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Abstract Aging is identified as a significant risk factor for severe coronavirus disease-2019 (COVID-19), often resulting in profound lung damage and mortality. Yet, the biological relationship between aging, aging-related comorbidities, and COVID-19 remains incompletely understood. This study aimed to elucidate the age-related COVID19 pathogenesis using an Hutchinson-Gilford progeria syndrome (HGPS) mouse model, a premature aging disease model, with humanized ACE2 receptors. Pathological features were compared between young, aged, and HGPS hACE2 mice following SARS-CoV-2 challenge. We demonstrated that young mice display robust interferon response and antiviral activity, whereas this response is attenuated in aged mice. Viral infection in aged mice results in severe respiratory tract hemorrhage, likely contributing a higher mortality rate. In contrast, HGPS hACE2 mice exhibit milder disease manifestations characterized by minor immune cell infiltration and dysregulation of multiple metabolic processes. Comprehensive transcriptome analysis revealed both shared and unique gene expression dynamics among different mouse groups. Collectively, our studies evaluated the impact of SARS-CoV-2 infection on progeroid syndromes using a HGPS hACE2 mouse model, which holds promise as a useful tool for investigating COVID-19 pathogenesis in individuals with premature aging.

ObjectivesDuring the COVID-19 pandemic, online health information search has been shown to influence the public’s health beliefs, risk attitudes, and vaccination behavior. This study constructs a conditional process model to explore how online health information search impacts public vaccination behavior, considering critical factors like healthcare system satisfaction, vaccine risk perception, and the perceived usefulness of information.MethodsData from the 2021 Chinese General Social Survey (N = 2,547) were analysed. The study utilized logistic regression, path analysis, and the Bootstrap method to test the conditional process model.ResultsIncreased online health information search promotes vaccination behavior, while increased vaccine risk perception hinders vaccination behavior. Higher satisfaction with the healthcare system encourages vaccination behavior, but online health information search reduces healthcare system satisfaction. Satisfaction with the healthcare system and vaccine risk perception play a chain mediating role between online health information search and vaccination behavior. Additionally, the perceived usefulness of information has a negative moderating effect on online health information search and healthcare system satisfaction.ConclusionThe research findings provide new insights for health information dissemination and vaccination interventions.

Summary: Background: Accurate staging of liver fibrosis (LF) is essential for clinical management in chronic liver disease. While non-contrast MRI (NC-MRI) yields valuable information for liver assessment, its effectiveness in predicting LF remains underexplored. This study aimed to develop and validate artificial intelligence (AI)-powered models utilizing NC-MRI for staging LF. Methods: A total of 1726 patients from Shengjing Hospital of China Medical University, registered between October 2003 and October 2022, were retrospectively collected, and divided into development (n = 1208) and internal test (n = 518) cohorts. An external test cohort consisting of 337 individuals from six centers, registered between June 2015 and November 2022, were also included. All participants underwent NC-MRI (T1-weighted imaging, T1WI; and T2-fat-suppressed imaging, T2FS) and liver biopsies. Two classification models (CMs), named T1 and T2FS, were trained on respective image types using 3D contextual transformer networks and evaluated on both test cohorts. Additionally, three CMs—Clinic, Image, and Fusion—were developed using clinical features, T1 and T2FS scores, and their integration via logistic regression. Classification effectiveness of CMs was assessed using the area under the receiver operating characteristic curve (AUC). A comparison was conducted between the optimal models (OMs) with highest AUC and other methods (transient elastography, five serum biomarkers, and six radiologists). Findings: Fusion models (i.e., OM) yielded the highest AUC among the CMs, achieving AUCs of 0.810 for significant fibrosis, 0.881 for advanced fibrosis, and 0.918 for cirrhosis in the internal test cohort, and 0.808, 0.868, and 0.925, respectively, in the external test cohort. The OMs demonstrated superior performance in AUC, significantly surpassing transient elastography (only for staging ≥ F2 and ≥ F3 grades), serum biomarkers, and three junior radiologists for staging LF. Radiologists, with the aid of the OMs, can achieve a higher AUC in LF assessment. Interpretation: AI-powered models utilizing NC-MRI, including T1WI and T2FS, accurately stage LF. Funding: National Natural Science Foundation of China (No. 82071885); General Program of the Liaoning Provincial Department of Education (LJKMZ20221160); Liaoning Province Science and Technology Joint Plan (2023JH2/101700127); the Leading Young Talent Program of Xingliao Yingcai in Liaoning Province (XLYC2203037).

Recent years have seen the Ulva green tide and Sargassum golden tide become commonplace in the coastal waters of China. However, little is known on how the combination of ocean warming and eutrophication would affect the interaction of green and golden tides. In this study, we cultured the green tide alga Ulva prolifera and the golden tide alga Sargassum horneri under different temperatures (5, 10, 15, 20, 25, and 30°C) and two nutrient concentrations (Low nutrient, LN: 5 μM-nitrate and 0.5 μM-phosphate; High nutrient, HN: 500 μM-N and 50 μM-P) in both monoculture and coculture systems to investigate the physiological responses and their competitive relationships. In monocultures, the growth of U. prolifera and S. horneri, along with pigment concentrations and photosynthesis, increased with rising temperature, reaching a plateau at 15 - 25°C. However, when the temperature increased to 30°C, the growth of U. prolifera and S. horneri decreased abruptly, with S. horneri even suffering death. In coculture, the growth of both U. prolifera and S. horneri was inhibited compared to the monoculture, with the greatest decline observed in S. horneri at 25°C under two nutrient conditions. Our results show that U. prolifera would outcompete S. horneri under high temperature in coculture, suggesting that ocean warming would enhance the competitive advantage of green tide over golden tide under eutrophication in the future.

The legacy of industry reflects a city's journey and marks its cultural essence, highlighting the significance of preserving and promoting such historical assets. There is a noticeable gap in the discourse surrounding the intrinsic value of industrial heritage attractions and their impact on the fidelity of tourists. In light of this, the present investigation, rooted in the ABC attitude framework, examines the linkage between the perceived value, contentment, and allegiance of initial visitors to these sites. The study delves into the intermediary function of contentment and the variable influence of gender. Focusing on the Maoming Open-pit Ecological Park as a case in point, this research gathered and scrutinized 320 valid questionnaires. The outcomes demonstrate that the perceived value exerts a positive effect on both the satisfaction and the steadfastness of tourists. Satisfaction is identified as an intermediary between perceived value and tourist steadfastness. Additionally, gender plays a notable role in moderating specific aspects of the model. This inquiry aids in harnessing the potential of industrial heritage sites for the advancement of tourism, fostering economic prosperity and the rebirth of cultural practices. Furthermore, it lays down essential theoretical and practical building blocks for the evolution of industrial heritage as a tourism asset.

Accurately estimating the state of charge （SOC） of energy storage batteries is of paramount importance for achieving balanced charging and discharging， and mitigating capacity degradation caused by overcharging and overdischarging. In view of the complex chemical states and nonlinear time-varying characteristics of SOC in energy storage batteries， this paper proposes a joint SOC estimation method for lithium-ion batteries based on the variable forgetting factor recursive least squares （VFFRLS） and unscented Kalman filter （UKF） algorithms. The VFFRLS algorithm is employed for online identification of battery model parameters such as resistance and capacitance， and based on the identification results， the UKF algorithm is utilized for real-time SOC estimation. Experimental results demonstrate that the proposed joint method exhibits high accuracy and stability.

Okadaic acid （OA） is widely distributed in seafoods and is a remarkable threat to food safety. Owing to its high toxicity， small size， and low molecular weight， obtaining antibodies against OA is a complex and difficult task. The aptamer SELEX technology can be used to achieve high affinity and single aptamer sequences for small molecules. Accordingly， this technology has several advantages for the detection of shellfish biotoxins. This review highlights the applications of biosensors based on OA aptamers， such as colorimetric sensors， fluorescent sensors， electrochemical sensors， and so on， in the rapid detection of OA. A summary of the performance and application potential of these sensors is provided， and the development direction of aptamer sensors is discussed to better promote their development and application.

Abstracts: Background: In the low-resource rural areas, older adults may experience prolonged and severe depressive symptoms. This study aimed to investigate the relationship between uric acid, depressive symptoms and immunoinflammatory among rural older adults. Method: This case-control study was conducted in 17 rural villages in Hunan Province, China, between January 2023 and April 2024. This study included 180 participants: (1) Rural Older Adults with Depressive Symptoms group:90 patients with first-time diagnosed with depressive symptoms (Geriatric Depression Scale-15, GDS-15 ≥ 5 scores); (2) Control group: 90 individually matched (age and sex) healthy subjects (GDS-15

Memristor crossbar arrays are expected to achieve highly energy-efficient neuromorphic computing via implementing parallel vector–matrix multiplication (VMM) in situ. The similarities between memristors and neural synapses offer opportunities for realizing hardware-based brain-inspired computing, such as spike neural networks. However, the nonlinear I–V characteristics of the memristors limit the implementation of parallel VMM on passive memristor crossbar arrays. In our work, we propose to utilize differential conductance as a synaptic weight to implement linear VMM operations on a passive memristor array in parallel. We fabricated a TiO2/HfO2 memristor crossbar array, in which differential-conductance-based synaptic weight exhibits plasticity, nonvolatility, multi-states, and tunable ON/OFF ratio. The noise-dependent accuracy performance of VMM operations based on the proposed approach was evaluated, offering an optimization guideline. Furthermore, we demonstrated a spike neural network circuit capable of processing small spiking signals through the differential-conductance-based synapses. The experimental results showcase effective space-coded and time-coded spike pattern recognition. Importantly, our work opens up new possibilities for the development of passive memristor arrays, leading to increased energy and area efficiency in brain-inspired chips.

Abstract Radial spokes (RS) transmit mechanochemical signals between the central pair (CP) and axonemal dynein arms to coordinate ciliary motility. Atomic-resolution structures of metazoan RS and structures of axonemal complexes in ependymal cilia, whose rhythmic beating drives the circulation of cerebrospinal fluid, however, remain obscure. Here, we present near-atomic resolution cryo-EM structures of mouse RS head-neck complex in both monomer and dimer forms and reveal the intrinsic flexibility of the dimer. We also map the genetic mutations related to primary ciliary dyskinesia and asthenospermia on the head-neck complex. Moreover, we present the cryo-ET and sub-tomogram averaging map of mouse ependymal cilia and build the models for RS1-3, IDAs, and N-DRC. Contrary to the conserved RS structure, our cryo-ET map reveals the lack of IDA-b/c/e and the absence of Tektin filaments within the A-tubule of doublet microtubules in ependymal cilia compared with mammalian respiratory cilia and sperm flagella, further exemplifying the structural diversity of mammalian motile cilia. Our findings shed light on the stepwise mammalian RS assembly mechanism, the coordinated rigid and elastic RS-CP interaction modes beneficial for the regulation of asymmetric ciliary beating, and also facilitate understanding on the etiology of ciliary dyskinesia-related ciliopathies and on the ependymal cilia in the development of hydrocephalus.

With the deep integration of the marine industry and modern finance, the capital market has become an important channel for investment and financing of offshore enterprises. However, the frequent occurrence of major risk events in the world today has had a strong impact on the capital market. At the same time, it has accelerated the accumulation and spread of risks in the capital market, and has also had a significant impact on sea-related enterprises, damaging the safety and stability of the marine industry chain and supply chain. In this context, studying the risk spillover characteristics between sea-related enterprises when major risk events occur, exploring the risk spillover effects between different industrial sectors, helps to establish efficient risk prevention and warning mechanisms, improve the resilience and safety level of the marine industry chain and supply chain, and promote high-quality growth of the marine economy. This paper used the spillover index method based on the TVP-VAR model to analyze the risk spillover effects among sea-related enterprises belonging to the fishing, offshore oil, maritime, and shipbuilding industries, and constructed a risk spillover network for topological feature analysis. The empirical results showed that the risk spillover intensity of sea-related enterprises to enterprises in the same industry sector was the highest. From an industrial perspective, the fishing and shipping industries were net recipients of risk, while the offshore oil and shipping industries were net spillovers of risk. The risk spillover effect had a time-varying characteristic, and the overall risk spillover index of sea-related enterprises would show a significant upward trend under the impact of major risk events. Compared to the period without major risk events, the average clustering coefficient of the risk spillover network during the period of major risk events was larger, and the connections of sea-related enterprises in the network were closer, resulting in higher risk transfer efficiency.

AbstractContext Qingyi granules can be used to effectively treat patients with severe acute pancreatitis (SAP).Objective To elucidate the role of gut microbiota-mediated metabolism in the therapeutic effects of Qingyi granules.Materials and methods Sprague–Dawley rats were grouped into the sham operation, SAP model, Qingyi granule intervention (Q, 1.8 g/kg) and emodin intervention (E, 50 mg/kg) groups and observed for 24 h. H&E staining and ELISA were used for histopathological analysis and serum enzyme and cytokine assays. 16S rDNA sequencing and UHPLC-HRMS were used for gut microbiota analysis and untargeted metabolomics.Results In SAP rats, Qingyi granules decreased the pancreatic pathological score (Q, 7.4 ± 1.14; SAP, 11.6 ± 1.14, p

We present an innovative methodology for the synthesis of MXene membranes through a dual-stage process involving etching and subsequent thermal self-crosslinking. A molar ratio of 1 (Al3+):9 (F−) using HCl/LiF was employed to convert raw Ti3AlC2 (MAX phase) into MXene within 48 h at 40 °C. This procedure predominantly yielded monolayers distinguished by diameters exceeding 500 nm, elevated crystallinity and a high overall yield. Advanced characterization techniques, including FESEM, TEM, HRTEM, AFM, XPS, and FTIR, were utilized. Instrumental analysis confirmed the formation of MXene exhibiting a single-flake morphology with diameters exceeding 500 nm. These monolayers were intact and continuous, with smooth peripheries and a uniform thickness of 2.1 nm. The surfaces were predominantly composed of carbon (C), oxygen (O), and titanium (Ti) atoms, interconnected by chemical bonds such as C–Ti–O, C–Ti–OH, C–C, C–O, and Ti–O. In the subsequent phase, vacuum filtration facilitated the assembly of a self-supporting MXene membrane. Thermal treatment at 170 °C for 30 h resulted in the reinforcement of C–Ti–O bonds within the nanosheets, increasing their prevalence to 43.14 % and 19.47 %, respectively. This thermal regulation reduced the interlayer d-spacing from 4.33 to 3.54 Å, which significantly improved the gas separation efficiency beyond the Knudsen diffusion limit, as demonstrated by the αH2/CF4 value exceeding 23.0.

The inversion of inherent optical properties (IOPs) and chlorophyll a (Chla) is one of the key objectives in water color remote sensing, and hyperspectral remote sensing with rich spectral information makes precise inversion possible. In this study, we developed a semi-analytical estimation method for inland water IOPs based on the quasi-analytical algorithm (QAA). Considering the complex optical characteristics of inland waters, empirical parameter regional optimization was conducted. Furthermore, a dual-band joint inversion strategy and Gaussian function fitting method were utilized to optimize the solution processes for the backscattering coefficient of particles (bbp) and the absorption coefficient of phytoplankton pigments (aph), respectively. This approach overcomes the limitations of single-band bbp inversion in inland waters. In addition, it directly decomposes the absorption coefficient to obtain aph using a Gaussian function, which can reduce the intermediate steps and errors caused by indirect inversion. For Chla, we constructed a binary inversion model using aph(677) and remote sensing reflectance (Rrs), where the coefficient of determination (R2) exceeded 0.8. We also constructed an airborne hyperspectral image correction process, including vicarious calibration, atmospheric correction, and bidirectional reflectance distribution function (BRDF) correction, obtaining high-precision Rrs images. The ground models were successfully applied to the airborne hyperspectral images, mapping the spatial distribution of IOPs and Chla concentration in the study area. The experiments demonstrated that the proposed semi-analytical method using airborne hyperspectral imagery exhibits a good performance in terms of modeling accuracy and mapping analysis, and successfully applied to long-term monitoring using satellite hyperspectral images, highlighting the significant potential of hyperspectral remote sensing for high-precision monitoring of regional water bodies.

Purposes This research is conducted to realize the assembly of steel-concrete composite beams without wet joints, so that the composite beam structure is easy to be disassembled and replaced. Methods ABAQUS analysis saftware was used to study the mechanical properties of high-strength bolted steel-concrete composite beams, an accurate and reliable finite element model was established, and the connection method of precast concrete slabs was improved. On this basis, the effects of bolt pre-tightening force, splicing gap, bolt diameter and strength on the stiffness and slip of the composite beam were studied. Findings The stiffness of the composite beam was improved with the increase of pre-tightening force, and decreased first and then remained unchanged with the increase of splicing gap. The mechanism of the sliding process of steel-concrete interface was analyzed, and the principle of increasing preload to improve the stiffness of the composite beam was clarified. The comparison between the fitting and the test results proves that the upgraded method proposed in this paper is effective and efficient in improving the stiffness and feasibility of the composite beam.

Currently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to achieve reasonable and scientific design of anchor bolt support. Firstly, it is suggested to establish an intelligent prediction database for coal mine roadway support. Through on-site research, questionnaire survey, and literature search, the coal mine roadway samples are collected. The data is processed using methods such as filling in missing values, modifying outliers in box charts, and removing local abnormal factors to establish a coal roadway support database. The paper proposes a coal roadway support parameter prediction model based on synthetic minority oversampling technique (SMOTE) - genetic algorithm (GA) - SVM. The data in the database is divided into training and testing sets. The SMOTE technology is used to balance training samples, and improve the model's fitting capability for minority class samples. The training process uses GA to globally optimize the hyperparameters of SVM, further improving the overall performance of the model. The test results show that the classificaton precision of the SMOTE-GA-SVM model reaches 83.8%, which is 21.8% higher than the traditional SVM model. The machine learning methods such as SVM, artificial neural network (ANN), RF, AdaBoost (ADA), and naive Bayesian classifier (NBC) are introduced into the prediction of coal roadway anchor support parameters. The corresponding support parameter prediction models are established. The comparison results showed that the best to worst prediction models are ranked as SMOTE-GA-SVM, RF, GA-ANN, SVM, NBC, and ADA, with an average classificaton precision of 69.9%. The result verifies the feasibility of machine learning methods in predicting the parameters of coal roadway bolt support. The SMOTE-GA-SVM model is applied in Shanxi Huobaoganhe Coal Mine Co., Ltd., with a precision of 87.5% and strong applicability and reliability.

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.

Abstract Input-Output (IO) data describing supply-demand relationships between buyers and sellers for goods and services within an economy have been used not only in economics but also in scientific, environmental, and interdisciplinary research. However, most conventional IO data are highly aggregated, resulting in challenges for researchers and practitioners who face complex issues in large countries such as China, where firms within the same IO sector may have significant differences in technologies across subnational regions and different ownerships. This paper is the first attempt to compile China’s interprovincial IO (IPIO) tables with separate information for mainland China-, Hong Kong, Macau, Taiwan-, and foreign-owned firms inside each province/industry pair. To do this, we collect relevant Chinese economic census data, firm surveys, product level Custom trade statistics, and firm value-added tax invoices and consistently integrate them into a 42-sector, 31-province IO account covering 5 benchmark years between 1997–2017. This work provides a solid foundation for a diverse range of innovative IO-based research in which firm heterogeneity information about location and ownership matters.

Abstract Background This study aims to evaluate the cost-effectiveness of immunosuppressive therapy for patients with progressive idiopathic membranous nephropathy (IMN) from the Chinese healthcare system perspective. Methods To estimate the cost-effectiveness of four regimens namely cyclophosphamide, cyclosporine, rituximab and tacrolimus-rituximab in treatment of IMN recommended by the updated Kidney Disease: Improving Global Outcomes (KDIGO) guideline 2021, a Markov model with five discrete states (active disease, remission, dialysis, kidney transplant and death) based on IMN patients aged 50 or above over a 30-years time horizon was constructed. Total costs were imputed from the Chinese healthcare system perspective, and health outcomes were converted into quality-adjusted life years (QALYs). The incremental cost-effectiveness ratio (ICER) was used to describe the results. The willingness-to-pay (WTP) threshold was set at $12,044 (China’s 2021 Gross Domestic Product per capita). Sensitivity analyses were performed to test the uncertainties of the results. Result Compared with cyclophosphamide, both cyclosporine (incremental cost $28,337.09, incremental QALY-1.63) and tacrolimus-rituximab (incremental cost $28,324.13, incremental QALY -0.46) were considered at strictly dominated for their negative values in QALYs, and the ICER value of rituximab was positive (incremental cost $9,162.19, incremental QALY 0.44). Since the ICER of rituximab exceeds the pre-determined threshold, cyclophosphamide was likely to be the best choice for the treatment of IMN within the acceptable threshold range. The results of the sensitivity analysis revealed that the model outcome was mostly affected by the probability of remission in rituximab. In a probabilistic sensitivity analysis, cyclophosphamide had 62.4% probability of being cost-effective compared with other regimens when the WTP was $12,044 per QALY. When WTP exceeded $18,300, rituximab was more cost-effective than cyclophosphamide. Conclusion Compared with cyclosporine, rituximab and tacrolimus-rituximab, our model results indicated that cyclophosphamide represented the most cost-effective regimen for patients with progressive IMN in China.

The classification of surrounding rock stability of coal roadway has important theoretical and practical significance for the design, construction and management of on-site rock mass engineering.This paper selected seven key indexes that affect the surrounding rock stability of coal roadway, collected the samples through field cases collection, questionnaires and literature measurement, and established the surrounding rock stability classification database.By drawing on six machine learning methods, this study established the classification prediction models of surrounding rock stability of coal roadway accordingly.Through model calculation, it is concluded that the Neural Network and the improved Support Vector Machine model have higher prediction accuracy.The model is applied to the actual project of Huozhou mining area.Results show that the neural network and the improved support vector machine methods have high prediction accuracy and good reliability.

Abstract The June‐July Yangtze flooding in 1998 and 2020 drew incredible attention owing to the extreme precipitation events and devastating societal/economic damages. However, the quantitative estimation of the moisture transport mechanism is intensely discussed but still unresolved. Here we investigated two events from a unique perspective of Eulerian atmospheric water tracers that tries to explain the two events from model physics. The results showed that the moisture supplies from the Northwest Pacific decreased despite of different inducements, whereas the southwest summer monsoon (SWSM)‐related moisture supplies exhibited conspicuous enhancements in both events, suggesting that the SWSM‐related moisture supplies controlled the occurrence of Yangtze flooding. The physical processes of the two events were further compared. The 2020 flooding was more severe than the 1998 event, which was related to the stronger advective convergences and in‐stratus condensations of the SWSM‐related moisture.

Background: This paper explores the assessment value of pathological condition of serum adiponectin (APN) and amylin in primary osteoporosis (POP) and their correlation with bone metabolism indexes. Methods: From January 2019 to June 2021, 79 cases of POP patients were selected as the research objects. A test of the patients' bone density was conducted, and clinical grading of POP was via T value (normal, mild, moderate, severe). The analysis of the assessment value of pathological condition of serum APN and amylin for POP and their association with bone metabolism indexes in patients was performed. Results: APN and amylin in patients were declined with POP's aggravation. APN of 5.15 mg/mL or less and amylin of 15.38 pmol/L or less were risk factors influencing the aggravation of pathological condition of POP (P< 0 .0 5). The area under the curve (AUC) of combined detection of APN and amylin to assess the severity of POP was elevated vs. alone test of amylin (P< 0.05). 25-hydroxyvitamin D (25-(OH) D) and total type 1 procollagen amino-terminal propeptide (t-PINP) in patients were descended with the aggravation of pathological condition of osteoporosis (P < 0.05). At the same time, no distinct differences were presented in the three groups of type I collagen hydroxyl terminal peptide b degradation product (b-CTX) and N-terminal osteocalcin (N-MID) (P> 0.05). APN, amylin, 25(OH)D, b-CTX, and t-PINP were negatively linked with POP clinical grade (P< 0.05). APN and amylin were associated with 25-(OH) D, b-CTX, t-PINP (P< 0.05), and APN and amylin were not linked with N-MID (P> 0.05). Conclusions: Serum APN and amylin are provided with evaluation values for the severity of POP and are associated with bone metabolism in patients.

Background/Purpose: Specific immunotherapy is the only effective etiological treatment for allergic rhinitis, but subcutaneous immunotherapy has a slow onset and poor compliance. Predicting the clinical efficacy of subcutaneous immunotherapy in advance can reduce unnecessary medical costs and resource waste. This study aimed to identify metabolites that could predict the efficacy of subcutaneous immunotherapy on seasonal allergic rhinitis by serum metabolomics. Methods: Patients (n = 43) with Artemisia sieversiana pollen allergic rhinitis were enrolled and treated with subcutaneous immunotherapy for one year. Patients were divided into the ineffective group (n = 10) and effective group (n = 33) according to the therapeutic index. Serum samples were collected before treatment. Metabolomics was determined by liquid chromatography-mass spectrometry combined with gas chromatography-mass spectrometry and analyzed differential compounds and related metabolic pathways. Results: A total of 129 differential metabolites (P

Abstract After the smooth cordgrass Spartina alterniflora invaded coastal China, native birds started avoiding the green desert, and bird diversity declined. After many years, a few passerine birds began to enter and use smooth cordgrass, but only birds with a nonspecialised habitat. In this study, we found that a native reed‐specific bird, the parrotbill Calamornis heudei, flocked and sang in a smooth cordgrass habitat throughout the overwintering period near Sheyang Port in Yancheng, Jiangsu Province. This observation suggests that native obligate birds may be forced to adapt to exotic smooth cordgrass habitats after long‐term coexistence, which would, obviously, affect the distribution, feeding and reproduction of birds. The concern is that this could be an ecological trap, leading to unknown consequences. More research is required to examine the process occurring along the Chinese coast.

Recently, the deep learning algorithms have been increasingly utilized in remote sensing change detection. However, incomplete buildings and the blurred edges caused by the complex scenes in change detection applications make the detection results fail to describe the real land cover changes. Superpixels can be used to alleviate edge blurring, but the existing superpixel methods cannot be trained jointly with the models in change detection. In this work, we investigated an innovative double-head method using deep learning, called double U-Net (W-Net), which consists of a superpixel module and a change detection module. Due to the superpixel module, W-Net can handle building edges very well. In order to solve problem that multiple subtasks fail to achieve the optimal results, a two-branch multi-task coupling framework of change detection and superpixels is designed for W-Net, which enables the model to achieve a globally optimal detection performance. The advancement of the W-Net was demonstrated using three public datasets. The F1score on LEVIR-CD dataset was 0.9031 and kappa coefficient was 0.8969. The F1-score on WHU building dataset was 0.9172 and kappa coefficient was 0.9142. The F1-score on SYSU-CD dataset was 0.8167and and kappa coefficient was 0.7724. The experiments confirmed that the W-Net is capable to detect the edges of changed area better and outperforms the other advanced change detection methods.

Understanding the factors that hamper immune therapy in breast cancer may increase the range of patients who benefit. Here authors show that breast cancer cells produce and subsequently transfer active TGF-β type II receptors to CD8 + T cells to render them exhausted, thus paralyzing the anti-tumor immune response.

With the development of e-commerce, manufacturers have opportunities to sell products to consumers through direct channels in addition to independent retailers. Although manufacturers’ encroachment upon the retailing market of traditional goods has been extensively studied, there is a lack of research on manufacturers’ encroachment in a pharmaceutical supply chain. This paper analyzes a pharmaceutical manufacturer encroachment strategy by taking into account the distinctive features of drugs, in which drug expenses are covered by medical insurance only in the physical channel and patients' channel preference is also considered. We find that under certain circumstances, the manufacturer encroachment may lead to a win-win outcome for both the manufacturer and the retailer, contradicting the conventional wisdom that the manufacturer's encroachment into retail space is detrimental to the independent retailer. Additionally, the manufacturer is more likely to encroach when the incurred extra cost is sufficiently small, the perceived quality of the drug selling through the direct channel is sufficiently high and the out-of-pocket percentage cost of the drug selling in the physical channel is high. We further study the impact of manufacturer encroachment on social welfare and find that the introduction of encroachment decreases the social welfare when patients' perceived quality of the drug obtained through the online store is low and improves the social welfare when the perceived quality exceeds a certain threshold.

Cheqianzi Decoction (CQD) is a Traditional Chinese Medicine (TCM) formula comprising four herbs and is recorded in the Ancient Materia Medica “Shengji Zonglu”. Individually, these four herbs have been shown to reduce uric acid (UA) levels, to treat hyperuricemia (HUA), and alleviate kidney damage. However, the therapeutic efficacy of the CQD and related mechanism are not yet clear.In this study, high performance liquid chromatography (HPLC) analysis confirmed that the contents of the chemical components of the four herbal medicines were in accordance with the provisions of the Chinese Pharmacopoeia. A total of 99 potential targets were identified in the network pharmacology analysis of CQD, indicating its involvement in the regulation of inflammatory and apoptotic signaling pathways, and potential value for treating HUA and alleviating kidney injury. In vivo pharmacodynamic studies showed that compared with the Model group, significantly decreased levels of serum uric acid (SUA), serum creatinine (SCr), blood urea nitrogen (BUN) (all P < 0.05), and inflammatory factors (P < 0.01) were detected in the CQD group. Quantitative real-time PCR and Western blot analyses showed that compared with the Model group, adenosine triphosphate (ATP)-binding cassette efflux transporter G2 (ABCG2) expression in the CQD group was significantly upregulated (P

IntroductionThe mechanism of the immediate adverse drug reactions (ADRs) induced by ShenMai injection (SMI) has not been completely elucidated. Within 30 minutes, the ears and lungs of mice injected with SMI for the first time showed edema and exudation reactions. These reactions were different from the IV hypersensitivity. The theory of pharmacological interaction with immune receptor (p-i) offered a new insight into the mechanisms of immediate ADRs induced by SMI.MethodsIn this study, we determined that the ADRs were mediated by thymus-derived T cells through the different reactions of BALB/c mice (thymus-derived T cell normal) and BALB/c nude mice (thymus-derived T cell deficient) after injecting SMI. The flow cytometric analysis, cytokine bead array (CBA) assay and untargeted metabolomics were used to explain the mechanisms of the immediate ADRs. Moreover, the activation of the RhoA/ROCK signaling pathway was detected by western blot analysis.ResultsIn BALB/c mice, the vascular leakage and histopathology results showed the occurrence of the immediate ADRs induced by SMI. The flow cytometric analysis revealed that CD4+ T cell subsets (Th1/Th2, Th17/Treg) were imbalanced. And the levels of cytokines such as IL-2, IL-4, IL12P70 and INF-γ increased significantly. However, in BALB/c nude mice, all the indicators mentioned above have not changed significantly. The metabolic profile of both BALB/c mice and BALB/c nude mice was significantly changed after injecting SMI, and the notable increase in lysolecithin level might have a greater association with the immediate ADRs induced by SMI. The Spearman correlation analysis revealed that LysoPC (18:3(6Z,9Z,12Z)/0:0) showed a significant positive correlation with cytokines. After injecting SMI, the levels of RhoA/ROCK signaling pathway-related protein increased significantly in BALB/c mice. Protein-protein interaction (PPI) showed that the increased lysolecithin levels might be related to the activation of the RhoA/ROCK signaling pathway.DiscussionTogether, the results of our study revealed that the immediate ADRs induced by SMI were mediated by thymus-derived T cells, and elucidated the mechanisms of such ADRs. This study provided new insights into the underlying mechanism of immediate ADRs induced by SMI.

Introduction: The accurate diagnosis of pneumatosis intestinalis (PI) is increasing despite patients’ limited identification of etiologic factors. Recently a patient with lung squamous carcinoma who developed pneumatosis intestinalis following methylprednisolone administration for immune-related adverse events was treated at our hospital. Subsequent a literature review and an analysis of the FDA Adverse Event Reporting System (FAERS) database enabled the identification of additional cases of pneumatosis intestinalis.Methods: A literature review of the MEDLINE/PubMed and Web of Science Core Collection databases using standard pneumatosis intestinalis search terms to identify published cases of immune checkpoint inhibitors (ICIs) or steroids causing pneumatosis intestinalis were performed. A separate retrospective pharmacovigilance study of FAERS enabled the extraction of unpublished cases of pneumatosis intestinalis between the first quarter of 2005 and the third quarter of 2022. Disproportionality and Bayesian analyses were performed to identify signal detection in reported odds ratios, proportional reporting ratios, information components, and empirical Bayesian geometric means.Results: Ten case reports of steroid-related pneumatosis intestinalis were retrieved from six published studies. The implicated drug therapies included pre-treatment with steroids before chemotherapy, combination therapy with cytotoxic agents and steroids, and monotherapy with steroids. In the FAERS pharmacovigilance study, 1,272 cases of immune checkpoint inhibitors or steroid-related pneumatosis intestinalis were incidentally reported. The signal detected in five kinds of immune checkpoint inhibitors and six kinds of steroids implied a positive correlation between the drugs and adverse events.Conclusion: Steroids might be the etiologic factors in the current case of pneumatosis intestinalis. Reports supporting the role of steroids in suspected cases of pneumatosis intestinalis can be found in literature databases and the FAERS database. Even so, as documented in FAERS, immune checkpoint inhibitors-induced pneumatosis intestinalis should not be excluded.

"Dogbone" deformation that the diameters of two ends are larger than the middle diameter of the stent under the effect of the balloon expanding, is one of the important standards to evaluate the mechanical properties of vascular stents. It is a huge challenge to simulate and evaluate the "Dogbone" behaviors of braided stents in the curved vessels. In this study, the key work was to investigate the "Dogbone" deformations of braided stents in the curved vessels by designing main parameters including strut diameter, braiding angle, and the circumferential number of unit cell. Based on the "Dogbone" stents in the curved vessels, the impact of "Dogbone" on the fatigue properties of braided stents was analyzed under the pulsatile effect of vessels. The influence of "Dogbone" stents on stress distribution of vascular walls was studied. To evaluate the "Dogbone" behaviors of stents in the curved vessels, the calculation method of "Dogbone" was improved by calculating the centerline and the bus bar of the curved vessels. Braided stents with various parameters (strut diameter t = 100,125 and 152 μm, braiding angle α = 30, 40 and 50°, the circumferential number of unit cell N = 8, 10, and 12) were designed respectively. Numerical simulation method was used to mimic the "Dogbone" deformation after stent expansion. The results showed that strut diameter and braiding angle had more influence on "Dogbone" deformations than the circumferential number of unit cell. "Dogbone" deformation could adversely affect fatigue performance and vascular walls.

For the sake of conducting distribution network reliability prediction in an accurate and efficient manner, a model for distribution network reliability prediction (IAO-LSSVM) based on an improved Aquila Optimizer (IAO) optimized mixed-kernel Least Squares Support Vector Machine (LSSVM) is thus proposed in this paper. First, the influencing factors that greatly affect the distribution network reliability are screened out through grey relational analysis. Afterwards, the radial basis kernel function and polynomial kernel function are combined and a mixed kernel LSSVM model is constructed, which has better generalization ability. However, for the AO algorithm, it is easy to fall into local extremum. In such case, the AO algorithm is innovatively improved after both the improved tent chaotic initialization strategy and adaptive t-distribution strategy are introduced. Next, the parameters of the mixed-kernel LSSVM model are optimized and the IAO-LSSVM distribution network reliability prediction model is established through using the improved AO algorithm. In the end, the prediction results and errors of the IAO-LSSVM prediction model and other models are compared in the actual distribution network applications. It is revealed that the IAO-LSSVM prediction model proposed in this paper features higher accuracy and better stability.

Data on the impact of morbid obesity (body mass index [BMI] ≥ 40 kg/m 2 ) on the pharmacokinetics (PK), pharmacodynamics (PD) of direct oral anticoagulants (DOACs) are relatively limited, making it difficult to design optimal dosing regimens in morbidly obese patients. To review literature on PK/PD profile, efficacy, and safety of DOACs in venous thromboembolism (VTE) and nonvalvular atrial fibrillation (AF) patients with morbid obesity and make recommendations regarding optimal dosing regimens in these patient populations. A detailed literature search was conducted (from inception to June 22, 2022) for relevant articles involving PK/PD and clinical data on DOACs use in morbidly obese patients with VTE or AF, or healthy volunteers. A total of 28 studies were identified. DOAC-specific PK variations and clinical outcomes have been observed. Obesity may have a modest effect on PK/PD of dabigatran, apixaban, or rivaroxaban. Dabigatran was effective in AF patients with morbid obesity but might increase the risk of gastrointestinal bleeding. Standard dosing of apixaban or rivaroxaban is effective and safe for VTE and AF patients with morbid obesity. Trough edoxaban concentration and anti-Xa activity were similar in different BMI groups (18.5 to >40 kg/m 2 ), and standard dosing of edoxaban may be effective and safe for AF patients. Current evidence suggests dabigatran should be used with caution in patients with AF as it might increase the risk of gastrointestinal bleeding; Standard dosing of apixaban or rivaroxaban can be used in VTE or AF patients; Standard dosing of edoxaban may be considered in AF patients.

The current forensic research on heaps mainly extracts information from the heap of Linux and the NT heap of Windows. However, the study of how to extract the information on the segment heap in the Windows 10 from dump files is not sufficient. To reproduce the internal information on the segment heap, this paper proposes a method for locating and extracting the internal information on the segment heap in the Windows 10 according to the field offset in the vtype description information of memory object. The method uses the pool scanning technology to locate the process object, obtains the starting position of the process heap and scans the process heap according to the structural information on the process object and the process environment block object. Then it locates the position of the segment heap with its feature values, thereby extracting its internal information. Based on the analysis results, five forensic plugins for extracting the information on the segment heap were developed on the Volatility framework. The experimental results show that this method can effectively extract the information on the address of each segment heap and its internal components in the memory and on the size of committed memory, etc. The information can help investigators to analyze the digital traces left in the memory by cyber criminals or cyber attackers.

Gastric cancer (GC) is a common lethal malignancy worldwide. Gastroscopy is an effective screening technique for decreasing mortality. However, there are still limited useful non-invasive markers for early detection of GC. Bile acids are important molecules for the modulation of energy metabolism. With an in-depth targeted method for accurate quantitation of 80 bile acids (BAs), we aimed to find potential biomarkers for the early screening of GC. A cohort with 280 participants was enrolled, including 113 GC, 22 benign gastric lesions (BGL) and 145 healthy controls. Potential markers were identified using a random forest machine algorithm in the discovery cohort (n=180), then validated in an internal validation cohort (n=78) and a group with 22 BGL. The results represented significant alterations in the circulating BA pool between GC and the controls. BAs also exhibited significant correlations with various clinical traits. Then, we developed a diagnostic panel that comprised six BAs or ratios for GC detection. The panel showed high accuracy for the diagnosis of GC with AUC of 1 (95%CI: 1.00-1.00) and 0.98 (95%CI: 0.93-1.00) in the discovery and validation cohort, respectively. This 6-BAs panel was also able to identify early GC with AUC of 1 (95%CI: 0.999-1.00) and 0.94 (95%CI: 0.83-1.00) in the discovery and validation cohort, respectively. Meanwhile, this panel achieved a good differential diagnosis between GC and BGL and the AUC was 0.873 (95%CI: 0.812-0.934). The alternations of serum bile acids are characteristic metabolic features of GC. Bile acids could be promising biomarkers for the early diagnosis of GC.

ObjectiveTo evaluate the effect of mindfulness intervention on improving mental health among undergraduate nursing students during the coronavirus disease 2019 (COVID-19) pandemic in China.MethodsAn online mindfulness intervention course named Mindfulness Living With Challenge (MLWC) was developed by the research team, and a randomized controlled trial using MLWC among Chinese undergraduate nursing students was carried out. A total of 120 undergraduate nursing students were randomized into control (60 students) and intervention groups (60 students) via a WeChat mini program. Self-administered questionnaire surveys were conducted at pre- and post-intervention, measuring depression, anxiety, stress, mindfulness, and perceived social support. After intervention, the acceptance of the online mindfulness course among participants was assessed by employing the theory of technology acceptance model (TAM).ResultsAmong the enrolled 120 participants, 86.67% (52/60) and 93.33% (56/60) of the intervention and control groups remained completed the trial and the questionnaire surveys. Compared with the control group, the reduction of anxiety and stress symptoms, as well as the improvement of mindfulness level and perceived social support in the MLWC intervention group were statistically significant (P < 0.05), while the change in depression was not statistically significant. The scores of the four dimensions in TAM ranged from (5.88 ± 0.94) to (5.91 ± 0.97).ConclusionOnline mindfulness intervention implemented in this study is effective in improving mental health among undergraduate nursing students, and they were interested in this intervention.Clinical trial registration[www.ClinicalTrials.gov], identifier [ChiCTR2 200058103].

Drug hypersensitivity reactions induced by small molecule drugs encompass a broad spectrum of adverse drug reactions with heterogeneous clinical presentations and mechanisms. These reactions are classified into allergic drug hypersensitivity reactions and non-allergic drug hypersensitivity reactions. At present, the hapten theory, pharmacological interaction with immune receptors (p-i) concept, altered peptide repertoire model, and altered T-cell receptor (TCR) repertoire model have been proposed to explain how small molecule drugs or their metabolites induce allergic drug hypersensitivity reactions. Meanwhile, direct activation of mast cells, provoking the complement system, stimulating or inhibiting inflammatory reaction-related enzymes, accumulating bradykinin, and/or triggering vascular hyperpermeability are considered as the main factors causing non-allergic drug hypersensitivity reactions. To date, many investigations have been performed to explore the underlying mechanisms involved in drug hypersensitivity reactions and to search for predictive and preventive methods in both clinical and non-clinical trials. However, validated methods for predicting and diagnosing hypersensitivity reactions to small molecule drugs and deeper insight into the relevant underlying mechanisms are still limited.

Pulmonary function testing (PFT) has important clinical value for the early detection of lung diseases, assessment of the disease severity, causes identification of dyspnea, and monitoring of critical patients. However, traditional PFT can only be carried out in a hospital environment, and it is challenging to meet the needs for daily and frequent evaluation of chronic respiratory diseases. In this study, we propose a novel method for accurately assessing pulmonary function by analyzing recorded forced cough sounds by mobile device without time and location restrictions. In the experiment, 309 clips of cough sound segments were separated from 133 patients who underwent PFT by using Audacity software. There are 247 clips of training samples and 62 clips of testing samples. Totally 52 features were extracted from the dataset, and principal component analysis (PCA) was used for feature reduction. Combined with biological attributes, the normalized features were regressed by using machine learning models with pulmonary function parameters (i.e., FEV1, FVC, FEV1/FVC, FEV1%, and FVC%). And a 5-fold cross-validation was applied to evaluate the performance of the regression models. As described in the experimental result, the result of coefficient of determination (R2) indicates that the support vector regression (SVR) model performed best in assessing FVC (0.84), FEV1% (0.61), and FVC% (0.62) among these models. The gradient boosting regression (GBR) model performs best in evaluating FEV1 (0.86) and FEV1/FVC (0.54). The result confirmed that the proposed method was capable of accurately assessing pulmonary function with forced cough sound. Besides, the cough sound sampling by a smartphone made it possible to conduct sampling and assess pulmonary function frequently in the home environment.

Quantitative evaluation of the relationship between land use/land cover (LULC) change and ecosystem service values (ESVs) is of great significance for the scientific optimization of land use structure and the formulation of sustainable land use policy, However, the responses of ESVs to LULC changes in fragile ecosystem regions over long time scales and large spatial scopes in the context of ecological restoration measures have seldom been studied. In this study, the Yungui Plateau in China was used as the study region. Based on annual Moderate–Resolution Imaging Spectroradiometer (MODIS) land cover datasets (MCD12Q1) from 2001 to 2020, using the equivalent coefficient value of ESVs modified for the specific situation of China, and adopting the Getis-Ord Gi* statistic method and Spearman’s rank correlation analysis, the study estimated the values of 11 types of ecosystem services, and analyzed the dynamics of LULC and its impact on ESVs from 2001 to 2020. The results showed that the LULC pattern in the Yungui Plateau changed dramatically in the past two decades, with a significant increase in forestland. The total ESVs improved remarkably from 638.10 billion yuan in 2001 to 746.99 billion yuan in 2020, and was mainly affected by forest restoration and the conversion of grassland to forestland and farmland to grassland. The distribution of ESVs shows obvious spatial and temporal heterogeneity; ESV hot spots were mainly distributed in the edge regions, while ESV cold spots were concentrated on the central and eastern of Yungui Plateau, and the decline in total ESVs still existed and displayed an increasing trend caused by farmland expansion and forestland degradation. Thus, we conclude that the LULC changes caused by ecological restoration measures have a significant positive effect on the improvement of the total ESVs in the Yungui Plateau and suggest that declines in ESVs in local areas should be of high concern in future land use planning and management.

A novel method, termed ladder-shape melting temperature isothermal amplification (LMTIA), was developed in this study. As a proof of concept, one pair of primers or two pairs of nested primers and a thermostable DNA polymerase were employed to amplify the internal transcribed spacer of Oryza sativa with the ladder-shape melting temperature curve. Our results demonstrated that the LMTIA assay with nested primers was 50-fold more sensitive than the LAMP assay with the same level of specificity. The LMTIA method has the potential to be used for the prevention and control of emerging epidemics caused by different types of pathogens.

BackgroundCurrently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine.MethodsThe integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis.ResultsIn this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that alanine, aspartate and glutamate (AAG) metabolism was significantly associated with the prevalence and progression of GC. Weighted metabolite correlation network analysis revealed that glycolysis/gluconeogenesis (GG) and AAG metabolism served as HER2-coexpressed metabolic pathways. Consensus clustering was used to stratify patients with GC into four subtypes with different metabolic characteristics (i.e. quiescent, GG, AAG and mixed subtypes). The GG subtype was characterised by a lower level of ERBB2 expression, a higher proportion of the inflammatory phenotype and the worst prognosis. However, contradictory features were found in the mixed subtype with the best prognosis. The GG and mixed subtypes were found to be highly sensitive to chemotherapy, whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.ConclusionsTranscriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC. Metabolomics analysis highlighted the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC. The novel integrated multi-omics strategy used in this study may facilitate the development of a more tailored approach to GC therapy.

Metabolites/impurities (MIs) of penicillin are normally considered to be the main substances inducing immediate hypersensitivity reactions in penicillin treatment. Our previous research found that penicillin can cause non-allergic hypersensitivity reactions (NAHRs) by directly triggering vascular hyperpermeability and exudative inflammation. However, the chief culprits and underlying mechanisms involved in penicillin-induced NAHRs have not yet been fully elucidated. In this study, we used a combination of approaches including a mouse non-allergic hypersensitivity reaction model, UPLC-MS/MS analyses of arachidonic acid metabolites (AAMs), immunoblotting technique, and molecular docking, etc to investigate the culprits involved in penicillin-induced hypersensitivity reactions. We found penilloic acid, one of the main MIs of penicillin, could trigger NAHRs via inducing increased vascular permeability, while the other MIs did no exhibit similar effect. Penilloic acid-induced reactions were not IgE-dependent. Significantly increased arachidonic acids and cascade metabolites in lungs, and activation of RhoA/ROCK signaling pathway in the ears and lungs of mice were noticed after once administration of penilloic acid. This study revealed that penilloic acid was the chief culprit involved in penicillin-induced immediate NAHRs in mice, which mainly associated with direct stimulation of vascular hyperpermeability and exudative inflammation. The activations of AAMs and RhoA/ROCK signaling pathway played important roles in these reactions.

Deep learning based change detection has brought a significant improvement in the accuracy and efficiency when compared with conventional machine learning methods. However, the issues of the lack of differential information and the diversity of the scale features of artificial objects are crucial barriers to the application of building change detection algorithms. A novel deep learning based approach named the high-resolution feature difference attention network (HDANet) is proposed in this work to solve these issues. HDANet can handle the change characteristics well, due to the Siamese network structure. To tackle the loss of the spatial features of buildings caused by the multiple successive down-sampling operations in the current change detection algorithms using fully convolutional networks (FCNs), a multi-resolution parallel structure is introduced in HDANet, and the image information with different resolutions is comprehensively employed, without any spatial information loss. Moreover, an innovative difference attention module is elaborated for the enhancement of the sensitivity to difference information, to keep the building change information. The experimental results obtained on building change detection datasets confirm that HDANet can improve the differential feature representation for change detection, and the performance of the building change detection is also superior to that of the other advanced change detection methods.

Lattice structure has excellent mechanical properties to develop lightweight devices, and additive manufacture makes it possible to construct complex lattice without extra cost. Especially, with the development of structural optimization technology, design of gradient lattice structure with optimized material distribution has become a research hotspot. In this paper, a double-sided metal mirror infilled with topology optimization (TO) based self-supporting lattice structure and fabricated with selective laser melting (SLM) was proposed. Considering the manufacturing constraints of SLM technology, commonly used body-centered cubic (BCC) lattice was modified and coordinate transformed to construct the self-supporting infill of the mirror. To further improve the mechanical properties of the structure, density-based TO method was introduced to optimize the diameters of all struts in the infill lattice. Finally, an SLM 3D printer was utilized to manufacture the mirror, and both stiffness and anti-vibration performance of the original and two different lattice infilled metal mirrors were evaluated through FEA. Compression tests were also conducted to verify the compressive property of all mirrors. Even though with 20% less material cost, both computational and experimental verifications have shown that the optimized lattice infilled mirror has much better mechanical properties compared with the original design.

Impaired intestinal barrier function and gut microbiota dysbiosis are believed to be related to exacerbation of acute pancreatitis (AP). As a bacterial cell wall peptidoglycan component, diaminopimelic acid (DAP) is a specific ligand of NOD1 that regulates the NOD1/RIP2/NF-kB signaling pathway. Here, we investigated the role of DAP in the crosstalk between the gut microbiota and pancreas during the occurrence of AP. Upregulation of NOD1/RIP2/NF-kB and elevated serum DAP levels were found in severe AP (SAP) model rats. The accumulation of DAP in SAP patients corroborated its ability to serve as an indicator of disease severity. Subsequently, SAP rats were treated with oral administration of the traditional Chinese medicine Qingyi Keli (QYKL) as well as neomycin, which can widely eliminate DAP-containing bacteria. Both QYKL and neomycin intervention ameliorated intestinal and pancreatic damage and systemic inflammation in SAP rats. Through 16S rDNA sequencing, we found that QYKL could rehabilitate the gut microbiota structure and selectively inhibit the overgrowth of enteric bacteria, such as Helicobacter and Lactobacillus, in SAP rats without affecting some protective strains, including Romboutsia and Allobaculum. Interestingly, we demonstrated that the decrease in serum DAP was accompanied by suppression of the NOD1/RIP2/NF-kB signaling pathway in both the intestine and pancreas of the two intervention groups. Taken together, these results suggested that the gut microbiota-DAP-NOD1/RIP2 signaling pathway might play a critical role in the progression of AP and that SAP could be alleviated via intervention in the signaling pathway. Our work provides new potential early warning indicators of SAP and targets for intervention.

Abstract Background COVID-19 can lead to increased psychological symptoms such as post-traumatic stress disorder (PTSD), depression, and anxiety among patients with COVID-19. Based on the previous mindfulness-based interventions proved to be effective, this protocol reports a design of a randomized controlled trial aiming to explore the efficacy and possible mechanism of a mindful living with challenge (MLWC) intervention developed for COVID-19 survivors in alleviating their psychological problems caused by both the disease and the pandemic. Methods In April 2021, more than 1600 eligible participants from Hubei Province of China will be assigned 1:1 to an online MLWC intervention group or a waitlist control group. All participants will be asked to complete online questionnaires at baseline, post-program, and 3-month follow-up. The differences of mental health status (e.g. PTSD) and physical symptoms including fatigue and sleeplessness between the COVID-19 survivors who receiving the online MLWC intervention and the control group will be assessed. In addition, the possible mediators and moderators of the link between the MLWC intervention and target outcomes will be evaluated by related verified scales, such as the Five Facets Mindfulness Questionnaire. Data will be analyzed based on an intention-to-treat approach, and SPSS software will be used to perform statistical analysis. Discussion The efficacy and potential mechanism of MLWC intervention in improving the quality of life and psychological status of COVID-19 survivors in China are expected to be reported. Findings from this study will shed light on a novel and feasible model in improving the psychological well-being of people during such public health emergencies. Trial registration Chinese Clinical Trial Registry (ChiCTR), ChiCTR2000037524; Registered on August 29, 2020, http://www.chictr.org.cn/showproj.aspx?proj=60034 .

To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm.

When solving the water infiltration problem in unsaturated soils, the hydraulic function is a function of water content or suction, which makes the equation governed by hydraulic function exhibits strong nonlinear characteristics resulting in the difficulty of solving the horizontal infiltration problem. Based on the assumption that the water flow in soil medium follows the path of time - consuming extreme value, a time functional was introduced, and the horizontal infiltration problem was transformed into a functional extreme value problem based on a variational principle. By solving Euler-Lagrange equation and combining with the boundary conditions, the explicit analytical solution of the nonlinear transient horizontal infiltration problem was obtained. Combined with the Brooks-Corey hydraulic function, the distribution of volume water content of this type of soils in the unsaturated state was explicitly solved. By calculating the water horizontal infiltration laws of four different types of soil samples through theoretical and numerical methods, the results obtained by the solution matched well with the existing results and numerical results, verifying the effectiveness of the method. The results show that the distribution of volume water content has a power function against location distance and wetting front distance ratio, and the power exponent depends on the shape parameter of soil water characteristic curve. Initial conditions and boundary conditions had different effects on the distribution of volumetric water content.