is essential for the treatment of this disease. In this review, we outline the mechanisms underlying TLR4/NF-κB pathway-mediated heart failure and discuss drugs that alleviate heart failure by regulating the TLR4/NF-κB pathway. Future directions: During TLR4/NF-κB overactivation, interventions targeting specific receptor antagonists may effectively alleviate heart failure, thus providing a basis for the development of new anti-heart failure drugs.

The optical properties of rocks and minerals provide a reliable way to measure their chemical and mineralogical composition due to the specific reflection behaviors, which is also the key insight behind most automatic identification and classification approaches. However, the inter-category spectral similarity poses a great challenge to the automatic identification and classification tasks because of the diversity of rocks and minerals. Therefore, this paper develops a recognition and classification approach of rocks and minerals using the highly discriminative representation derived from their raw spectral signatures. More specifically, a transformer-based classification approach integrated with category-aware contrastive learning is constructed and trained in an end-to-end manner, which would force instances of the same category to remain close-by while pushing instances of a dissimilar category far apart in the high-dimensional feature space, in order to produce the highly discriminative feature representation of the rocks and minerals. From both qualitative and quantitative views, experiments are conducted on the laboratory sample dataset with 30 types of rocks and minerals shared from the National Mineral Rock and Fossil Specimens Resource Center, and the spectral information of the laboratory rocks and minerals is captured using a multi-spectral sensor, with a duplicated payload of the counterpart onboard the Zhurong rover. Quantitative results demonstrate that the developed approach can effectively distinguish 30 types of rocks and minerals, with a high overall accuracy of 96.92%. Furthermore, the developed approach is remarkably superior to other existing methods, with average differences of 4.75% in the overall accuracy. Furthermore, we also visualized the derived highly discriminative features of different types of rocks and minerals by projecting them onto a two-dimensional map, where the same categories tend to be modeled by nearby locations and the dissimilar categories by distant locations with high probability. It can be observed that, compared with those in the raw spectral feature space, the clusters are formed better in the derived highly discriminative feature space, which further confirms the promising representation capability.

The velocity of an ice shelf is an important characteristic to understand its dynamics and interaction with the internal ice sheet. Therefore, we develop an improved multi-scale image matching method for producing a complete and accurate Amery ice shelf velocity field from Landsat 8 images. First, we investigate the relationship between the template size and the image entropy and propose an image entropy-based preliminary operation for distinguishing the high-contrast regions from the low-contrast regions prior to iteratively determining the optimum template size. Second, a Gaussian pyramid image-based hierarchical data structure is designed to support a coarse-to-fine image matching strategy.The image entropy-based matching method is applied on the top layer of the image pyramid to guarantee the matching results have optimal completeness and high accuracy. Finally, a postprocess procedure is performed to derive a complete and accurate Amery ice shelf velocity field. Experimental results demonstrate that the proposed method can significantly improve computational efficiency. Moreover, the proposed method provides more accurate and robust matching results than other existing methods, particularly over the low-contrast surfaces. Additionally, the derived velocity field also shows good consistency with the one acquired from MEaSUREs Annual Antarctic Ice Velocity Maps 2016–2017.

Tau hyper-phosphorylation and deposition into neurofibrillary tangles have been found in brains of patients with Alzheimer’s disease (AD) and other tauopathies. Molecular chaperones are involved in regulating the pathological aggregation of phosphorylated Tau (pTau) and modulating disease progression. Here, we report that nicotinamide mononucleotide adenylyltransferase (NMNAT), a well-known NAD+ synthase, serves as a chaperone of pTau to prevent its amyloid aggregation in vitro as well as mitigate its pathology in a fly tauopathy model. By combining NMR spectroscopy, crystallography, single-molecule and computational approaches, we revealed that NMNAT adopts its enzymatic pocket to specifically bind the phosphorylated sites of pTau, which can be competitively disrupted by the enzymatic substrates of NMNAT. Moreover, we found that NMNAT serves as a co-chaperone of Hsp90 for the specific recognition of pTau over Tau. Our work uncovers a dedicated chaperone of pTau and suggests NMNAT as a key node between NAD+ metabolism and Tau homeostasis in aging and neurodegeneration.

Abstract Background Cerebral edema, erupting simultaneously with severe ischemic stroke, might lead to increased intracranial pressure, cerebral herniation, and ultimately death. Studies conducted previously by our team have demonstrated the fact that bloodletting puncture at hand twelve Jing-well points (HTWP) could alleviate cerebral edema, which mainly results from the disruption of blood-brain barrier (BBB). The study, therefore, was first designed to demonstrate whether BBB-protection serves an important role in the edema-relief effect of HTWP bloodletting, based on which to research the molecular mechanism underlying. Methods The rats were made into model suffering from permanent middle cerebral artery occlusion (pMCAO) and then bloodletting puncture were treated at HTWP once a day. Wet and dry weight method was adopted to evaluate the degree of brain edema, evans blue extravasation and electron microscopy were used to evaluate the integrity of the BBB, and RT-qPCR was carried out to analyze the expression level of occludin, claudin-5, ICAM-1, and VEGF. Results Results revealed that bloodletting puncture treatment could reduce water content of brain and the permeability of BBB caused by ischemic stroke. In bloodletting puncture group, ameliorated tight junctions could be observed under electron microscopy. It was demonstrated in further study that, in bloodletting group, compared with pMCAO one, the expression levels of occludin and claudin-5 were up-regulated, while ICAM-1 and VEGF were down-regulated. Conclusions In conclusion, bloodletting puncture at HTWP might play a significant role in protecting the tight junctions of BBB, thus alleviating cerebral edema induced by ischemic stroke. Therefore, the therapy of bloodletting puncture at HTWP may be a promising strategy for acute ischemic stroke in the future.

Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.