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Mucosal vaccines offer potential benefits over parenteral vaccines for they can trigger both systemic immune protection and immune responses at the predominant sites of pathogen infection. However, the defense function of mucosal barrier remains a challenge for vaccines to overcome. Here, we show that surface modification of exosomes with the fragment crystallizable (Fc) part from IgG can deliver the receptor-binding domain (RBD) of SARS-CoV-2 to cross mucosal epithelial layer and permeate into peripheral lung through neonatal Fc receptor (FcRn) mediated transcytosis. The exosomes F-L-R-Exo are generated by genetically engineered dendritic cells, in which a fusion protein Fc-Lamp2b-RBD is expressed and anchored on the membrane. After intratracheally administration, F-L-R-Exo is able to induce a high level of RBD-specific IgG and IgA antibodies in the animals’ lungs. Furthermore, potent Th1 immune-biased T cell responses were also observed in both systemic and mucosal immune responses. F-L-R-Exo can protect the mice from SARS-CoV-2 pseudovirus infection after a challenge. These findings hold great promise for the development of a novel respiratory mucosal vaccine approach.

Summary: Oncogenic mutations (such as in KRAS) can dysregulate transcription and replication, leading to transcription-replication conflicts (TRCs). Here, we demonstrate that TRCs are enriched in human pancreatic ductal adenocarcinoma (PDAC) compared to other common solid tumors or normal cells. Several orthogonal approaches demonstrated that TRCs are oncogene dependent. A small interfering RNA (siRNA) screen identified several factors in the base-excision repair (BER) pathway as main regulators of TRCs in PDAC cells. Inhibitors of BER pathway (methoxyamine and CRT) enhanced TRCs. Mechanistically, BER pathway inhibition severely altered RNA polymerase II (RNAPII) and R-loop dynamics at nascent DNA, causing RNAPII trapping and contributing to enhanced TRCs. The ensuing DNA damage activated the ATR-Chk1 pathway. Co-treatment with ATR inhibitor (VX970) and BER inhibitor (methoxyamine) at clinically relevant doses synergistically enhanced DNA damage and reduced cell proliferation in PDAC cells. The study provides mechanistic insights into the regulation of TRCs in PDAC by the BER pathway, which has biologic and therapeutic implications.

Vaccine development requires high-resolution, in situ, and visual adjuvant technology. To address this need, this work proposed a novel adjuvant labeling that involved indocyanine green (ICG) and bovine serum albumin (BSA) with self-assembled aluminium adjuvant (Alum), which was called BSA@ICG@Alum. This compound exhibited excellent photoacoustic properties and has been confirmed its safety, biocompatibility, high antigen binding efficiency, and superior induction of immune response. Photoacoustic tomography (PAT) tracked the distribution of Alum in lymph nodes (LNs) and lymphatic vessels in real time after diverse injection modalities. The non-invasive imaging approach revealed that BSA@ICG@Alum was transported to the draining LNs 60 min after intramuscular injection and to distal LNs within 30 min after lymph node injection. In conclusion, PAT enabled real-time three-dimensional and quantitative visualization, thus offering a powerful tool for advancing vaccine design by providing critical insights into adjuvant transport and immune system activation.

Abstract Objective To investigate the correlation between MRI findings and histological features for preoperative prediction of histological grading and Ki-67 expression level in alveolar soft part sarcoma (ASPS). Methods A retrospective analysis was conducted on 63 ASPS patients (Jan 2017–May 2023). All patients underwent 3.0-T MRI examinations, including conventional sequences, dynamic contrast-enhanced scans with time-intensity curve analysis, and diffusion-weighted imaging with apparent diffusion coefficient (ADC) measurements. Patients were divided into low-grade (histological Grade I) and high-grade (histological Grade II/III) groups based on pathology. Immunohistochemistry was used to assess Ki-67 expression levels in ASPS. Statistical analysis included chi-square tests, Wilcoxon rank-sum test, binary logistic regression analysis, Spearman correlation analysis, and receiver operating characteristic curve analysis of various observational data. Results There were 29 low-grade and 34 high-grade patients (26 males and 37 females) and a wide age range (5–68 years). Distant metastasis, tumor enhancement characteristics, and ADC values were independent predictors of high-grade ASPS. High-grade ASPS had lower ADC values (p = 0.002), with an area under the curve (AUC), sensitivity, and specificity of 0.723, 79.4%, and 58.6%, respectively, for high-grade prediction. There was a negative correlation between ADC values and Ki-67 expression (r = −0.526; p

Building a high-quality big data resource platform for the government is an important basic project to realize the integration of government information across departments, regions and levels, accelerate the construction of digital government and improve the digital intelligence level of public services and social governance.Firstly, this paper reviews the development of China's E-government and summarizes the three construction mode and main problems of traditional big data platforms, i.e., low data freshness, poor data consistency, difficult business collaborative management, weak basic support and high overall investment.Secondly, this paper conducts a case study on the credit information resource management platform in Jiangsu and explains the reasons for using Jiangsu case as an example.This paper proposes corresponding solutions and overall architecture design for the problems existing in traditional construction models as well as four reference value of Jiangsu case.Finally, based on summarizing the research and practical experience in Jiangsu, five suggestions are summarized to provide a reference for various provinces to study and formulate policy documents, such as provincial government big data platform construction guidelines.

Methane explosion is one of the major disasters that seriously threaten the safety of coal mine production, the development of efficient methane explosion suppression technology can effectively improve the prevention and control level of methane explosion accidents, and its focus is on the function of explosion suppression materials. In order to systematically study the effect of typical haloalkanes extinguishing agents on methane explosion, the effects of typical haloalkanes such as heptafluoropropane (C3HF7), hexafluoropropane (C3H2F6) and trifluoromethane (CHF3) on the ignition and explosion characteristics of methane were systematically studied by combining experimental tests and theoretical analysis. The effects of haloalkanes on methane explosion pressure parameters and laminar burning velocity were tested by a 20 L spherical explosive vessel and a self-developed Bunsen burner laminar flame propagation velocity system. The variation laws of peak explosion pressure, maximum pressure rise rate, laminar burning velocity, and laminar flame morphology evolution were obtained. The results show that with the increase of the added volume fraction, the haloalkanes had a double effect of promoting and inhibiting the methane explosion process. Under the chemical equivalent condition, only C3HF7 can first promote and then inhibit the peak explosion pressure and maximum pressure rise rate of methane, while CHF3 and C3H2F6 can inhibit the effect. The three haloalkanes all showed inhibition on the combustion rate of methane laminar flow. In the oxygen-poor condition, the three haloalkanes inhibited the peak explosion pressure, the maximum pressure boost rate, and the laminar burning velocity of methane. In general, C3H2F6 and C3HF7 have better inhibition effects on methane explosion pressure characteristic parameters and laminar burning velocity than CHF3. The theoretical analysis results show that the double effect of promoting and inhibiting the haloalkanes with the increase of the mixture volume fraction can be attributed to the competition between the improvement of the heat release characteristics of the system reaction and the inhibition of the key free radicals such as H, O, and OH by the main intermediates containing F. The results of this paper provide a theoretical basis for the theoretical research and technical development of methane explosion prevention and control.

In the new era, government data has undergone tremendous changes in terms of form and generation mechanism, presenting multi-modality, huge volume, complex structure, and time series evolution, which has brought new challenges to traditional governance.This paper takes provincial government big data governance as the starting point, and mainly includes three aspects: (1) analyzed the technical framework and bottlenecks of traditional provincial government big data governance; (2) based on the government decision-making scenario, proposed a decisiondriven provincial government big data governance technology architecture (called Fengxiang model), introduced its components, design principles, overall architecture, and demonstrated its feasibility; (3) aiming at the four key business issues and key technical issues in the technical framework of Fengxiang model, further proposed targeted technical implementation ideas to provide a reference for the construction of follow-up governance projects in various provinces and cities and the research and selection of related technical routes.

Abstract The spatiotemporal extension/expansion of mine areas is affected by multiple factors. So far, very little has been done to examine the interaction between mine areas and political or economic realities. The (ultra‐)mafic magmatic mines in China played a specific role in supporting national development and providing an ideal research subject for monitoring their interrelationship. In this study, remote sensing and mining‐related GIS data were used to identify and analyze 1233 (ultra‐)mafic magmatic mine area polygons in China, which covered approximately 322.96 km2 of land and included a V–Ti–Fe mine, a copper–nickel mine, a chromite mine, an asbestos mine, and a diamond mine. It was found that (1) the areal expansion of mines is significantly related to the mine types, perimeter, topography, and population density. (2) The mine area variation also reflects market and policy realities. The temporal expansion of the mine area from 2010 to 2020 followed an S‐shaped pattern (with the turning point occurring in 2014), closely related to iron overcapacity and tightened mining policies. (3) The complexity (D) of the mine area may reflect mine design and excavation practices. To be specific, lower D indicates early‐stage or artisanal/small‐scale mining, whereas higher D represents large‐scale mining. This study demonstrates that the detailed mapping of mine land can serve as an indicator to implement mining‐related market and policy changes. The (ultra‐)mafic mines area data set can be accessed at https://zenodo.org/record/7636616#.Y-p0uXaZOa0.

Variations in Marine Dissolved Oxygen Concentrations (MDOC) play a critical role in the study of marine ecosystems and global climate evolution. Although artificial intelligence methods, represented by deep learning, can enhance the precision of MDOC inversion, the uninterpretability of the operational mechanism involved in the “black-box” often make the process difficult to interpret. To address this issue, this paper proposes a high-precision interpretable framework (CDRP) for intelligent MDOC inversion, including Causal Discovery, Drift Detection, RuleFit Model, and Post Hoc Analysis. The entire process of the proposed framework is fully interpretable: (i) The causal relationships between various elements are further clarified. (ii) During the phase of concept drift analysis, the potential factors contributing to changes in marine data are extracted. (iii) The operational rules of RuleFit ensure computational transparency. (iv) Post hoc analysis provides a quantitative interpretation from both global and local perspectives. Furthermore, we have derived quantitative conclusions about the impacts of various marine elements, and our analysis maintains consistency with conclusions in marine literature on MDOC. Meanwhile, CDRP also ensures the precision of MDOC inversion: (i) PCMCI causal discovery eliminates the interference of weakly associated elements. (ii) Concept drift detection takes more representative key frames. (iii) RuleFit achieves higher precision than other models. Experiments demonstrate that CDRP has reached the optimal level in single point buoy data inversion task. Overall, CDRP can enhance the interpretability of the intelligent MDOC inversion process while ensuring high precision.

Objective To understand the current status of surgical treatment for hemophilia osteoarthropathy (HO) in China.Methods Using an online questionnaire, select domestic hospitals that partici-pated in the compilation of the 'Guideline for perioperative management of hemophilia patients undergoing orthopaedic surgery in China ', in addition to members of the National Joint Surgery Group, and the Orthopedic Branch of the Chinese Medical Association for targeted investigation and analysis.Results A total of 17 domestic hospitals were included, all of which were general hospitals. Hospitals that started HO surgery treatment before 2000 accounted for 35.29%. A total of 3057 surgical cases of HO were reported by those hospitals. The most commonly performed surgical procedures were hip and knee joint replacement. The most commonly used coagulation factor replacement regimen was recombinant coagulation factor preparation. Ten hospitals reported finding patients with transfusion-related infectious diseases. Bleeding and hematoma formation were the most frequently reported surgical complications. Excessive length of hospital stay and high economic costs were the most frequently reported problems.Conclusions Surgical treatment for HO in 17 hospitals is mainly carried out in some large comprehensive medical centers in the eastern region. Compared with the patient base, the popularity and number of surgeries are still relatively insufficient. It is necessary to further standardize the treatment system by standardizing factor replacement and strengthening rehabilitation to improve surgical treatment outcomes.

Climate change is an increasingly severe global crisis, posing threats to ecosystems, economies, and human lives. To address these threats, different cities around the world have adopted various levels of adaptation strategies to cope with varying degrees of negative impact, such as increasing water efficiency and enhancing health response systems. Despite some progress, there is a spatial discrepancy among cities in terms of their vulnerability to climate change and their adaptation readiness. Uncovering the reasons behind this spatial discrepancy could help us formulate better policies to address climate issues. To this end, we conduct an empirical analysis using city response data from 2020, sourced from the Carbon Disclosure Project (CDP), focusing on a dataset that encompasses 421 cities worldwide. Specifically, we first formulate a “vulnerability index” to measure the propensity of cities to suffer negative effects in the event of climate hazards and a “readiness index” to represent their adaptation readiness level. Then we introduce the “discrepancy score” to quantify discrepancies across cities and discover the spatial distribution of the discrepancies through spatial visualization. Further, we employ a clustering analysis method named k-means to group different cities based on vulnerability index and readiness index. Finally, we perform Geographically Weighted Regression (GWR) to quantitatively analyze the spatial correlation between the economy and the discrepancy score of different cities. Our research mainly reveals several findings: (1) European cities tend to exhibit high levels of adaptation readiness with low degrees of vulnerability. In contrast, African cities consistently display heightened degrees of vulnerability combined with limited adaptation readiness. (2) Economic factors play a significant role in the spatial discrepancies between levels of adaptation readiness and degrees of vulnerability of cities; (3) The impact of the economy on the discrepancy scores exhibits significant spatial heterogeneity, with its influence being greater in European and African regions compared to other areas. Our study contributes both theoretically and practically to the comprehension of global climate change. It provides evidence-based support for the development of more effective climate change mitigation strategies and lays the foundation for fostering worldwide collaboration and initiatives.

This study describes a method for real-time examination of the microvascular system based on the three-dimensional photoacoustic imaging system to prevent arterial complications, especially vascular embolism, during hyaluronic acid (HA) injections. Chicken embryos were used to simulate the superficial blood vessels of human skin, and then the target area was imaged by the photoacoustic imaging system for three-dimensional vascular imaging, and then the syringe and blood vessels were monitored, and the syringe angle and penetration depth were adjusted in time using an injection device to avoid puncturing the arterial vasculature and clogging the blood vessels. HA was then injected into smaller vessels on the dorsum of the tongue in mice and into thicker vessels on the dorsal portion of the tongue in rats to mimic embolization, and the post-operative recovery was reflected by the changes in the pixel dots of the extracted part of the blocked blood vessels, and it was observed that the blood flow in the area of the fine vessels was restored in about 3 days, whereas blood flow in the area of the large vessels was restored in only about 1 h. The method presented in this paper allows precise guidance of injectable filler HA, which has good application prospects in improving the safety of injection micro-plastic surgery and reducing the experience requirements for medical personnel.

Abstract Background Pituitary dysfunction (PD) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH). The prevalence of PD varies widely at a global level and no recent meta-analysis is available. Therefore, the aim of our systematic review and meta-analysis was to summarize the updated estimates of worldwide prevalence of PD after aSAH. Methods Scopus, Embase, Web of Science, and PubMed databases were used to comprehensively search the appropriate literature and a random-effects meta-analysis on the results of the available studies was performed. The heterogeneity in the prevalence estimates was evaluated by subgroup analysis in terms of types of PD, and acute and chronic phases of aSAH. The onset of PD within 6 months after aSAH was considered as acute, while that after 6 months was considered as chronic. Results Twenty-seven studies with 1848 patients were included in this analysis. The pooled prevalence of PD in the acute phase was 49.6% (95% CI, 32.4-66.8%), and 30.4% (95% CI, 21.4-39.4%) in the chronic phase. Among the hormonal deficiencies, growth hormone dysfunction was the most prevalent in the acute phase, being 36.0% (95% CI, 21.0-51.0%), while hypoadrenalism was the most prevalent in the chronic phase, being 21.0% (95% CI, 12.0-29.0%). Among the six World Health Organization regions, the South-East Asia Region has the highest prevalence of PD in the acute phase (81.0%, 95%CI, 77.0-86.0%, P

This study utilized an infrared spotlight Hyperspectral infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) mounted on FY3D cloud products from the National Satellite Meteorological Center of China to obtain methane profile information. Methane inversion channels near 7.7 μm were selected based on the different distribution of methane weighting functions across different seasons and latitudes, and the selected retrieval channels had a great sensitivity to methane but not to other parameters. The optimization method was employed to retrieve methane profiles using these channels. The ozone profiles, temperature, and water vapor of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis data (ERA5) were applied to the retrieval process. After validating the methane profile concentrations retrieved by HIRAS, the following conclusions were drawn: (1) compared with Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flight data, the average correlation coefficient, relative difference, and root mean square error were 0.73, 0.0491, and 18.9 ppbv, respectively, with lower relative differences and root mean square errors in low-latitude regions than in mid-latitude regions. (2) The methane profiles retrieved from May 2019 to September 2021 showed an average error within 60 ppbv compared with the Fourier transform infrared spectrometer (FTIR) station observations of the Infrared Working Group (IRWG) of the Network for the Detection of Atmospheric Composition Change (NDACC). The errors between the a priori and retrieved values, as well as between the retrieved and smoothed values, were larger by around 400–500 hPa. Apart from Toronto and Alzomoni, which had larger peak values in autumn and spring respectively, the mean column averaging kernels typically has a larger peak in summer.

Accurate forecasting of ocean surface currents is crucial for the planning of marine activities, including fisheries, shipping, and pollution control. Previous studies have often neglected the consideration of spatiotemporal correlations and interdependencies among ocean elements, leading to suboptimal accuracy in medium to long-term forecasts, especially in regions characterized by intricate ocean currents. This paper proposes an adaptive spatiotemporal and multi-element fusion network for ocean surface currents forecasting (ASTMEN). Specifically, we use an improved Swin Transformer (Swin-T) to perform self-attention computation at any given moment, enabling the adaptive generation of multi-element time series with spatial dependencies. Then, we utilize a Long Short-Term Memory network (LSTM) to encode and decode these series in the dimensions of temporal and multi-element features, resulting in accurate forecasts of ocean surface currents. This study takes the Kuroshio region in the northwest Pacific Ocean as the study area with data from the ocean reanalysis dataset. The experimental results show that ASTMEN significantly outperforms the baseline model and the climate state method, and is the only model whose correlation coefficient is still higher than 0.8 at day 12. In the experiments during the summer, when the currents are most variable, ASTMEN provides better forecasts at the sea-land interface and at the junction of different currents, which has the potential to fill the gap of poor forecast performance of previous methods for complex current fields.

BackgroundThe aim of this study was to analyze the impact of the number and extranodal extension (ENE) of positive parotid lymph nodes (LNs) on distant metastasis in parotid cancer.MethodsPatients with surgically treated parotid cancer were retrospectively enrolled. The hazard ratios (HRs) of the number and ENE of positive parotid LNs on distant metastasis-free survival (DMFS) were evaluated.ResultsIn the Cox model, the groups with zero and one positive LN had comparable 10-year DMFS, but those with two positive LNs had an HR of 2.11 (95% CI: 1.36–5.29), and those with three or more positive LNs had an HR of 3.31 (95% CI: 2.05–8.43). The presence of ENE in parotid LNs did not impact the DMFS (p = 0.462; HR: 2.17; 95% CI: 0.84–6.17).ConclusionParotid LN metastasis was associated with decreased DMFS; this effect was mainly driven by the number of positive LNs rather than ENE.

Abstract Background Previous literature documents the direct and indirect economic costs of obesity, yet none has attempted to quantify the intangible costs of obesity. This study focuses on quantifying the intangible costs of one unit body mass index (BMI) increase and being overweight and obese in Germany. Methods By applying a life satisfaction-based compensation value analysis to 2002–2018 German Socio-Economic Panel Survey data for adults aged 18–65, the intangible costs of overweight and obesity are estimated. We apply individual income as a reference for estimating the value of the loss of subjective well-being due to overweight and obesity. Results The intangible costs of overweight and obesity in 2018 amount to 42,450 and 13,853 euros, respectively. A one unit increase in BMI induced a 2553 euros annual well-being loss in the overweight and obese relative to those of normal weight. When extrapolated to the entire country, this figure represents approximately 4.3 billion euros, an intangible cost of obesity similar in magnitude to the direct and indirect costs documented in other studies for Germany. These losses, our analysis reveals, have remained remarkably stable since 2002. Conclusions Our results underscore how existing research into obesity’s economic toll may underestimate its true costs, and they strongly imply that if obesity interventions took the intangible costs of obesity into account, the economic benefits would be considerably larger.

Abstract Esophageal squamous cell carcinoma (ESCC) is characterized by high morbidity and mortality. Circular RNAs (circRNAs) play an important role in tumor progression. We discovered an aberrantly expressed circRNA (hsa_circ_0021727) in patients with ESCC. However, the mechanism of action of hsa_circ_0021727 in tumors is unclear. The present study aimed to investigate the biological role of hsa_circ_0021727 and its mechanism in ESCC progression. We screened for the expression of hsa_circ_0021727 in ESCC patients. Patients with ESCC with high expression of hsa_circ_0021727 had shorter survival than those with low expression. Hsa_circ_0021727 promoted the proliferation, invasion, and migration of ESCC cells. However, miR-23b-5p inhibited this ability of hsa_circ_0021727. MiR-23b-5p acts by targeting TAK1-binding protein 1 (TAB1). Upregulation of TAB1 can activate the nuclear factor kappa B (NFκB) pathway. Hsa_circ_0021727 promoted ESCC progression by activating TAB1/NFκB pathway by sponging miR-23b-5p. In addition, in vivo experiments also confirmed that hsa_circ_0021727 could promote the proliferation, invasion, and migration of ESCC cells. In short, hsa_circ_0021727 promotes ESCC progression by targeting miR-23b-5p to activate the TAB1/NFκB pathway. These findings might provide potential targets to treat ESCC.

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications.

This paper discusses the blended teaching practice of foreign language teaching and intercultural education in the English newspaper course deploying the reading and writing integrated teaching model. It firstly explores the relationship between intercultural education and reading teaching of English news in Chinese higher education, then expounds the teaching objectives and curriculum design of this course, and also discusses the construction and application of the blended teaching in the context of new media. The study conducts teaching experiments in two classes (control class and experimental class) of English majors in a university in China for one semester (16 weeks), and utilizes questionnaires to gain insights into students’ perspectives on this course. The results show that the reading and writing integrated teaching model in the blended teaching class can better raise students’ learning interest, develop English comprehensive competence, enhance information literacy, promote critical thinking ability and improve the practical competence of intercultural communication.

The aim is to explore the predictive value of salivary bacteria for the presence of esophageal squamous cell carcinoma (ESCC). Saliva samples were obtained from 178 patients with ESCC and 101 healthy controls, and allocated to screening and verification cohorts, respectively. In the screening phase, after saliva DNA was extracted, 16S rRNA V4 regions of salivary bacteria were amplified by polymerase chain reaction (PCR) with high-throughput sequencing. Highly expressed target bacteria were screened by Operational Taxonomic Units clustering, species annotation and microbial diversity assessment. In the verification phase, the expression levels of target bacteria identified in the screening phase were verified by absolute quantitative PCR (Q-PCR). Receiver operating characteristic (ROC) curves were plotted to investigate the predictive value of target salivary bacteria. LEfSe analysis revealed higher proportions of Fusobacterium, Streptococcus and Porphyromonas, and Q-PCR assay showed significantly higher numbers of Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in patients with ESCC, when compared with healthy controls (all P

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a significant impact on global health. To address the urgent need for plasmids containing SARS-CoV-2 sequences in research, we have developed a high-throughput FastCloning platform for the construction of associated plasmids. Our platform uses a FastCloning method to construct a plasmid library from 29 ORFs of the virus and 20 commonly used vectors in the lab. The library contains 536 recombinant vectors, with a highly positive clone success rate of 92.4%. Our study provides a rapid and efficient approach to constructing a large plasmid library for SARS-CoV-2 research.

In this paper, a new type of neutron shielding material, boron particles reinforced Al composites (Bp/Al), was prepared by vacuum hot pressing method. Effect of volume fraction of boron particles and heat treatment on the microstructure and mechanical properties of the composites was studied. The results show that boron particles distribute uniformly in the composites and possess a good interface bonding with the matrix. Meanwhile, some boron particles react with Al matrix to form product AlB2 in the interfacial area. The bending strength of the composites first increases and then decreases as the volume fraction of boron particles increasing, and it also increases significantly after T6 treatment. The highest bending strength, 398 MPa, is obtained as the boron content is 30 vol%. Superabundant boron particles result in more micro-pores and micro-cracks through particle agglomeration and interfacial reaction. Therefore, the Bp/Al composites show brittle fracture when bearing excess external load.

Accurate and reliable wave significant wave height(SWH) prediction is an important task for marine and engineering applications. This study aims to develop a new deep learning algorithm to accurately predict the SWH of deep and distant ocean. In this study, we combine two methods, Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM), to construct an EEMD-LSTM model, and explore the optimal parameters of the model through experiments. A total of 5328 hours of SWH data from November 30, 2020, to July 9, 2021, are used to train and test the model to predict the SWH for the future 1h, 3h, 6h, 12h, and 18h. The results show that the EEMD-LSTM model has the best results compared with other comparative models for short-term and medium- and long-term predictions. The RMSEs are 0.0204, 0.0279, 0.0452, 0.0941, and 0.1949 for the SWH prediction in the future 1, 3, 6, 12, and 18 h. It can be used as a rapid SWH prediction system to ensure navigation safety to a certain extent, which has great practical significance and application value.

Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector.

Simulations of 108 emission reduction scenarios for NOx and VOCs using Comprehensive Air Quality Model with Extensions (CAMx) were conducted for eight cities in the Chengdu metropolitan area (CMA). The isopleth diagrams were drawn to explore the responses and differences of ozone (O3) concentrations to NOx and VOCs emission changes under Chengdu, CMA and Sichuan Province emission reduction scenarios. The results show that the O3-sensitive regimes of eight cities may change under different emission reduction scenarios. Under Chengdu emission reduction scenario, the Chengdu city is in the transition regime and O3 formation will shift from transition to VOC-limited when the VOCs emissions decreased by 50%, and the decreases in O3 concentrations caused by VOCs emission reductions are small. For the CMA and Sichuan Province emission reduction scenarios, all cities are NOx-limited in the baseline cases and with at least a 66% and a 77% reduction in NOx emissions, respectively, the daily maximum 8-h average O3 (MDA8) can attain the O3 standard (160 μg m−3). Although reductions in VOCs emissions can also lessen the O3 concentration, the effectiveness is relatively small. The changes in O3 concentrations under different VOCs to NOx emission reduction ratios indicate that all cities achieve a relatively high O3 concentration decrement with low VOCs to NOx emission reduction ratios and that the decreasing O3 concentrations caused by non-local emission reductions are much higher than those achieved by local emission reductions. In addition, the decreases in O3 concentrations in Chengdu are quite close when the total NOx and VOCs emissions reduction percentages are less than 30% under the CMA and Sichuan emission reduction scenarios.

Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. The model learns the intrinsic correlations of the data generated by the numerical model, making it possible to combine the correlations between wind and wind waves to improve the predictions. In addition, this study also optimizes the long-term prediction ability of the model through the proposed Mask method and Replace mechanism. The experimental results show that the introduction of the wind field can significantly improve the significant wave height prediction results. The research on the prediction effect of the entire study area and two separate stations shows that the prediction performance of the proposed model is better than the existing methods. The model makes full use of the physical correlation between wind and wind waves, and the validity is up to 24 hours. The 24-hour forecast R² reached 0.69.

Bio-inspired (biomimetic) materials, which are inspired by living organisms, offer exciting opportunities for the development of advanced functionalities. Among them, bio-inspired superhydrophobic surfaces have attracted considerable interest due to their potential applications in self-cleaning surfaces and reducing fluid resistance. Although the mechanism of superhydrophobicity is understood to be the free energy barrier between the Cassie and Wenzel states, the solid-surface technology to control the free energy barrier is still unclear. Therefore, previous studies have fabricated solid surfaces with desired properties through trial and error by measuring contact angles. In contrast, our study directly evaluates the free energy barrier using molecular simulations and attempts to relate it to solid-surface parameters. Through a series of simulations, we explore the behavior of water droplets on surfaces with varying values of surface pillar spacing and surface pillar height. The results show that the free energy barrier increases significantly as the pillar spacing decreases and/or as the pillar height increases. Our study goes beyond traditional approaches by exploring the relationship between free energy barriers, surface parameters, and hydrophobicity, providing a more direct and quantified method to evaluate surface hydrophobicity. This knowledge contributes significantly to material design by providing valuable insights into the relationship between surface parameters, free energy barriers, and hydrophilicity/hydrophobicity.

Abstract Whether microRNAs (miRNAs) from plasma exosomes might be dysregulated in patients with depression, especially treatment-resistant depression (TRD), remains unclear, based on study of which novel biomarkers and therapeutic targets could be discovered. To this end, a small sample study was performed by isolation of plasma exosomes from patients with TRD diagnosed by Hamilton scale. In this study, 4 peripheral plasma samples from patients with TRD and 4 healthy controls were collected for extraction of plasma exosomes. Exosomal miRNAs were analyzed by miRNA sequencing, followed by image collection, expression difference analysis, target gene GO enrichment analysis, and KEGG pathway enrichment analysis. Compared with the healthy controls, 2 miRNAs in the plasma exosomes of patients with TRD showed significant differences in expression, among which has-miR-335-5p were significantly upregulated and has-miR-1292-3p were significantly downregulated. Go and KEGG analysis showed that dysregulated miRNAs affect postsynaptic density and axonogenesis as well as the signaling pathway of axon formation and cell growths. The identification of these miRNAs and their target genes may provide novel biomarkers for improving diagnosis accuracy and treatment effectiveness of TRD.

Introduction. Intraosseous epidermal inclusion cysts (IEpC) are benign bone lesions lined with squamous epithelium. Finger phalanges are the second most common site of predilection after the skull. Case report. We presented a case of a typical IEpC at the distal phalanx of the right ring finger following a remote history of a crush injury to the finger (40 years earlier). The patient experienced painful enlargement and progressive swelling of that finger during the previous month. On physical examination, the finger showed typical “clubbing” with local tenderness. X-ray showed bone destruction, and magnetic resonance imaging (MRI) revealed abnormal signals in bone tissue in the distal phalanx of the right ring finger. The patient underwent distal phalanx amputation of the right ring finger. The diagnosis of IEpC was histopathologically confirmed. At follow-up 2 years later, the stump healed well and without recurrence. Conclusion. IEpC with a history of up to 40 years is very rare, and although the patient presents with a typical “clubbing” finger, the diagnosis was eventually con-firmed by surgery and pathology.

ObjectivesTo determine the predictor for occult neck metastases and the role of elective neck dissection (END) in cT3-4N0 parotid adenoid cystic carcinoma (ACC).MethodsPatients with surgically treated parotid ACC were retrospectively enrolled. Predictors of occult neck metastases and the effect of END on disease specific survival (DSS), overall survival (OS), locoregional control survival (LRC), and distant metastasis free survival (DMS) were analyzed.ResultsOccult neck metastases occurred in 35 (19.7%) of the 178 patients undergoing an END. The tumor stage [p=0.011, 4.215 (1.387–10.435)] and intra-parotid lymph node metastasis [p=0.032, 3.671 (1.693–8.775)] were related to the possibility of occult neck metastases independently. The END group had better 10-year LRC than the observation group (56% vs. 43%, p=0.002) and also better 10-year DMS than the observation group (43% vs. 32%, p

The incidence of esophageal cancer has obvious genetic susceptibility. Identifying esophageal cancer-related genes plays a huge role in the prevention and treatment of esophageal cancer. Through various sequencing methods, researchers have found only a small number of genes associated with esophageal cancer. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. This method fuses graph convolutional network and logical matrix factorization to effectively identify esophageal cancer-related genes through the association between genes. We call this method GCNLMF which achieved AUC as 0.927 and AUPR as 0.86. Compared with other five methods, GCNLMF performed best. We conducted a case study of the top three predicted genes. Although the association of these three genes with esophageal cancer has not been reported in the database, studies by other reseachers have shown that these three genes are significantly associated with esophageal cancer, which illustrates the accuracy of the prediction results of GCNLMF.

ObjectivesTo explore the possible risk factors of orocutaneous fistula (OCF) development in free flap reconstruction of the tongue/floor of the mouth (TFOM).MethodsData of patients who underwent free flap reconstruction of the TFOM were retrospectively analyzed. The association between clinicopathologic variables and OCF occurrence was analyzed using univariate and multivariate analyses.ResultsAltogether, 469 patients were enrolled. OCF occurred in 43 patients with a rate of 9.2%. The univariate analysis revealed the negative effects of smoking, preoperative albumin level, cachexia, T4 stage, neck dissection, entire resection of the floor of the mouth (FOM), segmental mandibulectomy, and surgical site infection on OCF occurrence. The multivariate analysis confirmed the independence of cachexia (p

By virtue of the merits of wide swath, persistent observation, and rapid operational response, geostationary remote sensing satellites (e.g., GF-4) show tremendous potential for sea target system surveillance and situational awareness. However, ships in such images appear as dim small targets and may be affected by clutter, reef islands, clouds, and other interferences, which makes the task of ship detection and tracking intractable. Considering the differences in visual saliency characteristics across multispectral bands between ships and jamming targets, a novel approach to visual detecting and association tracking of dense ships based on the GF-4 image sequences is proposed in this paper. First, candidate ship blobs are segmented in each single-spectral image of each frame through a multi-vision salient features fusion strategy, to obtain the centroid position, size, and corresponding spectral grayscale information of suspected ships. Due to the displacement of moving ships across multispectral images of each frame, multispectral association with regard to the positions of ship blobs is then performed to determine the final ship detections. Afterwards, precise position correction of detected ships is implemented for each frame in image sequences via multimodal data association between GF-4 detections and automatic identification system data. Last, an improved multiple hypotheses tracking algorithm with multispectral radiation and size characteristics is put forward to track ships across multi-frame corrected detections and estimate ships’ motion states. Experiment results demonstrate that our method can effectively detect and track ships in GF-4 remote sensing image sequences with high precision and recall rate, yielding state-of-the-art performance.

Deep neural networks are used as effective methods for the Low Probability of Intercept (LPI) radar waveform recognition. However, existing models' performance degrades seriously at low Signal-to-Noise Ratios (SNRs) because the effective features extracted by the networks are insufficient under noise jamming. In this paper, we propose a multi-resolution deep feature fusion method for LPI radar waveform recognition. First, we apply the enhanced Fourier-based Synchrosqueezing Transform (FSST), which shows good performance at low SNRs, to convert radar signals into time-frequency images. Then, we construct a multi-resolution deep convolutional network to extract more deep features from each resolution channel. Next, we explore an interactive feature fusion strategy for deep feature fusion. By some down-sampling or up-sampling blocks, different resolution features are fused to generate new features. Finally, we apply a fusion algorithm to the fully connected layer to achieve classification fusion for better performance. Simulation experiments on twelve kinds of LPI radar waveforms show that the overall recognition accuracy of our method can reach 95.2% at the SNR of -8 dB. It is proved that our approach does indeed improve the recognition accuracy effectively at low SNRs.

Automatic human gender recognition is an important and classical problem in artificial intelligence. Most of the previous gender recognition works are based on vision appearance and biometric characteristics. However, there are fewer gender recognition approaches for 3D human shapes. In this article, we propose a novel deep neural network learning method for gender recognition of 3D human shapes. Firstly, we introduce effective descriptors to distinguish male and female of 3D human shapes via probability distributions of biharmonic distances among points. Secondly, the above distances-based low-level descriptors are fed into a fully connected neural network for gender recognition. Furthermore, we construct a larger 3D human shape dataset for evaluation of the proposed gender recognition method by collecting and labeling human shape models. Compared with previous works, our method obtains higher recognition accuracy and has more advantages, such as posture invariant, robust to noises, and no need of landmarks or pre-alignment process.

Purpose. By comparing the performance of different models between artificial intelligence (AI) and doctors, we aim to evaluate and identify the optimal model for future usage of AI. Methods. A total of 500 fundus images of glaucoma and 500 fundus images of normal eyes were collected and randomly divided into five groups, with each group corresponding to one round. The AI system provided diagnostic suggestions for each image. Four doctors provided diagnoses without the assistance of the AI in the first round and with the assistance of the AI in the second and third rounds. In the fourth round, doctor B and doctor D made diagnoses with the help of the AI and the other two doctors without the help of the AI. In the last round, doctor A and doctor B made diagnoses with the help of AI and the other two doctors without the help of the AI. Results. Doctor A, doctor B, and doctor D had a higher accuracy in the diagnosis of glaucoma with the assistance of AI in the second (p=0.036, p=0.003, and p≤0.000) and the third round (p=0.021, p≤0.000, and p≤0.000) than in the first round. The accuracy of at least one doctor was higher than that of AI in the second and third rounds, in spite of no detectable significance (p=0.283, p=0.727, p=0.344, and p=0.508). The four doctors’ overall accuracy (p=0.004 and p≤0.000) and sensitivity (p=0.006 and p≤0.000) as a whole were significantly improved in the second and third rounds. Conclusions. This “Doctor + AI” model can clarify the role of doctors and AI in medical responsibility and ensure the safety of patients, and importantly, this model shows great potential and application prospects.

FastCloning, a reliable cloning technique for plasmid construction, is a widely used protocol in biomedical research laboratories. Only two-step molecular manipulations are required to add a gene (cDNA) of interest into the desired vector. However, parallel cloning of the gene into multiple vectors is still a labor-intensive operation, which requires a range of primers for different vectors in high-throughput cloning projects. The situation could even be worse if multiple fragments of DNA are required to be added into one plasmid. Here, we describe a high-throughput FastCloning (HTFC) method, a protocol for parallel cloning by adding an adaptor sequence into all vectors. The target gene and vectors were PCR amplified separately to obtain the insert product and linear vectors with 18-base overlapping at each end of the DNAs required for FastCloning. Furthermore, a method for generating polycistronic bacterial constructs based on the same strategy as that used for HTFC was developed. Thus, the HTFC technique is a simple, effective, reliable, and low-cost tool for parallel cloning.

Mixed-flow pumps with adjustable blades are widely used in municipal, agricultural, and hydropower applications. However, a limitation of adjustable blades is that the influence of the water pressure in the pump causes the rotation angle to deviate, which not only reduces the hydraulic efficiency of the pump and increases energy consumption, but it is also detrimental to the stable operation of the pump. To investigate the influence of blade rotation angle deviations (BRADs) on the hydraulic pulsation characteristics of a mixed-flow pump, in this study, a three-dimensional unsteady numerical simulation was adopted to analyze the effects of seven BRAD design schemes on the energy performance, pressure pulsation characteristics, and axial and radial forces in the impeller. When the rotation angle of a single blade deviated counterclockwise, the optimal hydraulic efficiency point of the mixed-flow pump moved toward larger flow rates, and vice versa. Unlike a situation with no BRADs, when there were BRADs, the central symmetry of the low-pressure area near the suction surface of the impeller blades was destroyed. BRADs led to increases in the pressure pulsation amplitudes at the inlet and outlet of the impeller. The dominant pressure pulsation frequencies near the shroud side at the inlet and outlet of the impeller were not affected by BRADs (both of them were equal to the blade frequency). However, the amplitude of the dominant pressure pulsation frequency at the impeller outlet and the radial force of the impeller both increased with increases in the absolute value of the deviation angle. Moreover, when the rotation angle of a single blade was only in the counterclockwise direction, the axial force of the impeller increased. This study can provide an engineering reference for the stability of mixed-flow pumps with BRADs.

This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation of this uncertainty should be provided. Previous studies construct this uncertainty through ensemble or statistical methods, neither of which can directly characterize this uncertainty and suffer from problems such as excessive computational effort. And for this reason, we propose to assess the forecast without this uncertainty through the forecast distribution. Meanwhile, none of the previous studies on TC intensity forecasting by artificial intelligence methods characterize the uncertainty, so this study is a new supplement to data-driven TC forecasting. During the 2010–2020 evaluation period, the model’s point forecast can outperform the current state-of-the-art operational statistic-dynamical model results, and can obtain forecast intervals to provide reliable probabilistic forecasts, which are critical for disaster warnings.

Pancreatic ductal adenocarcinoma (PDAC) is presently one of the cancers with the worst survival rates. The current treatment options for PDAC are relatively scarce due to insufficient understanding of molecular characteristics and subtypes of PDAC. Based on next-generation sequencing (NGS), we firstly presented a case about a KRAS wild-type pancreatic ductal adenocarcinoma patient harboring a concurrent targetable rare somatic novel KANK1-ALK, UPP2-NTRK3 fusion, and pathogenetic germline BRCA mutation. These two novel fusion statuses were assayed by immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH). Our findings demonstrated that comprehensive and systematic screening of PDAC for actionable genomic alteration may substantially improve the therapeutic prospects for a sizeable fraction of patients with PDAC. To improve the management of PDAC in an era of precision medicine, it is important to identify ALK or NTRK fusion-positive and pathogenic germline mutation subsets of patients who can benefit from targeted therapies.

Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits’ detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction.

In the present work, unsteady flow solved by the Reynolds time-averaged Navier–Stokes equation was investigated to determine the inflow distortion mechanism and the spatial distribution of hydraulic loss in a bidirectional axial flow pumping station (Case 1) based on the entropy production theory. A laboratory-scale performance experiment was also employed for the accuracy verification of the simulation approach, and an axial flow pump with pipe passages (Case 2) accompanying uniform inflow was utilized for analysis comparison. The results show that the non-uniform inflow causes a noticeable reduction in head and efficiency, as high as 27% and 21%, respectively, and the best efficiency point with uniform inflow shifts to the point with a larger flow rate. The axial velocity of the impeller inlet in Case 2 changes more smoothly along the Span compared with that in Case 1, which further indicates a more uniform inflow at the impeller inlet. The total entropy production (TEP) of each domain in Case 1 is always higher than that in Case 2, and the TEP of the whole domain in Case 1 increased by 18.68%, 30.50%, and 29.67% with flow rates of 0.8Qdes, 1.0Qdes, and 1.2Qdes, respectively, compared with that in Case 2. In the inlet passage, the larger TEPR regions in Case 1 are mainly located in the horn passage, which is far away from the inlet side, and are also distributed in the suction side of impeller blades and guide vanes. Therefore, this work may provide an optimal design reference for pumping stations in practical application.

Background: Primary trigeminal neuralgia (PTN) is known to reoccur following microvascular decompression (MVD) surgery. However, the rates and contributing factors related to PTN recurrence remain controversial. The purpose of this study was to explore the postoperative recurrence rates and related influencing factors of patients with PTN after MVD. Additionally, recurrence rates after different treatments were compared to provide guidelines for clinicians.Methods: We conducted systematic reviews and meta-analyses in accordance with the preferred reporting items of the PRISMA guidelines. We searched nine databases, namely, the PubMed, EMBASE, Cochrane Library, Web of Science, CINAHL, CBM, CNKI, VIP, and Wanfang databases, from establishment to July 13, 2020, selecting for studies about the long-term postoperative efficacy of MVD in the treatment of PTN. Factors associated with higher recurrence rates after MVD and long-term postoperative results of other treatments underwent formal meta-analysis, where odds ratios (ORs) with the corresponding 95% confidence intervals (CIs) were calculated. The dose-response model was used to inspect the associations between several factors and higher recurrence rates.Results: Seventy-four studies (8,172 patients, 32 case series studies, and 42 non-randomized controlled trials) were analyzed in our research. Overall, 956 out of 8,172 patients relapsed, and the pooled recurrence rate was 0.096 (0.080–0.113). Influencing factors of relatively higher recurrence rates included atypical trigeminal neuralgia symptoms, lack of nerve groove, non-arterial compression, patients who were 50–60 years old, and longer disease duration. Dose–response analysis showed that the recurrence rate had a significant trend with the published year and the follow-up time. Simultaneously, the recurrence rate of MVD treatment was much lower than that of conventional drug treatment, gamma knife surgery, percutaneous balloon compression, and radiofrequency thermocoagulation. When the surgical technique was improved or combined with partial sensory rhizotomy (PSR), the postoperative recurrence rates were significantly reduced.Conclusions: Even for PTN patients who have a successful operation, ~10% of them will still relapse. This research identifies several factors that can affect the recurrence rate. Compared with other operations, MVD has a relatively lower recurrence rate. Our analysis suggests that improved surgical techniques and combining PSR and MVD will yield better results.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42020159276.

Abstract Background Many studies have reported the impact of air pollution on cardiovascular disease (CVD), but few of these studies were conducted in severe haze-fog areas. The present study focuses on the impact of different air pollutant concentrations on daily CVD outpatient visits in a severe haze-fog city. Methods Data regarding daily air pollutants and outpatient visits for CVD in 2013 were collected, and the association between six pollutants and CVD outpatient visits was explored using the least squares mean (LSmeans) and logistic regression. Adjustments were made for days of the week, months, air temperature and relative humidity. Results The daily CVD outpatient visits for particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the 90th-quantile group were increased by 30.01, 29.42, 17.68, 14.98, 29.34%, and − 19.87%, respectively, compared to those in the

The incidence of cholelithiasis is more than 10% in the natural population. It is a common and frequently occurring disease worldwide. The lesions are predominantly sand-like and are difficult to distinguish in medical images. Although ultrasound is often the first-line technique for diagnosing cholelithiasis, CT plays an important role in diagnosing complications related to gallstones. To the best of our knowledge, no effective method has been proposed for segmenting CT images of gallstones. To address this difficulty, we have proposed a novel deep learning segmentation model, U-NeXt, which can automatically provide the importance of different shapes and sizes of target structures in images. a) We proposed attention up-sampling blocks for up-sampling. b) We also proposed a spatial pyramid pooling of skip connections, which is called SkipSPP. A series of convolution layers are connected by jumping connections to generate multi-scale features. c) We employed a dense connection to many details of the model to extract more features. In addition, we contributed a new data set for gallstone segmentation, which is co-labelled by three chief physicians from the hepatobiliary department and the medical imaging department of Shandong Province Third Hospital, China. The data set included 5,350 images from 726 patients. Compared with the previous best CT image segmentation method for gallstones, the intersection over union (IoU) improved by 24%, and compared with the medical image segmentation baseline model, the IoU improved by 7%, with a satisfactory segmentation effect. We extended U-NeXt to the nuclei segmentation task, showing that the model has good generalizability.

With the aim of investigating the influence of guide vane meridians on the external characteristics and internal flow field of the mixed-flow pump device, this research constructed seven guide vane meridians and applied computational fluid dynamic (CFD) and entropy production theory to investigate the spread of hydraulic loss in a mixed-flow pump. As observed, when the guide vane outlet diameter Dgvo decreased from 350 mm to 275 mm, the head and efficiency increased by 2.78% and 3.05% at 0.7 Qdes, respectively. At 1.3 Qdes, when Dgvo increased from 350 mm to 425 mm, the head and efficiency increased by 4.49% and 3.71%, respectively. At 0.7 Qdes and 1.0 Qdes, the entropy production of the guide vane increased with the increase of Dgvo due to flow separation. When Dgvo < 350 mm, at 1.0 Qdes and 1.3 Qdes, entropy production of the outlet channel increased as Dgvo decreased owing to the excessive flow rate, but at 0.7 Qdes, entropy production did not change much. When Dgvo > 350 mm, at 0.7 Qdes and 1.0 Qdes, due to the expansion of the channel section, the flow separation intensified, which resulted in an increase of the entropy production, but the entropy production decreased slightly at 1.3 Qdes. These results provide guidance for improving the efficiency of pumping stations.

The aim of this paper is to study the influence of non-uniform suction flow on the transient characteristics of a vertical axial-flow pump device. The unsteady calculation is employed to forecast the unstable flow structure with three inlet deflection angles α, and the calculation accuracy under uniform inlet flow is verified by the external characteristic test. The results depict that a promotion in the α will increase the head and shaft power and thus improve the stress and fatigue failure risk of the impeller. At the impeller inlet, the pressure pulsation intensity (PPI) with α = 40° is lower than that with α = 0° caused by a decline in the axial velocity. The dominant frequency of the unsteady pressure signal is the blade-passing frequency (BPF), and the dominant frequency amplitude rises with the increase in α due to the improvement of the pre-rotation impact intensity. At the guide vanes inlet, the dominant frequency of the unsteady pressure signal at the guide vane inlet is also the blade-passing frequency. An improvement in α magnifies the angle between the trailing edge jet of the impeller and the leading edge of the guide vanes under 0.8Qdes and 1.0Qdes, while it diminishes the angle under 1.2Qdes. Thus, the PPI and dominant frequency amplitude with α = 40° are higher than that with α = 0° under 0.8Qdes and 1.0Qdes, but these are lower than that with α = 0° under 1.2Qdes.

Triboelectric nanogenerators can convert wave energy into the electrical energy required by ocean sensors, but the problem of the low electrical output performance of triboelectric nanogenerators has always been a concern. In this paper, an annular triboelectric nanogenerator (A-TENG) composed of an annular outer shell and an inner ball is proposed to improve the electrical output performance of the triboelectric nanogenerator by optimizing the structural parameters and wave parameters. Using the control variables, the effects of structural parameters (structure size, number of electrodes, electrode spacing, inner ball diameter, and number of inner balls) and wave parameters (wave frequency and wave amplitude) on the electrical output performance of the A-TENG were studied by combining COMSOL simulation and experimental research. The experimental results show that increasing the diameter and number of inner spheres can improve the open-circuit voltage between electrodes; the multi-electrode structure can improve the electron transfer rate and efficiently collect wave energy in all directions; and within the range of fixed sea conditions, there is an optimal annular size, which has the advantages of good electrical output performance and small size. The electrical output performance of the A-TENG can be greatly improved by optimizing the structural parameters. There are optimal wave parameters, such that the A-TENG can maximize the ocean wave energy conversion. This low-cost, long-life, efficient, and reliable energy harvesting system is ideal for powering ocean sensors.