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Abstract The preservation of Chinese traditional villages plays a crucial role in promoting the sustainable development of rural natural, cultural, and ecological environments. It is also a key strategy for achieving rural revitalization. Current research on traditional villages predominantly focuses on the realm of cultural landscapes, with an emphasis on preserving the cultural ecological value of these communities. In comparison, discussions on the quality of the ecological environment of villages from the perspectives of natural environment, economic environment, and the social organizational environment within regional development are relatively scarce. Our study employed GIS and RS technology and refers to the Technical Criterion for Ecosystem Status Evaluation. Several sub-indices of the ecological environment status, including the biological richness index, vegetation coverage index, water network density index, and land stress index, were selected to construct an ecological environment assessment model. This model was used to analyze the spatial–temporal changes in the ecological environment status of each county, county-level city, and traditional village within the jurisdiction of Enshi Tujia and Miao Autonomous Prefecture and its surrounding areas from 2010 to 2020. The study quantitatively evaluated the ecological environment status of each county, county-level city, and village in Enshi before and after the implementation of traditional village preservation policies. Through comparative analysis, the study revealed the impact of these policies on the natural ecological environment of the study area. The results indicated the following: (1) From 2010 to 2020, the ecological index (EI) values in the villages of Enshi Prefecture exhibited a similar trend to the EI values in the respective counties and county-level cities they are located in, although significant differences in magnitude of change were observed. (2) The EI values in the counties, county-level cities, and villages demonstrated greater variation in the latter five years of the decade (2015–2020) compared to the previous five years (2010–2015). (3) In 2020, the EI value of the villages experienced more significant changes compared to 2010, whereas the overall EI value of the counties and county-level cities showed less pronounced changes. The findings of this study suggest that the traditional village preservation policies implemented in Enshi Prefecture have both positive and negative impacts on the ecological environment of the surrounding areas of protected villages, and these impacts become increasingly evident over time. By comparing and analyzing the ecological changes in the surrounding areas of traditional villages in Enshi Prefecture with the overall ecological changes in the respective counties and county-level cities, our study employs quantitative analytical methods to delve into the impact of traditional village conservation policies on the natural ecological environment. It assesses the effects of policy implementation on the natural ecological environment of traditional villages, analyzing both the positive and negative impacts brought about by the execution of these policies, with the aim of effectively guiding the natural ecological conditions of traditional villages towards a more healthy trajectory of development.

Abstract The research and exploration of time series prediction (TSP) have attracted much attention recently. Researchers can achieve effective TSP based on the deep learning model and a large amount of data. However, when sufficient high-quality data are not available, the performance of prediction models based on deep learning techniques may degrade. Therefore, this paper focuses on few-shot time series prediction (FTSP) and plans to combine meta-learning and generative models to alleviate the problems caused by insufficient training data. When using meta-learning techniques to process FTSP tasks, researchers set the meta-parameter in model-agnostic meta-learning (MAML) as a meta-sample and construct meta-sample generation methods based on advanced generative modeling theory to achieve better uncertainty coding. The existing meta-sample generation methods in FTSP scenes have an inherent limitation: With the increase of the complexity of prediction tasks, samples based on Gaussian distribution may be sensitive to noise and outliers in the meta-learning environment and lack of uncertainty expression, thus affecting the robustness and accuracy of prediction. Therefore, this paper proposes an adaptive sample generation method called JLSG-Diffusion. Based on the Jensen constraint framework and Laplace modeling theory, this method constructs a sample adapter with reasonable adaptive steps and fast convergence for specific tasks. The advantage is to realize fast adaptive convergence of samples to new tasks at lower cost, effectively control the overall generalization error, and improve the robustness and non-Gaussian generalization of sample posterior reasoning. Moreover, the meta sampler of JLSG-Diffusion embeds meta-learning from the implicit probability measure level of Denoising Diffusion Probabilistic Models (DDPM), which makes the meta-sample distribution directly establish a function mapping with the new task and effectively quantifies the uncertainty of spatiotemporal dimension. Experimental results on three real datasets prove the efficiency and effectiveness of JLSG-Diffusion. Compared with the benchmark methods, the prediction model combined with JLSG-Diffusion shows better accuracy.

IntroductionAvian orthoreovirus (ARV) is a significant pathogen causing viral arthritis, leading to substantial economic losses in the poultry industry worldwide.MethodsA novel ARV strain, designated FJ202311, was isolated from a broiler farm in Fujian Province, China. Whole-genome sequencing was conducted using next-generation sequencing with MGI technology, and phylogenetic analysis of the sigma C amino acid sequence was performed. Comparative sequence analysis identified unique genetic features of FJ202311. Pathogenicity studies were carried out by inoculating broilers with the isolated strain and monitoring clinical signs, weight gain, and histopathological changes.ResultsThe complete genome of FJ202311 was determined to be 23,495 base pairs in length, encoding 12 major proteins. Phylogenetic analysis revealed that FJ202311 forms a distinct genotypic cluster, exhibiting only 47.1% to 59.3% sequence identity to 16 reference ARV strains. Notably, 50 unique amino acid substitutions were identified in the sigma C protein. Pathogenicity tests demonstrated that FJ202311 caused severe arthritis and tenosynovitis in broilers. Infected birds exhibited significant weight loss compared to controls, with reductions of 11.78% and 8.93% at 14 and 21 days post-infection, respectively.DiscussionThis study highlights the unique molecular and pathogenic characteristics of the novel ARV strain FJ202311, contributing to our understanding of ARV diversity and epidemiology in China. These findings underscore the importance of continuous monitoring and provide insights for developing improved prevention and control strategies against ARV.

This study aims to elucidate the intricate effects of Acetyl tributyl citrate (ATBC) on bone metabolism, disentangling the underlying molecular mechanisms that govern the impact of environmental contaminants on disease processes. Leveraging the exhaustive exploration of databases such as ChEMBL, STITCH, GeneCards, and OMIM, we have identified a comprehensive list of 164 potential targets intimately associated with both ATBC and bone metabolism. Following rigorous refinement using the STRING platform and Cytoscape software, we pinpointed ten core targets, encompassing KDM1A, EP300, HDAC2, EHMT2, DNMT1, and several others. In-depth Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, conducted within the Metascape database, revealed that the core targets of ATBC’s influence on bone metabolism are predominantly concentrated within vital signaling cascades, including thyroid hormone signaling, FOXO signaling, glucagon signaling, AMPK signaling, insulin signaling, adipocytokine signaling, and Notch signaling pathways. Additionally, molecular docking simulations performed with AutoDock software confirmed the robust binding interactions between ATBC and these core targets, reinforcing our understanding of their interactions. To explore the cellular impact of ATBC, we performed in vitro experiments using osteoblasts (MC3T3-E1) exposed to relevant concentrations. Our findings revealed that low-dose ATBC (100 μM) significantly impaired cell proliferation and migration. Concurrently, we observed a downregulation in the transcriptional expression of key epigenetic regulators (KDM1A, EP300, HDAC2), suggesting that ATBC can disrupt bone metabolism at the cellular level. Collectively, our findings provide a theoretical scaffold for comprehending the intricate molecular mechanisms mediating ATBC’s effects on bone metabolism, and paves the way for the development of preventive and therapeutic strategies against orthopedic disorders that may arise from exposure to plastic products containing ATBC or excessive ATBC environments.

Background: Cannabidiol (CBD), extracted from Cannabis sativa, has anticancer, anti-inflammation, and analgesic effects. Nevertheless, its therapeutic effect and the mechanism by which it alleviates oral mucositis (OM) remain unclear. Aims: To explore the impact of CBD on OM in mice and on human oral keratinocyte (HOK) cells. Study Design: Expiremental study. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, GeneCard, DisGeNET, and Gene Expression Omnibus databases were used to conduct therapeutic target gene screening for drugs against OM. Cytoscape software was used to build networks linking components, targets, and diseases. The STRING database facilitated analysis of intertarget action relationships, and the target genes were analyzed for Kyoto Encyclopedia of Genes and Genomes pathway enrichment. Occurrence of serum inflammation-related factors, hematoxylin and eosin staining, and immunohistochemistry were used to assess OM injury. Cell proliferation, migration, pyroptosis, and apoptosis of HOK cells under different treatments were assessed. Molecular mechanisms were elucidated through western blot and quantitative real-time polymerase chain reaction analyses. Results: A total of 49 overlapping genes were pinpointed as potential targets, with NF-κB1, PIK3R1, NF-κBIA, and AKT1 being recognized as hub genes among them. Additionally, the PI3K/Akt/NF-κB and interleukin-17 signaling pathways were identified as relevant. Our in vivo experiments showed that CBD significantly reduced the proportion of lesion area, mitigated oral mucosal tissue lesions, and downregulated the expression levels of genes and levels of proteins, including NLRP3, P65, AKT, and PI3K. In vitro experiments indicated that CBD enhanced HOK cell proliferation and migration and reduced apoptosis through inhibition of the PI3K/Akt/NF-κB signaling pathway and pyroptosis. Conclusion: Our findings suggest a novel mechanism for controlling OM, in which CBD suppresses the PI3K/Akt/NF-κB signaling pathway and pyroptosis, thereby mitigating OM symptoms.

Plastic film mulching has been widely used to increase maize yield in the semiarid area of China. However, whether long-term plastic film mulching is conducive to agricultural sustainability in this region remains controversial. A field experiment was initiated in 2013 with five different film mulching methods: (i) control method, flat planting without mulching (CK), (ii) flat planting with half film mulching (P), (iii) film mulching on ridges and planting in narrow furrows (S), (iv) full film mulching on double ridges (D), and (v) film mulching on ridges and planting in wide furrows (R). The effects on soil organic carbon (SOC) content, storage, and fractions, and on the carbon management index (CMI) were evaluated after nine consecutive years of plastic film mulching. The results showed that long-term plastic film mulching generally maintained the initial SOC level. Compared with no mulching, plastic film mulching increased the average crop yield, biomass yield, and root biomass by 48.38, 35.06, and 37.32%, respectively, which led to the improvement of SOC sequestration. Specifically, plastic film mulching significantly improved CMI, and increased the SOC content by 13.59%, SOC storage by 7.47% and easily oxidizable organic carbon (EOC) by 13.78% on average, but it reduced the other labile fractions. SOC sequestration and CMI were improved by refining the plastic film mulching methods. The S treatment had the best effect among the four mulching methods, so it can be used as a reasonable film mulching method for sustainable agricultural development in the semiarid area.

There is intense competition among pharmaceutical companies with the rapid growth of the global pharmaceutical industry. In recent years, China has continuously increased the reform of the medical system. Technology mergers and acquisitions (M&A) in China’s pharmaceutical industry have emerged in this complex policy and economic background. This paper conducts an empirical study from the dual perspectives of financial performance and innovation performance, based on unbalanced panel data of Chinese listed pharmaceutical firms from 2012 to 2022. The impact of technology M&A on firm performance is analyzed in terms of the heterogeneity of firm characteristics. Meanwhile, the relationship between R&D investment in technology M&A and firm performance is examined. The results show that technology M&A can promote the performance of pharmaceutical companies, and R&D investment has a mediating effect on the impact of technology M&A on corporate performance. Based on the above findings, this study enriches the relevant literature on technology M&A in the pharmaceutical industry, provides warnings and suggestions for pharmaceutical companies to improve corporate performance through technology M&A, and provides reference materials for future policy formulation.

Abstract Since probability hypothesis density (PHD) filters do not need explicit data association, they have recently been widely used in radar multi-target tracking (MTT). However, in existing PHD filters, sampling times are generally considered the same for all targets. Due to the limitation of antenna beam width in radar applications, the same sampling time for all targets will lead to a mismatch between the predicted data and measurement data, reducing the accuracy of radar MTT. In order to eliminate the estimation error with less computational cost, a radar nonlinear multi-target tracking method with a parallel PHD filter is proposed in this article. The measurement area is divided into several subspaces according to the beam width of the radar antenna, and the PHD of all subspaces is calculated in parallel. Then, multi-feature information in radar echo assists tracking and improves real-time performance. Experimental results in various scenarios illustrate that the proposed method can eliminate the estimation errors introduced by sampling time diversity at the cost of less computation cost, especially in cluttered environments.

In high-intensity farming, swiftly and accurately adjusting the proportion of artificially reared pollinators according to the flowering phenology of crops is vital. This adjustment is essential for sustaining crop yields without disrupting the ecological niche of native pollinators. Although advancements in remote sensing provide practical means to gather data on crop coverage and extent, identifying insect-pollinated crops during their initial flowering to guide the deployment of managed pollinators is an underexamined research area. Here, we tested the capability of utilizing Sentinel-2 satellite images combined with a deep learning model to map crop fields during the initial flowering stage of insect-pollinated crops in Zhaosu County, Xinjiang, China. Specifically, we examined rapeseed fields by employing 12 neural network designs to identify images of the initial flowering stage. Different network combinations of three convolutional neural network (CNN) models (U-Net, PSPNet, and DeepLab V3) and four different backbone networks (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) were explored to determine the most effective model for detecting rapeseed fields at the initial flowering stage. A comparison was conducted with Sentinel-2 images obtained at the peak stage of rapeseed flowering. Our results suggest that the use of a deep learning model in combination with Sentinel-2 image data can successfully identify rapeseed fields at the initial flowering stage, thereby offering preliminary insights for the strategic introduction of managed pollinators. The PSPNet model emerged as the superior choice for the identification of rapeseed fields, exhibiting high accuracy in both detection and boundary recognition, with F1 scores of 88.17 % and an intersection over union (IoU) of 53.11 % during initial flowering and F1 scores of 93.33 % with an IoU of 53.49 % during peak flowering. The planting area of rapeseed fields detected by the model in Zhaosu County was 122.16 km2, distributed within an area of 1068 km2. These findings can provide foundational data for informed decisions regarding artificial pollinator supplementation, helping to sustain the health and stability of agricultural ecosystems.

Abstract Background Depression and anxiety have been found prevalent during all phases of the COVID-19 pandemic. In late December 2022, almost all COVID-19 control measures were lifted in China, leading to a surge in COVID-19 infections. The public’s perceived risk and fear of COVID-19 would be increased. This study aims to examine the prevalence of depression and anxiety in the Chinese general population and explores the mediating role of fear of COVID-19 between COVID-19 perceived risk and depression/anxiety and the moderating role of resilience between fear of COVID-19 and depression/anxiety. Methods A cross-sectional online survey was conducted in Wenzhou, China, immediately following almost all COVID-19 control measures lifted. The 9-item Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), the COVID-19 Risk Perception Scale, the Fear of COVID-19 Scale, and the Connor-Davidson Resilience Scale (CD-RISC) were used to evaluate depression, anxiety, COVID-19 perceived risk, fear of COVID-19, and resilience, respectively. Structural Equation Modeling (SEM) with Maximum Likelihood (ML) estimator and adjusted for significant background factors was performed to test the moderated mediation. Data obtained from 935 participants were analyzed. Results The prevalence of moderate to severe depression and anxiety was 23.7% and 9.5%, respectively. The present study revealed positive associations among COVID-19 perceived risk, fear of COVID-19 and depression/anxiety, and negative associations between resilience and fear of COVID-19/depression/anxiety. Fear of COVID-19 partially mediated the association between COVID-19 perceived risk and depression/anxiety. Furthermore, resilience significantly moderated the association between fear of COVID-19 and depression/anxiety. Two moderated mediation models were constructed. Conclusion Depression and anxiety were prevalent among Chinese adults during the final phase of the pandemic in China. The significant mediation role of fear of COVID-19 implies that reducing fear of COVID-19 may effectively alleviate depression and anxiety symptoms. Moreover, enhancing public resilience during an epidemic crisis is crucial for promoting mental health.

Background: Neolamarckia cadamba (Rubiaceae) is a well-recognized medicinal plant with recorded therapeutical attributes. However, a thorough assessment of active compounds in its fruits is lacking, limiting their use and valorization in pharmacological industries. Methods: Thus, this study investigated variations in the fruits’ secondary metabolite (SM) profiles, as well as antioxidant activities in aqueous (WA) and ethanol (ET) extracts. Results: Liquid chromatography–electrospray ionization tandem mass spectrometry identified 541 SMs, of which 14 and 1 (di-O-glucosylquinic acid) were specifically detected in ET and WA, respectively. Phenolic acids (36.97%), flavonoids (28.10%), terpenoids (12.20%), and alkaloids (9.98%) were the dominant SMs. The SM profiles of the fruits in WA and ET were quite different. We revealed 198 differentially extracted (DE) metabolites between WA and ET, including 62 flavonoids, 57 phenolic acids, 45 terpenoids, 14 alkaloids, etc. Most DE flavones (36 out of 40), terpenoids (45 out of 45), and alkaloids (12 out of 14) had higher content in ET. Catechin and its derivatives, procyanidins, and tannins had higher content in WA. ABTS and DPPH assays showed that the antioxidant activity of ET was significantly higher than that of WA. Conclusions: Our findings will facilitate the efficient extraction and evaluation of specific active compounds in N. cadamba.

High-strength engineered geopolymer composite (EGC) materials exhibit excellent mechanical properties under quasistatic loading, thus showing great potential in military and civilian facilities subjected to impact or explosive loading. However, its dynamic mechanical response under high-speed loading is not fully understood. In this study, dynamic compressive test was performed on EGC with PE fiber contents of 0%, 0.5%, 1.0%, 1.5%, and 2.0% using the Split Hopkinson Pressure Bar (SHPB) test. The results indicated that EGC reinforced with 1.5% fiber exhibited optimal static and dynamic mechanical performance. In the strain rate range of 181 s−1 to 201 s−1, when the fiber content increased from 1.0% to 1.5% and 2.0%, the dynamic compressive strength of the EGC increased by 24.3%, 28.8%, and 44.0%, respectively, compared to the matrix without fiber. Dynamic parameters of the EGC, including dynamic compressive strength, dynamic increase factor, and impact toughness, showed sensitivity to strain rates and increased with strain rate. A modified model, incorporating the fiber bridging effect, was proposed based on the CEB-FIP model, providing important guidance for practical engineering applications.

Objective: This review provides a comprehensive intensive care unit (ICU) perspective on amniotic fluid embolism (AFE), encompassing its epidemiology, pathophysiology, diagnosis, and management. Mechanism: AFE is an abrupt and perilous condition. The enhancement of diagnostic criteria, starting from the understanding of pathologic physiology, can facilitate the development of more specialized disease management approaches and targeted interventions. Findings in Brief: Significant research advancements have improved the timeliness and accuracy of clinical diagnosis and treatment for AFE, leading to the development of several effective rescue procedures. Progress is expected to be made in various aspects including a comprehensive exploration of pathophysiological mechanisms, identification and utilization of biomarkers, innovation in treatment methods, enhancement of personnel training and collaboration within treatment teams, as well as the application of big data technology. Conclusions: The recent research has greatly contributed to substantial progress in the clinical diagnosis and treatment for AFE. However, further research holds potential to provide even greater benefits for patients.

Objectives This study aimed to clarify the relationship between white blood cell (WBC) and adverse pregnancy outcomes.Design A total of 25 270 pregnant women underwent peripheral blood white blood cell count tests in the first, second and third trimesters. Adverse pregnancy outcomes were gestational hypertension, pre-eclampsia, gestational diabetes mellitus, preterm birth, low birth weight, caesarean delivery, macrosomia and fetal distress. Due to acute infectious disease or other diseases, 1127 were excluded.Setting Minhang Hospital, China.Participants A total of 24 143 pregnant women were included in this study.Primary and secondary outcome measures The primary outcome was the adverse pregnancy outcomes.Results For the 24 143 participants, we calculated adjusted ORs for adverse pregnancy outcomes associated with an increased WBC count. For gestational hypertension, the ORs were 1.18 (95% CI, 1.05 to 1.24) in the first trimester and 1.10 (1.06 to 1.13) in the second trimester; for pre-eclampsia, ORs were 1.14 (95% CI, 1.47 to 1.64) in the first trimester and 1.10 (1.05 to 1.16) in the second trimester; for gestational diabetes mellitus, ORs were 1.06 (95% CI, 1.00 to 1.13) in the first trimester and 1.10 (1.04 to 1.16) in the second trimester; for preterm birth, ORs were 1.12 (95% CI, 1.06 to 1.18) in the first trimester, 1.10 (1.06 to 1.13) in the second trimester and 1.12 (1.09 to 1.15) in the third trimester; for low birth weight, ORs were 1.09 (95% CI, 1.02 to 1.17) in the first trimester, 1.03 (0.99 to 1.08) in the second trimester and 1.12 (1.08 to 1.16) in the third trimester. Significant associations were not observed obviously for caesarean delivery, macrosomia and fetal distress.Conclusions Our results indicate strong, continuous associations of maternal WBC count with increased risks of adverse pregnancy outcomes.

Abstract A consensus species tree is reconstructed from 11 gene trees for human, bat, and pangolin beta coronaviruses from samples taken early in the pandemic (prior to April 1, 2020). Using coalescent theory, the shallow (short branches relative to the hosts) consensus species tree provides evidence of recent gene flow events between bat and pangolin beta coronaviruses predating the zoonotic transfer to humans. The consensus species tree was also used to reconstruct the ancestral sequence of human SARS-CoV-2, which was 2 nucleotides different from the Wuhan sequence. The time to most recent common ancestor was estimated to be Dec 8, 2019 with a bat origin. Some human, bat, and pangolin coronavirus lineages found in China are phylogenetically distinct, a rare example of a class II phylogeography pattern (Avise et al. in Ann Rev Eco Syst 18:489–422, 1987). The consensus species tree is a product of evolutionary factors, providing evidence of repeated zoonotic transfers between bat and pangolin as a reservoir for future zoonotic transfers to humans.

Honey is well-known as a food product that is rich in active ingredients and is very popular among consumers. Free amino acids (FAAs) are one of the important nutritional components of honey, which can be used not only as a nutritional indicator of honey but also as an indicator of plant source identification. In this study, the contents of 20 FAAs in seven types of honey from 11 provinces in China were examined for the first time. The 20 FAAs were analyzed by ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS). By analyzing 93 honey samples from seven types of honey, the FAAs were found to range from 394.4 mg/kg (linden honey) to 1771.7 mg/kg (chaste honey). Proline ranged from 274.55 to 572.48 mg/kg, and methionine was only present in some of the linden honey, chaste honey, acacia honey, and rape honey. Evaluated by amino acid principal component analysis, multifloral grassland honey had the highest overall evaluation score, acacia and jujube honey were the most similar, while chaste honey was the least similar to the other types of honey. In addition, DNA was extracted from 174 Xinjiang grassland honey samples and different plant leaves for PCR and sequencing to identify the species of nectar plants. As a result, 12 families and 25 species of honey plants were identified. The results confirmed the diversity of FAAs in dissimilar types and sources of honey. This study provides a reference for expanding honey quality standards and verifying the authenticity of honey.

The structural behavior of precast concrete shear walls (PSWs) is highly dependent on their vertical connections. Current PSWs mainly use two-row (paired) connections, which is relatively time-consuming and involves high cost and construction difficulties on wall assembly. Reducing the number of vertical connections from two-row to one-row or even partial could be economical and efficient solutions for the use of PSWs in practice. Reversed cyclic tests on seven full scale shear wall specimens were conducted under the axial compression ratio of 0.24. Grouted steel sleeves (GS) and grouted corrugated metallic duct (CMD) connections were used in the test program, considering three different layouts: two-row, one-row and partial. Test results showed that all shear walls failed by flexural-shear. The PSWs exhibited more and plumper hysteretic curves than the monolithic wall. The load carrying capacity of PSWs was close to the monolithic wall with an average difference of less than 7 %. The displacement ductility coefficients of the PSWs with two-row, one-row and partial GS were 2.5, 2.8 and 2.6 respectively, which were slightly higher than that of the monolithic specimen (2.2). The ductility of the PSWs with CMD were2.3, 2.9 and 2.3. It was also indicated that PSWs with GS or CMD connections had greater energy dissipation capacity than the monolithic shear wall. The decline in stiffness and energy dissipation capacities of all precast specimens were similar.

Abstract Background Gastric cancer ranks high in terms of morbidity and mortality worldwide. Multimodal therapy is therefore essential for locally advanced gastric cancer. Recent studies have demonstrated that both perioperative chemotherapy and neoadjuvant chemoradiotherapy can improve the prognosis of patients. However, the completion rate of chemotherapy after surgery remains low, which may affect survival. Thus, identifying the best way to combine radiotherapy, chemotherapy and surgery is important. The aim of this study was to explore the toxicity and efficacy of the total neoadjuvant therapy modality for locally advanced gastric cancer. Methods This study will be a prospective, multicenter, single-arm, phase II clinical trial. Patients diagnosed with locally advanced (stage cT3-4 and cN positive, AJCC 8th) gastric cancer and gastroesophageal junction adenocarcinoma will be enrolled. Patients will initially receive radiotherapy (95% planned target volume: 45 Gy/25 f) and concurrent chemotherapy (S-1: 40–60 mg twice a day) followed by six cycles of consolidated chemotherapy (SOX, consisting of S-1 and oxaliplatin) and surgery. The primary objective will assess pathological complete response; the secondary objectives will include toxicities assessing surgical complications, the tumor downstaging rate and the R0 resection rate. Discussion Investigation of total neoadjuvant therapy in gastric cancer is limited. The goal of this trial is to explore the efficacy and toxicity of total neoadjuvant therapy for locally advanced gastric cancer and gastroesophageal junction adenocarcinoma. Trial registration: Clinicaltrials.gov (NCT04062058, August 20, 2019).

Abstract As the COVID‐19 pandemic evolves and new variants emerge, the development of more efficient identification approaches of variants is urgent to prevent continuous outbreaks of SARS‐CoV‐2. Field‐effect transistors (FETs) with two‐dimensional (2D) materials are viable platforms for the detection of virus nucleic acids (NAs) but cannot yet provide accurate information on NA variations. Herein, 2D Indium selenide (InSe) FETs were used to identify SARS‐CoV‐2 variants. The device's mobility and stability were ensured by atomic layer deposition (ALD) of Al2O3. The resulting FETs exhibited sub‐fM detection limits ranging from 10–14 M to 10–8 M. The recognition of single‐nucleotide variations was achieved within 15 min to enable the fast and direct identification of two core mutations (L452R, R203M) in Delta genomes (p

Non-fasting lipidemia (nFL), mainly contributed by postprandial lipidemia (PL), has recently been recognized as an important cardiovascular disease (CVD) risk as fasting lipidemia (FL). PL serves as a common feature of dyslipidemia in Type 2 Diabetes (T2D), albeit effective therapies targeting on PL were limited. In this study, we aimed to evaluate whether the therapy combining probiotics (Prob) and berberine (BBR), a proven antidiabetic and hypolipidemic regimen via altering gut microbiome, could effectively reduce PL in T2D and to explore the underlying mechanism. Blood PL (120 min after taking 100 g standard carbohydrate meal) was examined in 365 participants with T2D from the Probiotics and BBR on the Efficacy and Change of Gut Microbiota in Patients with Newly Diagnosed Type 2 Diabetes (PREMOTE study), a random, placebo-controlled, and multicenter clinical trial. Prob+BBR was superior to BBR or Prob alone in improving postprandial total cholesterol (pTC) and low-density lipoprotein cholesterol (pLDLc) levels with decrement of multiple species of postprandial lipidomic metabolites after 3 months follow-up. This effect was linked to the changes of fecal Bifidobacterium breve level responding to BBR alone or Prob+BBR treatment. Four fadD genes encoding long-chain acyl-CoA synthetase were identified in the genome of this B. breve strain, and transcriptionally activated by BBR. In vitro BBR treatment further decreased the concentration of FFA in the culture medium of B. breve compared to vehicle. Thus, the activation of fadD by BBR could enhance FFA import and mobilization in B. breve and diliminish the intraluminal lipids for absorption to mediate the effect of Prob+BBR on PL. Our study confirmed that BBR and Prob (B. breve) could exert a synergistic hypolipidemic effect on PL, acting as a gut lipid sink to achieve better lipidemia and CVD risk control in T2D.

The reliability and safety of launch vehicle launch missions might be effectively increased thanks to the fault prediction and health management (PHM) technology of engines, which could also improve with problem diagnostics and decrease the cost of operation and maintenance overhaul. This paper combines the equipment characteristics and the current state of safeguarding for large, complex space systems, introduces the intelligent launch vehicle engine PHM technology methods that are being gradually implemented in space systems, and discusses and compares fault detection and health assessment techniques. Subsequently, analysis of the measurement signals from a rocket engine was performed using an example, and it was shown that the established comprehensive health assessment structure, which is based on the fault prediction algorithm method and the fuzzy comprehensive assessment method, could successfully realize the effectiveness of the rocket engine system health assessment, which had an outstanding application value.

Abstract Aims/Introduction Although the efficacy of teneligliptin, a highly selective dipeptidyl peptidase‐4 inhibitor, has been amply studied for the treatment of type 2 diabetes, no clinical trials of teneligliptin have been carried out in China. We evaluated the efficacy and safety of teneligliptin monotherapy compared with a placebo in Chinese patients with type 2 diabetes mellitus inadequately controlled with diet and exercise. Materials and Methods This multicenter, randomized, double‐blind, placebo‐controlled, parallel‐group study, carried out at 42 sites, enrolled type 2 diabetes patients with glycosylated hemoglobin 7.0 to

The gut microbiome affects systemic metabolism and is a therapeutic target for type 2 diabetes. Here the authors demonstrate in a randomized controlled trial that effects of berberine, a plant alkaloid known to lower blood glucose, may be explained by the inhibition of Ruminococcus bromii mediated biotransformation of the bile acid deoxycholic acid.

Functional groups on support membranes are important for the preparation of high-performance membrane adsorbers (MAs). In this work, the effects of different functional groups on support membranes on the adsorption performance of MAs were studied through surface modifications such as alkali treatment, oxidation treatment and polydopamine (PDA) deposition. Our experiment results indicate that chemical bonds are prone to form between C-F on PVDF membrane and amine groups on PEI, which serves to improve the protein adsorption capacity. Furthermore, PDA deposition has the potential to enhance protein adsorption capacity. Introducing a PDA layer on a support membrane with uniform pore size distribution shows potential to improve protein adsorption capacity.

The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR). In order to solve the problem of radar sampling time variation exacerbated by extending the beam dwell time when detecting weak targets, a sector-matching (SM) PHD filter is proposed, which combines the actual radar system with a PHD filter and quantifies the relationship between the beam dwell time, the false alarm rate and the detection probability. The proposed filter divides the scanning area into small sectors to obtain actual multi-target measurement times and rederives the prediction and update steps based on the actual sampling time. Furthermore, a state correction step is added before state extraction. Applying the SM structure to the basic Gaussian mixture PHD (GM-PHD) filter and labeled GM-PHD filter, the simulation results demonstrate that the proposed structure can improve the accuracy of multi-weak-target state estimation in the dense clutter and can continuously generate explicit trajectories. The overall real-time performance of the proposed filter is similar to that of the PHD filter.

The Sox family plays essential roles as transcription factors in vertebrates; however, little is known about the Sox family in Lutraria sieboldii. L. sieboldii are pleasant to eat with a short growth cycle and have become one of the best bottom-seeded enrichment species in Guang Xi. In this study, Sox2 (named LsSox2) and Sox9 (named LsSox9) from L. sieboldii were cloned, and their expression patterns were analyzed. The length of the LsSox2 gene coding sequence was 1011 bp, encoding 336 amino acids, and LsSox9 was 1449 bp, encoding 482 amino acids. LsSox2 had its highest expression levels in the ovary, which were 356 times those in testis, whereas LsSox9 presented higher expression in testis, which was 6 times more highly expressed than in the ovary. LsSox2 exhibited the highest expression during the morula stage, which was 20 times that of the D-shaped larvae or zygote. LsSox9 exhibited two expression peaks, one at the four-cell stage and the other at the trochophore stage, while the lowest expression was in the zygote. LsSox9 was 73 times more highly expressed in the four-cell stage than in the zygote stage. During gonadal development, LsSox2 presented the highest expression in the mature ovary, which was 756 times more highly expressed than in mature testis. LsSox9 presented higher expression in testis at the emission stage which was 6 times more highly expressed than in the ovary. These results indicate that LsSox2 and LsSox9 may play important roles in embryonic and gonadal development.

Objective: Recent studies have shown that neuregulin 4 (Nrg4), a member of the epidermal growth factor (EGF) family of extracellular ligands, plays an important role in the prevention of obesity, insulin resistance and nonalcoholic fatty liver disease (NAFLD). Considering that thyroid hormone (TH) has profound effects on whole-body energy metabolism, we speculate that circulating Nrg4 levels might be altered in patients with hyperthyroidism. Design and methods: A total of 129 hyperthyroid patients and 100 healthy subjects were recruited. Of them, 39 hyperthyroid patients received thionamide treatment for 3 months until euthyroidism. Serum Nrg4 levels were determined using the ELISA method. To further confirm the relationship between TH and Nrg4, C57BL/6 mice were treated with T3 and quantitative real-time PCR was performed to detect Nrg4 gene expression. Results: Serum Nrg4 levels were significantly elevated in hyperthyroid patients as compared with normal controls (3.84 ± 1.63 vs 2.21 ± 1.04 ng/mL, P < 0.001). After achieving euthyroidism by thionamide treatment, serum Nrg4 levels dropped markedly from 3.57 ± 1.26 to 1.94 ± 0.72 ng/ml (P < 0.001). After adjustment for potential confounders, serum Nrg4 levels were independently associated with hyperthyroidism. The upregulation of Nrg4 expression in the livers and white adipose tissues by T3 was further confirmed by animal and cell culture experiments. Conclusions: Serum Nrg4 levels were increased in patients with hyperthyroidism. The liver and white adipose tissue might be primary sources contributing to elevated serum Nrg4 concentrations.

Background: Many studies have demonstrated that microRNAs, a class of small and non-coding RNA molecules, play an important role in the regulation of glucose and lipid homeostasis. In the present study, we sought to investigate the function of miR-592 in the development of obesity-associated metabolic disorders, including hyperglycemia andinsulin resistance. Methods: The expression levels of miR-592 were measured in the liver of obese mice and humans by quantitative reverse transcription PCR. Loss- and gain-of function experiments were employed to explore the metabolic function of miR-592 using locked nucleic acids and adenovirus in lean and obese mice, respectively. The molecular target of miR-592 was determined by western blotting and luciferase reporter assays. Findings: We found a significant decreased expression of miR-592 in the liver of obese mice and humans. Inhibition of miR-592 led to elevated blood glucose levels, enhanced gluconeogenesis and reduced insulin sensitivity in lean mice. In contrast, adenovirus-mediated overexpression of hepatic miR-592 improved metabolic disorders in obese mice. Mechanistically, we found that the transcription factor forkhead box O1 (FOXO1) is a direct target gene of miR-592 to mediate its metabolic functions. miR-592 was able to inhibit the mRNA and protein expression of FOXO1 by binding to its 3′-untranslated region. Interpretations: Our findings demonstrate that obesity-associated down-regulation of miR-592 plays an important role in the progression of metabolic diseases. Restoration of hepatic miR-592 could improve glucose and lipid metabolism in obese mice. Fund: This work is supported by the National Key Research and Development Program of China (No. 2016YFC1304805 to Dr. Chen), Natural Science Foundation of China (No. 81771574 to Dr. Wu), Shanghai Science Foundation (No. 18ZR1437800 to Dr. Li), Science and Technology Commission of Shanghai Municipality (Nos.18dz2304400 and 15,411,970,700 to Dr. Yang). Keywords: Obesity, Gluconeogenesis, Insulin resistance, Liver steatosis, MicroRNAs, FOXO1

Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increases exponentially. The Gaussian mixture probability hypothesis density (GM-PHD) filtering based on a random finite set (RFS) provides an effective method to solve multi-target tracking problems without the requirement of explicit data association. However, it is difficult to track targets accurately in real-time with dense clutter and low detection probability. To solve this problem, this paper proposes a multi-feature matching GM-PHD (MFGM-PHD) filter for radar multi-target tracking. Using Doppler and amplitude information contained in radar echo to modify the weights of Gaussian components, the weight of the clutter can be greatly reduced and the target can be distinguished from clutter. Simulations show that the proposed MFGM-PHD filter can improve the accuracy of multi-target tracking as well as the real-time performance with high clutter density and low detection probability.

Sheep face recognition models deployed on edge devices require a good trade-off between model size and accuracy, but the existing recognition models cannot do so. To solve the above problems, this paper combines Mobilenetv2 with Vision Transformer to propose a balanced sheep face recognition model called MobileViTFace. MobileViTFace enhances the model’s ability to extract fine-grained features and suppress the interference of background information through Transformer to distinguish different sheep faces more effectively. Thus, it can distinguish different sheep faces more effectively. The recognition accuracy of 96.94% is obtained on a self-built dataset containing 5490 sheep face photos of 105 sheep, which is a 9.79% improvement compared with MobilenetV2, with only a small increase in Params (the number of parameters) and FLOPs (floating-point operations). Compared to models such as Swin-small, which currently performs SOTA, Params and FLOPs are reduced by nearly ten times, whereas recognition accuracy is only 0.64% lower. Deploying MobileViTFace on the Jetson Nano-based edge computing platform, real-time and accurate recognition results are obtained, which has implications for practical production.

The decarbonization of the shipping industry has risen to a new level of significance on the agendas of policy makers and strategic decision makers. This paper focuses on the likely cost implications for the shipping industry of recently implemented, and future planned policies that seek to reduce the carbon footprint of the sector. The scale of the problem is presented through a summary review of the International Maritime Organization’s fourth greenhouse gas study of 2020. The regulatory context, which is emerging as a response to overcoming this challenge, is analyzed at both the global and EU level. The potential cost implications for the future, in terms of this new regulatory context, are then analyzed on the basis of a review of academic and industry sources. Our paper concludes that alternative, zero-carbon fuels are the best way forward, but that operational and technological innovations will not be sufficient to bring about the complete decarbonization of the industry within the foreseeable future. Thus, market-based measures are required. Finally, the options available, and the likely implications of each of these options, is addressed, suggesting that there will be both winners and losers in the race to decarbonize the shipping industry.

In order to solve the problem that the measurement noise covariance may be unknown or change with time in actual multi-target tracking, this paper brings the variational Bayesian approximation method into the trajectory probability hypothesis density (TPHD) filter and proposes a variational Bayesian TPHD (VB-TPHD) filter to obtain measurement noise covariance adaptively. By modeling the unknown covariance as the random matrix that obeys the inverse gamma distribution, VB-TPHD filter minimizes the Kullback–Leibler divergence (KLD) and estimates the sequence of multi-trajectory states with noise covariance matrices simultaneously. We propose the Gaussian mixture VB-TPHD (AGM-VB-TPHD) filter under adaptive newborn intensity for linear Gaussian models and also give the extended Kalman (AEK-VB-TPHD) filter and unscented Kalman (AUK-VB-TPHD) filter in nonlinear Gaussian models. The simulation results prove the effectiveness of the idea that the VB-TPHD filter can form robust and stable trajectory filtering while learning adaptive measurement noise statistics. Compared with the tag-VB-PHD filter, the estimated error of the VB-TPHD filter is greatly reduced, and the estimation of the trajectory number is more accurate.

The authors examine the effects of antidiabetic medication on the gut microbiome and bile acid composition and show that these data can be used to stratify treatment regimens for type 2 diabetes.

We analyzed the complete mitochondrial genome of the recently discovered Xinyuan honey bee, Apis mellifera sinisxinyuan using single molecule real-time sequencing. The mitochondrial genome of A. m. sinisxinyuan is a circular molecule of 16,886 bp, comprising 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and a control region rich in A + T. Phylogenetic analysis using 13 protein-coding genes supports a close relationship to another M-lineage honey bee, A. m. mellifera.

Stanniocalcin 2 (STC2), a secreted glycoprotein hormone, regulates many biological processes, including cell proliferation, apoptosis, tumorigenesis, and atherosclerosis. However, its role in hepatic triglyceride metabolism remains unknown. In the present study, we found that expression levels of STC2 were significantly reduced in the livers of leptin-deficient and high fat diet-induced obese mice. Systemic administration of STC2 recombinant protein or adenovirus-mediated overexpression of STC2 markedly attenuated hepatosteatosis and hypertriglyceridemia in obese mice. At the molecular level, we found that STC2 activated the STAT3 signaling pathway to inhibit lipogenic gene expression. Consistently, in vitro studies further showed that inhibition of STAT3 signaling abolished the anti-steatotic effects of STC2. Together, our results revealed an important role of STC2 in the regulation of hepatic triglyceride metabolism, which might provide a potential therapeutic target for the treatment of fatty liver and related metabolic disorders.

Additive manufacturing (AM) of single crystal superalloys has got some progress in recent researches, but there are few reports on eliminating recrystallization or mechanical properties of single crystal superalloys by AM. In this work, single-crystal samples of SRR99 were fabricated at high temperature by laser melting deposition (LMD). Owing to the high temperature of substrate in the deposition process, recrystallization in the following heat treatment process was eliminated. The contrast sample of SRR99 was prepared by directional solidification, and the microstructure evolution during heat treatment and tensile property of deposited samples were analyzed. The results showed that the shapes of the γ ′ phase became irregular after solution treatment in deposited samples. After a solid solution and aging treatment, the γ ′ phase size is larger and the γ ′ volume fraction is slightly lower in deposited samples than in the contrast sample. As a result, the yield and tensile strength of deposited samples are slightly lower than that of contrast samples, but the plasticity of deposited samples is better.

The aim of this study was to determine whether TPCN2 genetic variants are associated with type 2 diabetes and to elucidate which variants in TPCN2 confer diabetes susceptibility in the Chinese population.The sample population included 384 patients with type 2 diabetes and 1468 controls. Anthropometric parameters, glycemic and lipid profiles and insulin resistance were measured. We selected 6 TPCN2 tag single nucleotide polymorphisms (rs35264875, rs267603153, rs267603154, rs3829241, rs1551305, and rs3750965). Genotypes were determined using a Sequenom MassARRAY SNP genotyping system.Ultimately, we genotyped 3 single nucleotide polymorphisms (rs3750965, rs3829241, and rs1551305) in all individuals. There was a 5.1% higher prevalence of the rs1551305 variant allele in type 2 diabetes individuals (A) compared with wild-type homozygous individuals (G). The AA genotype of rs1551305 was associated with a higher diabetes risk (p

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.