Search

Abstract Despite the importance of radiation therapy as a non-surgical treatment for non-small cell lung cancer (NSCLC), radiation resistance has always been a concern, due to poor patient response and prognosis. Therefore, it is crucial to uncover novel targets to enhance radiotherapy and investigate the mechanisms underlying radiation resistance. Previously, we demonstrated that NRP1 was connected to radiation resistance in NSCLC cells. In the present study, bioinformatics analysis of constructed radiation-resistant A549 and H1299 cell models revealed that transcription coactivator YAP is a significant factor in cell proliferation and metastasis. However, there has been no evidence linking YAP and NRP1 to date. In this research, we have observed that YAP contributes to radiation resistance in NSCLC cells by stimulating cell proliferation, migration, and invasion. Mechanistically, YAP dephosphorylation after NSCLC cell radiation. YAP acts as a transcription co-activator by binding to the transcription factor TEAD4, facilitating TEAD4 to bind to the NRP1 promoter region and thereby increasing NRP1 expression. NRP1 has been identified as a new target gene for YAP/TEAD4. Notably, when inhibiting YAP binds to TEAD4, it inhibits NRP1 expression, and Rescue experiments show that YAP/TEAD4 influences NRP1 to regulate cell proliferation, metastasis and leading to radiation resistance generation. According to these results, YAP/TEAD4/NRP1 is a significant mechanism for radioresistance and can be utilized as a target for enhancing radiotherapy efficacy.

Abstract Haematopoietic stem cells (HSCs) possess unique physiological adaptations to sustain blood cell production and cope with stress responses throughout life. To maintain these adaptations, HSCs rely on maintaining a tightly controlled protein translation rate. However, the mechanism of how HSCs regulate protein translation remains to be fully elucidated. In this study, we investigate the role of transfer RNA (tRNA) m1A58 ‘writer’ proteins TRMT6 and TRMT61A in regulating HSCs function. Trmt6 deletion promoted HSC proliferation through aberrant activation of mTORC1 signaling. TRMT6-deficient HSCs exhibited an impaired self-renewal ability in competitive transplantation assay. Mechanistically, single cell RNA-seq analysis reveals that the mTORC1 signaling pathway is highly upregulated in HSC-enriched cell populations after Trmt6 deletion. m1A-tRNA-seq and Western blot analysis suggest that TRMT6 promotes methylation modification of specific tRNA and expression of TSC1, fine-tuning mTORC1 signaling levels. Furthermore, Pharmacological inhibition of the mTORC1 pathway rescued functional defect in TRMT6-deficient HSCs. To our knowledge, this study is the first to elucidate a mechanism by which TRMT6-TRMT61A complex-mediated tRNA-m1A58 modification regulates HSC homeostasis.

Background: Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious. Methods: To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered. Results: In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images. Conclusions: In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Abstract Background Long non-coding RNAs (LncRNAs) are recognized as a pivotal element in the processes of fracture healing and the osteogenic differentiation of stem cells. This study investigated the molecular mechanism and regulatory significance of lncRNA MAGI2-AS3 (MAGI2-AS3) in fracture healing. Methods Serum levels of MAGI2-AS3 in patients with normal and delayed fracture healing were verified by RT-qPCR assays. The predictive efficacy of MAGI2-AS3 for delayed fracture healing was analyzed by ROC curve. Osteogenic markers were quantified by RT-qPCR assays. MC3T3-E1 cell viability was detected using CCK-8 assay, and flow cytometry was utilized to measure cell apoptosis. The dual-luciferase reporter gene assay was used to determine the targeted binding between MAGI2-AS3 and miR-223-3p. Results Serum MAGI2-AS3 expression was decreased in patients with delayed fracture healing compared with patients with normal healing. Elevated MAGI2-AS3 resulted in an upregulation of the proliferative capacity of MC3T3-E1 cells and a decrease in mortality, along with increased levels of both osteogenic markers. However, after transfection silencing MAGI2-AS3, the trend was reversed. Additionally, miR-223-3p was the downstream target of MAGI2-AS3 and was controlled by MAGI2-AS3. miR-223-3p mimic reversed the promoting effects of MAGI2-AS3 overexpression on osteogenic marker levels and cell growth, and induced cell apoptosis. Conclusion The upregulation of MAGI2-AS3 may expedite the healing of fracture patients by targeting miR-223-3p, offering a novel biomarker for diagnosing patients with delayed healing.

Dynamic assessment of water scarcity utilising blue and green water can enhance water resource management. The traditional water scarcity assessment mainly considers blue water, ignoring green water, for static evaluation. The improvement objective of this study is dynamically quantifying water scarcity, integrated blue and green water. This study proposed a framework to present an overview of water scarcity within multiple indicators and pinpoint water-stressed areas within an ever-changing process. The framework is based on the theorem of mutual change of quality and quantity to assess the spatiotemporal variability of blue and green water availability and to quantify water scarcity in watersheds. A case study was carried out in Taoer River Basin, a semiarid region of China, to demonstrate the use of the framework. The anthropogenic elements (such as water demand) and natural conditions were combined to quantify water scarcity, as measured by blue and green water scarcity indices. This study also analysed the variation of water scarcity on different spatiotemporal scales. The findings demonstrate that severe water scarcity has been occurring downstream with a tendency towards upstream of the watershed. Collectively, this study provides a useful tool for dynamic water scarcity assessment, helping develop policies to promote sustainable development. HIGHLIGHTS Proposed a coupled framework for quantifying water scarcity dynamically.; Integrated multiple indicators to assess the status of water scarcity efficiently.; Identified hot spots of water scarcity based on varied spatiotemporal analysis.;

Abstract The difference in the survival of obese patients and normal-weight/lean patients with diabetic MAFLD remains unclear. Therefore, we aimed to describe the long-term survival of individuals with diabetic MAFLD and overweight/obesity (OT2M), diabetic MAFLD with lean/normal weight (LT2M), MAFLD with overweight/obesity and without T2DM (OM), and MAFLD with lean/normal weight and without T2DM (LM). Using the NHANESIII database, participants with MAFLD were divided into four groups. Hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause, cardiovascular disease (CVD)-related, and cancer-related mortalities for different MAFLD subtypes were evaluated using Cox proportional hazards models. Of the 3539 participants, 1618 participants (42.61%) died during a mean follow-up period of 274.41 ± 2.35 months. LT2M and OT2M had higher risks of all-cause mortality (adjusted HR, 2.14; 95% CI 1.82–2.51; p

Abstract Mud loss is the most serious formation damage in oil and gas well drilling engineering and is an unsolved technical problem. To prevent mud loss, it is necessary to accurately understand and identify three key factors of mud loss, including the location of the loss, the time of occurrence, and the severity of the loss. The diagnosis of mud loss is a prerequisite for the proper formulation of mud loss control techniques. It emphasizes the integration of predrilling, drilling, and postanalysis information to describe and characterize loss zones and predict potential loss zones. On the basis of the theory of engineering fuzzy mathematics, we develop a mathematical model for loss probability evaluation that combines logging anomaly features and engineering data to predict the location of losses from drilling mud and develop a loss formation identification method. The study of hydraulic fracture deformation through stress‐sensitive experiments and numerical simulations can predict the deformation and severity of loss channels, which can help optimize the loss of circulating material by adding drilling fluid. Loss pressure models have been developed based on mud loss mechanisms, and loss pressure for hydraulic fracture creation, connection, and extension has been studied to help identify loss mechanisms and types. Mud losses can be identified by unusual engineering characteristics, including sudden changes in drilling times, cuttings, and mud logging. Real‐time logging parameters can be used to monitor the loss process and hence predict the loss trend. The framework of loss diagnosis techniques is established, which helps in successful mud loss controlling.

Background: Cuproptosis may represent a potential biomarker for predicting prognosis and immunotherapy response, but the available evidence is insufficient. Methods: The multiple single-cell RNA sequencing (scRNA-seq) datasets were analyzed to investigate the specific occurrence of cuproptosis in distinct cell populations. Utilizing 28 scRNA-seq datasets, TCGA pan-cancer cohort, and 10 immunotherapy cohorts, we developed a cuproptosis signature (Cup.Sig). This signature was used to construct prediction models for immunotherapy response and identify potential prognostic biomarkers for pan-cancer using 11 different machine learning algorithms. Results: Malignant cells demonstrate the higher cuproptosis scores in comparison to other cell types across diverse cancer types. The Cup.Sig exhibits significant associations with cancer hallmarks and immune cell response in multiple cancer types. Leveraging the Cup.Sig, the robust pan-cancer immunotherapy prediction model and prognostic biomarker have been established and validated using diverse datasets from various platforms. Conclusions: We developed a pan-cancer cuproptosis signature for predicting survival and immunotherapy response.

Background: Coronary heart disease (CHD) is a common heart disease and a leading cause of death in developed countries and some developing countries such as China. It is recognized as a multifactorial disease, with dyslipidemia being closely associated with the progression of coronary atherosclerosis. Numerous studies have confirmed the relationship between a single indicator of low-density lipoprotein cholesterol (LDL-C) or high-density lipoprotein cholesterol (HDL-C) and CHD. However, the association between LDL-C to HDL-C ratio (LHR) and CHD remains unclear. This study aimed to comprehensively explore the association between LHR and CHD. Methods: This meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses. PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases were comprehensively searched up to June 15, 2023, to find the studies that indicated the connection between LHR and CHD. A total of 12 published studies were selected. The random-effects model was used to pool the data and mean difference (MD), and the 95% confidence intervals (CI) were taken as the overall outcome. No language restrictions existed in the study selection. The Review Manager 5.4 and Stata 12 were used to analyze the data. Results: Twelve high-quality clinical studies involving 5544 participants, including 3009 patients with CHD, were enrolled in the meta-analysis. The findings revealed that the LHR was higher by 0.65 in patients with CHD than in those without CHD (MD, 0.65; 95% CI, 0.50–0.80). Conclusion: The LHR was found to be positively correlated with CHD, suggesting that it may serve as a potential indicator of CHD.

Multibeam water column images (WCI) play a crucial role in the detection and recognition of underwater targets. However, WCI is not a complete image of underwater space due to the gaps between beams inherent in multibeam systems. In this study, we design the WCI super-resolution network (WCISRN) based on Swin-transformer to adapt to the uneven and sparse distribution of water column data. To enable WCISRN to learn morphological features effectively, we produced simulated datasets for pre-training. Subsequently, we conduct self-supervised training on the measured WCI dataset to fine-tune the network parameters, aiming to better adapt WCISRN to the backscattering intensity features of WCI. Finally, by performing network cross-fusion, the pre-trained network and self-supervised training network are combined to form a new network that achieves a balance between image morphology and intensity features. The proposed method is evaluated through visual analysis and quantitative comparison using simulated and real-world datasets. The experimental results demonstrate that our method exhibits stronger detail restoration ability and clearer image morphology. The average peak signal-to-noise ratio (PSNR) on the test set improved from 23.75–26.14, indicating a significant enhancement in the imaging quality of WCI.

Abstract Diabetic cardiomyopathy (DCM) is a condition characterized by myocardial dysfunction that occurs in individuals with diabetes, in the absence of coronary artery disease, valve disease, and other conventional cardiovascular risk factors such as hypertension and dyslipidemia. It is considered a significant and consequential complication of diabetes in the field of cardiovascular medicine. The primary pathological manifestations include myocardial hypertrophy, myocardial fibrosis, and impaired ventricular function, which can lead to widespread myocardial necrosis. Ultimately, this can progress to the development of heart failure, arrhythmias, and cardiogenic shock, with severe cases even resulting in sudden cardiac death. Despite several decades of both fundamental and clinical research conducted globally, there are currently no specific targeted therapies available for DCM in clinical practice, and the incidence and mortality rates of heart failure remain persistently high. Thus, this article provides an overview of the current treatment modalities and novel techniques pertaining to DCM, aiming to offer valuable insights and support to researchers dedicated to investigating this complex condition.

Radiation therapy has been a critical and effective treatment for cancer. However, not all cells are destroyed by radiation due to the presence of tumor cell radioresistance. In the current study, we investigated the effect of low-dose radiation (LDR) on the tumor suppressive effect of high-dose radiation (HDR) and its mechanism from the perspective of tumor cell death mode and DNA damage repair, aiming to provide a foundation for improving the efficacy of clinical tumor radiotherapy. We found that LDR pre-irradiation strengthened the HDR-inhibited A549 cell proliferation, HDR-induced apoptosis, and G2 phase cell cycle arrest under co-culture conditions. RNA-sequencing showed that differentially expressed genes after irradiation contained pyroptosis-related genes and DNA damage repair related genes. By detecting pyroptosis-related proteins, we found that LDR could enhance HDR-induced pyroptosis. Furthermore, under co-culture conditions, LDR pre-irradiation enhances the HDR-induced DNA damage and further suppresses the DNA damage-repairing process, which eventually leads to cell death. Lastly, we established a tumor-bearing mouse model and further demonstrated that LDR local pre-irradiation could enhance the cancer suppressive effect of HDR. To summarize, our study proved that LDR pre-irradiation enhances the tumor-killing function of HDR when cancer cells and immune cells were coexisting.

Several COVID-19 vaccines have been approved for emergency use according to China's immunization programs. These vaccines has created hope for patients with epilepsy, because the vaccines can help to reduce their risk of becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aim of this study was to investigate the COVID-19 vaccine safety in patients with epilepsy. Here, we assessed the time of symptom control and the features of adverse events of seizure patients following their COVID-19 vaccinations. The results showed that adverse events of COVID-19 vaccinations for epilepsy patients included local pain at the injection site, dizziness and headache, epileptic attack, somnolence, limb weakness, limb pain, allergy, and fever. In addition, the average recovery time of the adverse events was approximately 42 h. More importantly, our study showed that it was relatively safe to vaccinate epilepsy patients who did not experience seizures for approximately 12 months prior to the immunization date.

Coal fire is a serious problem in China’s mining areas, which has become a major threat to regional environment and energy security. The timely and accurate grasp of the distribution range of coal fires and the clarification of their spatiotemporal evolution law are crucial for effective control and management in coal fire areas. In this paper, a coal fire identification method based on satellite remote sensing considering the difference of land cover is proposed. Firstly, based on Landsat satellite images, the atmospheric correction was carried out using the radiative conduction equation method to invert the absolute surface temperature. Secondly, the supervised classification method of support vector machine is used to obtain the land cover type and determine the abnormal region of coal fire. Finally, the distribution characteristics of coal fire in time and space are deduced by regression analysis method, and the coal fire prediction model is constructed. Wuhai City, Inner Mongolia Autonomous Region is selected as the research area, and the Landsat time series satellite images from 2018—2023 are used as the data source to carry out the coal fire anomaly identification and prediction experiment. The results show that the coal fire identification results of the proposed method are consistent with the field verification and low-level monitoring results, and the predicted region and coal fire degree are consistent with the satellite inversion results, which confirms the reliability of the proposed method for the “sweep target” identification of coal fires in large areas, and provides reliable technical support for the timely discovery of coal fires in mining areas, the analysis of space-time evolution laws, and the restoration and treatment of coal fires.

SF6 is a strong greenhouse effect gas, which is widely used in high-voltage electrical equipment such as circuit breakers and high-voltage switchgear because of its excellent insulation performance and arc extinguishing ability. In recent years, the use and emission of SF6 have been rising, and with the proposal of the dual carbon strategic goal, its harmless degradation has become an urgent problem to be solved. In this paper, SF6 was degraded by pulsed DBD plasma technology and O2. Studies have shown that the addition of O2 can effectively promote the degradation of SF6. With the increase in the added O2 content, the DRE and EY of SF6 first increased and then decreased. Under the conditions of the input power of 50 W, SF6 concentration of 2%, and gas flow rate of 50 mL/min, the reaction system obtained the highest DRE and EY of 58.40% and 5.24 g/kWh when the O2 content was 1%, respectively. In the SF6/Ar/O2/H2O system, the addition of H2O could improve the product selectivity of SO2F2, and when the O2 concentration was 1%, the highest selectivity of SO2F2 was 48.96%, and the concentration was 8006.76 ppm. The addition of O2 inhibited the production of SO2, and with the addition of the O2 system, SO2F2 and SOF4 were the main components of degradation products; however, there were also SOF2, SO2, SiF4, SF4, etc. In this paper, the decomposition path of O2 under SF6 was analyzed in detail according to infrared spectroscopy and decomposition products.

SO2F2 is widely used as a fumigant pesticide owing to its strong diffusion permeability, low residue, and high insecticidal speed. However, the strong greenhouse effect limits its application, and harmless treatment is also one of the problems. Dielectric barrier discharge (DBD) is an effective way to deal with harmful gas, and the treatment efficiency can be significantly improved by adding active gases such as H2O, H2, etc. In this paper, the effect of H2O and H2 on SO2F2 degradation by DBD is investigated. It shows the synergistic effect of reactive gas and DBD plasma on SO2F2 degradation that reactive particles generated from the discharge participate in the degradation of SO2F2 and hinder its recovery. When the input power is 80 W, the 2% SO2F2/1% H2O/97% Ar system achieves a degradation removal efficiency (DRE) of 86.26% and an energy yield (EY) of 13.55 g/kWh, while the 2% SO2F2/1% H2/97% Ar system shows a DRE and an EY of 80.29% and 12.61 g/kWh, respectively. However, in the 2% SO2F2/Ar system, the DRE and the EY are only 64.13% and 10.11 g/kWh, respectively. Moreover, the decomposition path of SO2F2 is analyzed, and it is found that SO2 and SOF2 are the main products in both H2O and H2 systems while the H2 addition generates solid S via the reduction of SO2F2. The results show that the addition of reactive gas can effectively promote the degradation and regulatory product of SO2F2, which provides support for the efficient and harmless degradation of SO2F2 in industry.

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decision-maker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learning-based approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.

Abstract Background Diffuse large B-cell lymphoma (DLBCL) is an aggressive malignancy. Neutrophil extracellular traps (NETs) are pathogen-trapping structures in the tumor microenvironment that affect DLBCL progression. However, the predictive function of NET-related genes (NRGs) in DLBCL has received little attention. This study aimed to investigate the interaction between NRGs and the prognosis of DLBCL as well as their possible association with the immunological microenvironment. Methods The gene expression and clinical data of patients with DLBCL were downloaded from the Gene Expression Omnibus database. We identified 148 NRGs through the manual collection of literature. GSE10846 (n = 400, GPL570) was used as the training dataset and divided into training and testing sets in a 7:3 ratio. Univariate Cox regression analysis was used to identify overall survival (OS)-related NETs, and the least absolute shrinkage and selection operator was used to evaluate the predictive efficacy of the NRGs. Kaplan–Meier plots were used to visualize survival functions. Receiver operating characteristic (ROC) curves were used to assess the prognostic predictive ability of NRG-based features. A nomogram containing the clinical information and prognostic scores of the patients was constructed using multivariate logistic regression and Cox proportional risk regression models. Results We identified 36 NRGs that significantly affected patient overall survival (OS). Eight NRGs (PARVB, LYZ, PPARGC1A, HIF1A, SPP1, CDH1, S100A9, and CXCL2) were found to have excellent predictive potential for patient survival. For the 1-, 3-, and 5-year survival rates, the obtained areas under the receiver operating characteristic curve values were 0.8, 0.82, and 0.79, respectively. In the training set, patients in the high NRG risk group presented a poorer prognosis (p

Amidst the rapid advancements in the fields of photovoltaics and optoelectronic devices, CsPbBr3 nanosheets (NSs) have emerged as a focal point of research due to their exceptional optical and electronic properties. This work explores the application potential of CsPbBr3 NSs in photonic and catalytic domains. Utilizing an optimized hot-injection method and a ZnBr2-assisted in situ passivation strategy, we successfully synthesized CsPbBr3 NSs with controlled dimensions and optical characteristics. Comprehensive characterization revealed that the nucleation environment and thickness significantly influenced the structure and optical performance of the materials. The results indicate that the optimized synthesis method enables control over the lateral dimensions of the nanoparticles, ranging from 9.1 ± 0.06 nm to 334.5 ± 4.40 nm, facilitating the tuning of the excitation wavelength from 460 nm (blue) to 510 nm (green). Further analyses involving photoresponse and electrochemical impedance spectroscopy demonstrated the substantial potential of these NSs in applications such as photocatalysis and energy conversion.

Metal halide perovskite semiconductors have emerged as promising materials for various optoelectronic applications due to their unique crystal structure and outstanding properties. Among different forms, perovskite nanowires (NWs) offer distinct advantages, including a high aspect ratio, superior crystallinity, excellent light absorption, and carrier transport properties, as well as unique anisotropic luminescence properties. Understanding the formation mechanism and structure–property relationship of perovskite NWs is crucial for exploring their potential in optoelectronic devices. In this study, we successfully synthesized all-inorganic halide perovskite NWs with high aspect ratios and an orthorhombic crystal phase using the hot-injection method with controlled reaction conditions and surface ligands. These NWs exhibit excellent optical and electrical properties. Moreover, precise control over the halogen composition through a simple anion exchange process enables the tuning of the bandgap, leading to fluorescence emission, covering a wide range of colors across the visible spectrum. Consequently, these perovskite NWs hold great potential for efficient energy conversion and catalytic applications in photoelectrocatalysis.

Transition metal dichalcogenides (TMDs), particularly monolayer TMDs with direct bandgap properties, are key to advancing optoelectronic device technology. WSe2 stands out due to its adjustable carrier transport, making it a prime candidate for optoelectronic applications. This study explores monolayer WSe2 synthesis via H2-assisted CVD, focusing on how carrier gas flow rate affects WSe2 quality. A comprehensive characterization of monolayer WSe2 was conducted using OM (optical microscope), Raman spectroscopy, PL spectroscopy, AFM, SEM, XPS, HRTEM, and XRD. It was found that H2 incorporation and flow rate critically influence WSe2’s growth and structural integrity, with low flow rates favoring precursor concentration for product formation and high rates causing disintegration of existing structures. This research accentuates the significance of fine-tuning the carrier gas flow rate for optimizing monolayer WSe2 synthesis, offering insights for fabricating monolayer TMDs like WS2, MoSe2, and MoS2, and facilitating their broader integration into optoelectronic devices.

As a direct band gap semiconductor, perovskite has the advantages of high carrier mobility, long charge diffusion distance, high defect tolerance and low-cost solution preparation technology. Compared with traditional metal halide perovskites, which regulate energy band and luminescence by changing halogen, perovskite quantum dots (QDs) have a surface effect and quantum confinement effect. Based on the LaMer nucleation growth theory, we have synthesized CsPbBr3 QDs with high dimensional homogeneity by creating an environment rich in Br− ions based on the general thermal injection method. Moreover, the size of the quantum dots can be adjusted by simply changing the reaction temperature and the concentration of Br− ions in the system, and the blue emission of strongly confined pure CsPbBr3 perovskite is realized. Finally, optical and electrochemical tests suggested that the synthesized quantum dots have the potential to be used in the field of photocatalysis.

Hyperspectral image (HSI) classification aims to recognize categories of objects based on spectral–spatial features and has been used in a wide range of real-world application areas. Attention mechanisms are widely used in HSI classification for their ability to focus on important information in images automatically. However, due to the approximate spectral–spatial features in HSI, mainstream attention mechanisms are difficult to accurately distinguish the small difference, which limits the classification accuracy. To overcome this problem, a spectral–spatial-sensorial attention network (S3AN) with controllable factors is proposed to efficiently recognize different objects. Specifically, two controllable factors, dynamic exponential pooling (DE-Pooling) and adaptive convolution (Adapt-Conv), are designed to enlarge the difference in approximate features and enhance the attention weight interaction. Then, attention mechanisms with controllable factors are utilized to build the redundancy reduction module (RRM), feature learning module (FLM), and label prediction module (LPM) to process HSI spectral–spatial features. The RRM utilizes the spectral attention mechanism to select representative band combinations, and the FLM introduces the spatial attention mechanism to highlight important objects. Furthermore, the sensorial attention mechanism extracts location and category information in a pseudo label to guide the LPM for label prediction and avoid details from being ignored. Experimental results on three public HSI datasets show that the proposed method is able to accurately recognize different objects with an overall accuracy (OA) of 98.69%, 98.89%, and 97.56%, respectively.

Abstract The near-trench coseismic rupture behaviour of the 2011 Tohoku–Oki earthquake remains poorly understood due to the scarcity of near-field observations. Differential bathymetry offers a unique approach to studying offshore coseismic seafloor deformation but has a limited horizontal resolution. Here we use differential bathymetry estimates with improved horizontal resolutions to investigate near-trench coseismic slip behaviours in the 2011 Tohoku–Oki earthquake. In the main rupture region, a velocity-strengthening behaviour in the shallow fault is observed. By contrast, the seafloor uplift decreases towards the trench, but the trend inverts near the backstop interface outcrop, revealing significant off-fault deformation features. Amongst various competing off-fault effects observed, we suggest that inelastic deformation plays a predominant role in near-trench tsunami excitation. Large trench-bleaching rupture is also observed immediately north of 39°, delimiting the northern extent of the main rupture region. Overall, striking spatial heterogeneity of the shallow rupture behaviour is revealed for the region.

Abstract Background Tyrosine kinase inhibitors (TKI) have been highlighted for the therapy of non-small-cell lung cancer (NSCLC), due to their capability of efficiently blocking signal pathway of epidermal growth factor receptor (EGFR) which causes the inhibition and apoptosis of NSCLC cells. However, EGFR-TKIs have poor aqueous solubility and severe side effects arising from the difficulty in control of biodistribution. In this study, folate-functionalized nanoparticles (FA-NPs) are designed and fabricated to load EGFR-TKI through flash nanoprecipitation (FNP) strategy, which could enhance the tumor-targeting drug delivery and reduced drug accumulation and side effects to normal tissues. Results Herein, the EGFR-TKI loaded FA-NPs are constructed by FNP, with FA decorated dextran-b-polylactide as polymeric stabilizer and gefitinib as TKI. The fast mixing and co-precipitation in FNP provide FA-NPs with well-defined particle size, narrow size distribution and high drug loading content. The FA-NPs exhibit efficient uptake and cytotoxicity in HCC827 NSCLC cells, and reduced uptake and cytotoxicity in normal cells comparing with free gefitinib. In vivo evaluation of gefitinib-loaded FA-NPs confirms the selective drug delivery and accumulation, leading to enhanced inhibition on NSCLC tumor and simultaneously diminished side effects to normal tissues. Conclusion The facile design of FA-NPs by FNP and their achieved performance in vitro and in vivo evaluations offer new therapeutic opportunities for treatment of non-small cell lung cancer. Graphical Abstract

Abstract As an important component of the machining system, the influence of fixtures on the machining deformation of the workpiece cannot be ignored. By controlling the clamping force during the machining process is an effective means to suppress or improve the machining deformation. However, due to the dynamic coupling of part geometry, clamping method, manufacturing process and time-varying cutting forces, it is difficult to obtain accurate clamping forces, which hinders the realization of fixture-based deformation control. In this paper, the variation of clamping force is considered as the response of the joint action of cutting force and other working conditions in spatial and temporal terms, and a clamping force prediction method based on deep spatio-temporal network is proposed. The part geometry model is first parameterized based on voxels, after which the cutting forces are dynamically correlated with the clamping forces in spatial and temporal terms. Then, a convolutional network was designed to capture the spatial correlation between the working conditions such as cutting force and clamping force, and a gated recurrent cell network to capture the temporal correlation to predict the clamping force during machining. Finally, an experiment of milling a cylindrical thin-walled part illustrates the effectiveness of the proposed method.

The production of fractured oil and gas reservoirs in the world accounts for more than half of total oil and gas production and is one of the important fields for increasing oil and gas storage and production in the 21st century. The key to characterizing fractured oil and gas reservoirs is the distribution pattern of natural fractures. The distribution of natural fractures is dominated by structural deformations and fractures. Therefore, analytical and structural simulations of the tectonic stress field in geological structural systems play a crucial role in obtaining fracture distribution patterns. In this paper we have developed a description of the distribution of natural fractures based on finite element numerical simulations of the paleotectonic stress field. The study focused on the Chang 7 and Huangjialing Chang 8 reservoirs in the underground Siwan region of the Ordos Basin in China. First, an experimental assessment of the rock mechanics of the reservoir was completed, and the values of the paleotectonic stresses obtained from the tests were used as inversion criteria for the stress field simulations. Based on the geology, a refined geological model has been developed to study the structure of the Chang 7 and Chang 8 oil reservoirs in the block. The simulated paleotectonic stress fields for natural fractures in the Indosinian period are as follows: maximum principal stress of 94.67 MPa, minimum principal stress of 21.58 MPa, and vertical stress of 28.07 MPa. The direction of maximum principal stress remains essentially in the NE-SW direction, with the largest differential stress occurring in the Chang 8 oil layer group in Huangjialing, while the differential stress is relatively uniform in the Chang 7 oil layer group in Xiasiwan. It predicts the relative density of natural fractures in the Chang 7 and Chang 8 reservoirs, and finds high-to-low plane heterogeneity in the Huangjialing Chang 8 reservoir group and the Xiasiwan Chang 7 reservoir group, respectively. The paper reveals the pattern of development of reservoir fractures in both vertical and horizontal directions, providing an important geological basis for efficient and rational exploitation of oil and gas resources in the study area and improving oil recovery.

In order to improve the film cooling efficiency of the cooling gas on the hot side wall of the plate, the flow field, temperature field and the average film cooling efficiency of the cylindrical hole, rectangular hole and waveform hole near the wall were compared and analyzed by numerical calculation method. Compared with cylindrical holes, the average spanwise film cooling efficiency of rectangular hole is improved by 100%, and the average spanwise film cooling efficiency of waveform hole is improved by 130%. In addition, the effects of the amplitude A of the waveform hole and the initial phase ϕ on the cooling efficiency are compared and analyzed. The results show that: Within the scope of this study, when the blowing ratio M = 1, lifting the initial phase ϕ of the waveform hole can effectively improve the film cooling efficiency of the cooling gas on the wall. When the amplitude A of the waveform hole is raised, the film cooling efficiency of the cooling gas on the wall will first increase and then decrease, reaching the maximum at the amplitude A = 0.4D. At low blowing ratio, the geometric structure of waveform hole has little influence on the cooling effect of wall surface. Under high blowing ratio, when the geometric structure of waveform hole is changed, the influence of secondary flow on the average spanwise cooling efficiency of wall surface is great.

Summary: Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.

Abstract As a common pathology of many ocular disorders such as diabetic retinopathy and glaucoma, retinal ischemia/reperfusion (IR) triggers inflammation and microglia activation that lead to irreversible retinal damage. The detailed molecular mechanism underlying retinal IR injury, however, remains poorly understood at present. Here we report the bioinformatic identification of a lncRNA 1810058I24Rik (181-Rik) that was shown to encode a mitochondrion-located micropeptide Stmp1. Its deficiency in mice protected retinal ganglion cells from retinal IR injury by attenuating the activation of microglia and the Nlrp3 inflammasome pathway. Moreover, its genetic knockout in mice or knockdown in primary microglia promoted mitochondrial fusion, impaired mitochondrial membrane potential, and reactive oxygen species (ROS) production, diminished aerobic glycolysis, and ameliorated inflammation. It appears that 181-Rik may trigger the Nlrp3 inflammasome activation by controlling mitochondrial functions through inhibiting expression of the metabolic sensor uncoupling protein 2 (Ucp2) and activating expression of the Ca2+ sensors S100a8/a9. Together, our findings shed new light on the molecular pathogenesis of retinal IR injury and may provide a fresh therapeutic target for IR-associated neurodegenerative diseases.

Polymetallic nodules are spherical or ellipsoidal mineral aggregates formed naturally in deep-sea environments. They contain a variety of metallic elements and are important solid mineral resources on the seabed. How best to quickly and accurately identify polymetallic nodules is one of the key questions of marine development and deep-sea-mineral-resource utilization. We propose a method that uses YOLOv5s as a reference network and integrates the IoU (Intersection over Union) and the Wasserstein distance in the optimal transmission theory to accurately identify different sizes of polymetallic nodules. Experiment using deep-sea hyperspectral data obtained from the Peru Basin was performed. The results showed that better recognition effects were achieved when the fusion ratio of overlap and Wasserstein distance metric was 0.5, and the accuracy of the proposed algorithm reached 84.5%, which was 6.2% higher than that of the original baseline network. In addition, the rest of the performance indexes were also improved significantly compared to traditional methods.

Seizure is associated with pathological changes of hippocampus, but the mechanism by which hippocampal neuronal apoptosis promotes epilepsy is unclear. Our previous study showed that the expression of NHE-1 was increased in epileptic model rats. Therefore, this study further explores the effect of NHE-1 on hippocampal cells apoptosis and seizure in lithium chloride-pilocarpine epileptic model rats. First, we established a lithium chloride-pilocarpine induced epileptic rat model and detected the expression of NHE-1, calpain1 and apoptosis in the hippocampus. Then, we further down-regulated NHE-1 to observe the expression of calpain1 and apoptosis in the hippocampus, as well as its effect on seizures in rats. We found that the expression of NHE-1 and calpain1 and apoptosis in the hippocampus was significant increased in the model group. After down-regulating NHE-1, the expression of calpain1 was decreased, and hippocampal cell apoptosis was alleviated. In addition, down-regulation of NHE-1 reduced the frequency and duration of seizures in epileptic rats. Therefore, hippocampal NHE-1 overexpression is closely related to the development of neuronal apoptosis in a rat model of epilepsy, and downregulating NHE-1 expression can reduce cell apoptosis. Moreover, the NHE-1/calpain1 signaling pathway may be an important mechanism leading to hippocampal cell apoptosis.

Abstract SF6 has been recognized worldwide as the main insulating gas for Gas‐Insulated Switchgear (GIS). It is often required to accurately and effectively detect typical SF6 decomposition. In this paper, a sub‐ppm‐level SO2F2 and SOF2 gas sensor based on photoacoustic spectroscopy (PAS) is proposed and demonstrated. The steel resonant photoacoustic cell with a resonant frequency of 1750 Hz was designed and fabricated. The harmonic detection technique in wavelength modulation spectroscopy was applied to improve the signal‐to‐noise ratio. A mercury‐cadmium‐telluride photodetector was added in the PAS system to monitor the input optical power, in order to compensate for the errors caused by power fluctuations. SO2F2 and SOF2 were measured by using distributed feedback quantum cascade lasers at 6648 and 7463 nm, respectively. The results show that the proposed sensor performed with favourable linearity within the dynamic range, and reached the minimum detection limit of 0.22 ppm for SO2F2 and 0.28 ppm for SOF2, which indicated its great potential for monitoring the decomposition process of SF6 in GIS.

Background The coronavirus disease (COVID-19) epidemic has not been completely controlled. Although great achievements have been made in COVID-19 research and many antiviral drugs have shown good therapeutic effects against COVID-19, a simple oral antiviral drug for COVID-19 has not yet been developed. We conducted a meta-analysis to investigate the improvement in mortality or hospitalization rates and adverse events among COVID-19 patients with three new oral antivirals (including molnupiravir, fluvoxamine and Paxlovid). Methods We searched scientific and medical databases, such as PubMed, Web of Science, Embase and Cochrane Library for relevant articles and screened the references of retrieved studies on COVID-19. Results A total of eight studies were included in this study. The drug group included 2440 COVID-19 patients, including 54 patients who died or were hospitalized. The control group included a total of 2348 COVID-19 patients, including 118 patients who died or were hospitalized. The overall odds ratio (OR) of mortality or hospitalization was 0.33 (95% confidence interval [CI], 0.22–0.49) for COVID-19 patients in the drug group and placebo group, indicating that oral antiviral drugs were effective for COVID-19 patients and reduced the mortality or hospitalization by approximately 67%. Conclusions This study showed that three novel oral antivirals (molnupiravir, fluvoxamine and Paxlovid) are effective in reducing the mortality and hospitalization rates in patients with COVID-19. In addition, the three oral drugs did not increase the occurrence of adverse events, thus exhibiting good overall safety. These three oral antiviral drugs are still being studied, and the available data suggest that they will bring new hope for COVID-19 recovery and have the potential to be a breakthrough and very promising treatment for COVID-19.KEY MESSAGES Many antiviral drugs have shown good therapeutic effects, and there is no simple oral antiviral drug for COVID-19 patients. Meta-analysis was conducted for three new oral antivirals to evaluate the improvement in mortality or hospitalization rates and adverse events among COVID-19 patients. We focussed on three new oral Coronavirus agents (molnupiravir, fluvoxamine and Paxlovid) and hope to provide guidance for the roll-out of oral antivirals.

Although recent studies have indicated that mutations in the gene encoding diacylglycerol kinase epsilon (DGKE) result in some proteinuria related hereditary kidney diseases, the DGKE expression pattern in the kidney and its contribution to acute kidney injury (AKI) remain unknown. Therefore, the present study was designed to detect the role of DGKE in mice with AKI. DGKE expression was time-dependently altered in the kidneys of mice with renal ischemia/reperfusion injury (IRI). Compared with wild-type (WT) mice, DGKE- overexpressing mice (Rosa26-Dgke+/+) exhibited protective effects against renal IRI, including reduced serum creatinine, blood urea concentration, tubular cell death and inflammatory responses as well as improved morphological injuries. Consistently, in vitro, DGKE overexpression in human renal proximal tubule (HK-2) cells also protected against oxygen-glucose deprivation (OGD)/reoxygenation-induced cell death. Mechanistically, DGKE regulated Klotho expression, at least partly via the transcription factor Krüppel-like factor (KLF) 15. Moreover, a significant reduction in DGKE was also found in kidneys from patients with ischemia-associated acute tubular necrosis (ATN). Collectively, our studies demonstrate that DGKE protects against AKI in mice at least partly through KLF15/Klotho signaling pathway, indicating that DGKE may present an innovative therapeutic strategy for treating patients with AKI.

Abstract Evaluation of tool wear is vital in manufacturing system, since early detections on worn-out condition can ensure workpiece quality, improve machining efficiency. With the development of intelligent manufacturing, tool wear prediction technology plays an increasingly important role. However, traditional tool wear prediction methods rely on experience and knowledge of experts and are labor-extensive. Deep learning provides an effective way to extract features of raw data and establish the mapping relationship between features and targets automatically. In this paper, a new local-feature and global-dependency based tool wear prediction method is proposed. It is a hybrid approach combining manual features with automatic features. Firstly, an enhanced CNN network is designed and applied on the transformed wavelet scalogram to learn the local single-scale specific features and multi-scale correlation features automatically. Secondly, sequence of local feature vectors combining manual features with automatic features are fed into multi-layer LSTM step by step for the global dependency. A fully connected layer is then trained to predict tool wear. Finally, two statistics are proposed to illustrate the overall prediction performance and generalization ability of the model. An experiment illustrates the effectiveness of our proposed method under multiple working conditions.

Abstract Background With the increasing incidence of asymptomatic carriers or milder symptoms, children and adolescents are likely to become a silent source of infection. In view of the efficacy and safety of vaccines in the treatment of novel Coronavirus pneumonia, population-wide vaccination will be an inevitable trend to control the spread of COVID-19. However, there is no survey on the attitudes of Chinese parents of children and adolescents towards their children’s COVID-19 vaccination. Methods We used online questionnaires to find out the attitudes of Chinese parents toward their children’s immunization against COVID-19. Logistic regression was used to explore the influencing factors. Results A total of 2019 parents participated in the survey. Overall, 74.38% parents said they would actively get vaccinated, 8.90% refused to get vaccinated, 4.60% said they would delay vaccination and 12.12% were still undecided. Conclusions In general, Chinese parents have a high desire to be vaccinated against COVID-19, and most parents have a positive attitude towards their children’s vaccination. However, many people still hesitate or even refuse to be vaccinated. Education background, attitudes towards children’s vaccination, children’s age, recent illness and other factors have a certain impact on Chinese parents of children and adolescents towards their children’s COVID-19 vaccination.

The morbidity and mortality of colorectal cancer (CRC) have been increasing in recent years, and early detection of CRC can improve the survival rate of patients. RNA methylation plays crucial roles in many biological processes and has been implicated in the initiation of various diseases, including cancer. Serum contains a variety of biomolecules and is an important clinical sample for biomarker discovery. In this study, we developed a targeted metabolomics method for the quantitative analysis of nucleosides in human serum samples by using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We successfully quantified the concentrations of nucleosides in serum samples from 51 healthy controls, 37 patients with colorectal adenomas, and 55 patients with CRC. The results showed that the concentrations of N6-methyladenosine (m6A), N1-methyladenosine (m1A), and 3-methyluridine (m3U) were increased in patients with CRC, whereas the concentrations of N2-methylguanosine (m2G), 2′-O-methyluridine (Um), and 2′-O-methylguanosine (Gm) were decreased in patients with CRC, compared with the healthy controls and patients with colorectal adenomas. Moreover, the levels of 2′-O-methyluridine and 2′-O-methylguanosine were lower in patients with colorectal adenomas than those in healthy controls. Interestingly, the levels of Um and Gm gradually decreased in the following order: healthy controls to colorectal adenoma patients to CRC patients. These results revealed that the aberrations of these nucleosides were tightly correlated to colorectal adenomas and CRC. In addition, the present work will stimulate future investigations about the regulatory roles of these nucleosides in the initiation and development of CRC.

IntroductionUnlike the effect of repetitive transcranial magnetic stimulation (rTMS) in treating neuropsychiatric diseases, little is known about how personal factors might account for the disparity of results from studies of cognition and rTMS. In this study, we investigated the effects of high-frequency rTMS on response inhibition control and explored the time course changes in cognitive processing and brain metabolic mechanisms after rTMS using event-related potentials (ERPs) and magnetic resonance spectroscopy (1H-MRS).MethodsParticipants were all right-handed and were naive to rTMS and the Go/NoGo task. Twenty-five healthy young participants underwent one 10 Hz rTMS session per day in which stimulation was applied over the left dorsolateral prefrontal cortex (DLPFC), and a homogeneous participant group of 25 individuals received a sham rTMS treatment for 1 week. A Go/NoGo task was performed, an electroencephalogram (EEG) was recorded, and 1H-MRS was performed.ResultsThe results revealed that there was a strong trend of decreasing commission errors of NoGo stimuli by high frequency rTMS over the left DLPFC, whereas there was no significant difference between before and after rTMS treatment with respect to these parameters in the sham rTMS group. High-frequency rTMS significantly increased the amplitude of NoGo-N2 but not Go-N2, Go-P3, or NoGo-P3. The myo-inositol /creatine complex (MI/Cr) ratio, indexing cerebral metabolism, in the left DLPFC was decreased in the rTMS treated group.DiscussionThis observation supports the view that high-frequency rTMS over the left DLPFC has the strong tendency of reducing commission errors behaviorally, increase the amplitude of NoGo-N2 and improve the response inhibition control of healthy young participants. The results are consistent with the excitatory properties of high frequency rTMS. We suggest that the increase in the NoGo-N2 amplitude may be related to the increased excitability of the DLPFC-anterior cingulate cortex (ACC) neural loop. Metabolic changes in the DLPFC may be a possible mechanism for the improvement of the response inhibition control of rTMS.

Abstract Due to quick response and large quantity of electric motor torque, the traction wheels of battery electric vehicle are easy to slip during the initial phase of starting. In this paper, a sliding mode control approach of acceleration slip regulation is designed to prevent the slip of the traction wheels. The wheel slip ratio is used as the state variable for the formulation of system dynamics model. The fuzzy algorithm is utilized to adjust the switch function of sliding mode controller. After stability and robustness analysis, the sliding mode control law is transferred into C code and downloaded into vehicle control unit, which is validated under wet and dry road conditions. The experimental results with a small overshoot and a quick response during starting indicate that the sliding mode controller has good control effect on the slip ratio regulation. This article proposes an acceleration slip regulation method that improves the safety during acceleration for battery electric vehicle.

Erianin is an active constituent of Dendrobium candidum. In this work, 18F-fluoroethoxylerianin ([18F]FEE), a 18F-Labeled erianin analogue, was designed and synthesized to evaluate the properties of erianin and related analogues by in vivo PET imaging. The initial product was separated and purified by liquid-phase separation module Explora LC and simple homemade solid-phase extraction, and high purity [18F]FEE was finally obtained. The radiochemical purity of [18F]FEE was determined by Radio-TLC and Radio-HPLC. [18F]FEE showed good stability in normal saline and serum, and could be quickly eliminated from mice. Cell experiments, biological distribution, and small-animal PET/CT further showed that [18F]FEE had a high uptake rate in HepG2 tumor cells, and showed good imaging ability in a HepG2 tumor model. The results of this study indicate that the synthesized 18F-labeled erianin analogue is an effective new probe for positron emission tomography (PET) imaging of HepG2 hepatocellular carcinoma, which provides an intuitive and reliable theoretical basis for the development of erianin as an anticancer drug.

Abstract With the miniaturization of crystals‐based photoelectric devices, electrode contact‐geometries may play a critical role in determining the device performance. However, investigation of the role of electrode contact geometries in situ faces great challenges due to the electrode contact geometry is typically unmodifiable. To this end, a kind of liquid metal is employed as an adaptive‐deformable electrode to study the carrier transport through perovskite microcrystals, in which the electrode contacts geometries/positions and thus the carrier‐pathways can be adjusted. Under light illumination, a spike feature of photocurrent is observed when carriers transport along the perovskite microcrystal surface upon an edge‐contact geometry, which is absent as the carrier mainly transport through crystal interior upon a top‐contact geometry. Switching, rectifying, and memristor functions are selectively realized just by modifying the contact geometry. The underlying mechanism for the observations is further elucidated. This study provides a platform for studying carrier transport through microscale crystals with adjustable contact geometry and supplies an approach for fabricating diverse functional devices by changing the electrode contact‐geometries.

Major shale gas exploration and development fields are located in the Sichuan basin. It requires huge water sources for shale gas fracking, but the well sites are mostly in the hills, which limits the industrialization of shale gas development. CO2 foam fluids can meet the requirements of fracking fluids and relieve water stress. It analyzed the feasibility of CO2 foaming fracturing for shale gas formation fracturing, proposed a design philosophy for CO2 foaming fracturing, and optimized fracturing parameters such as foam mass, proppant concentration, friction, and discharge rate. The flowchart of CO2 foam fracturing was established in, where the fracture morphology and propagation behavior of CO2 foam fracturing were obtained from numerical simulations comparable to the hydraulic fracture generated by conventional hydraulic fracturing. The CO2 foaming fracturing technique can provide a discharge rate of 6.0 m3/min and fluid volume and captures the volume effect of the current stimulated reservoir, which needs to be improved. It can be considered an initial survey of CO2 foam fracturing available in the Sichuan Basin shale formation, which may provide new methods and clues for stimulation.

The physical property of Chang 6 reservoir in Yanchang oilfield is poor, and the heterogeneity is strong. Multistage fracturing of horizontal wells is easy to form only one large horizontal fracture, but it is difficult to control the fracture height and length. The new mining method of “bow horizontal well + multistage horizontal joint” can effectively increase the multistage horizontal joint’s spatial position, which improves the drainage area and stimulation efficiency of oil wells. Due to the reservoir’s low permeability and strong heterogeneity, the single well mode of “bow horizontal well + multistage horizontal fracture” cannot effectively produce Chang 6 reservoir. To improve the production degree of the g 6 reservoir, the fracture model is established using equivalent conductivity and the multigrid method. The pressure response functions of horizontal wells and volume fracturing horizontal wells are established by using the source function, and the relationship between reservoir permeability and starting pressure gradient in the block is calculated. On this basis, the reservoir productivity equation of the block is established, which provides a basis for optimizing the fracturing design parameters of horizontal wells. It is proposed that the flow unit should be considered in the design of fracturing parameters of horizontal fractures, the number of fractures should comprehensively consider whether the fractures can make each flow unit be used, and have large controlled reserves, and the scale of fracturing should comprehensively consider the output and cost. The fracture network model is established by using equivalent conductivity and multi-gridthod, and the volume fracturing design parameters of horizontal wells are optimized, considering the seepage characteristics of the flow unit. The fracturing design parameters of the horizontal section are further defined, which provides a theoretical basis for the efficient development of shallow tight reservoirs.

With the improvement of spectral resolution, the redundant information in the hyperspectral imaging (HSI) datasets brings computational, analytical, and storage complexities. Feature selection is a combinatorial optimization problem, which selects a subset of feasible features to reduce the dimensionality of data and decrease the noise information. In recent years, the evolutionary algorithm (EA) has been widely used in feature selection, but the diversity of agents is lacking in the population, which leads to premature convergence. In this paper, a feature selection method based on discarding–recovering and co-evolution mechanisms is proposed with the aim of obtaining an effective feature combination in HSI datasets. The feature discarding mechanism is introduced to remove redundant information by roughly filtering the feature space. To further enhance the agents’ diversity, the reliable information interaction is also designed into the co-evolution mechanism, and if detects the event of stagnation, a subset of discarded features will be recovered using adaptive weights. Experimental results demonstrate that the proposed method performs well on three public datasets, achieving an overall accuracy of 92.07%, 92.36%, and 98.01%, respectively, and obtaining the number of selected features between 15% and 25% of the total.

When Unmanned Aerial Vehicles (UAVs) are used in search and rescue operations, electro-optical (EO) devices are usually used as the detection equipment, and area coverage is used as the main search method. However, the sector scanning mode of EO puts forward higher requirements for task parameter planning. First, to ensure there is no missing coverage, a method to determine the full coverage width of EO equipment in sector scanning mode is proposed. Second, the constraint of no interval missing and the model of the speed-to-high ratio constraint are established, and the constraints of other factors are addressed in the context of the problem situation. Third, a coverage efficiency index is proposed for the boustrophedon coverage of a rectangular area, and a comprehensive coverage index is established. Finally, task parameter planning algorithms are designed, based on Immune Algorithm (IA), Grey Wolf Optimization (GWO) and Variable Neighborhood Search (VNS), respectively. The simulation results showed that the designed algorithms, based on IA, GWO and VNS, can effectively solve task planning problems. In general, IA is more suitable for offline occasions, VNS is suitable for online real-time planning, and GWO has characteristics between the two. The coverage process, based on optimized parameters, meets all constraints, has higher search efficiency and does not miss areas, proving the correctness of these models and the effectiveness of the planning algorithm. The research presented in this paper provides a technical basis for efficient and fully automated target search and rescue.

Abstract Background Growing evidence indicates that poor sleep harms health. Early to bed and early to rise is considered as a healthy lifestyle in Chinese population. The current study aimed to examine the effects of sleep habits on acute myocardial infarction (AMI) risk and severity of coronary artery disease (CAD) in Chinese population from two centers. Methods A total of 873 patients including 314 AMI cases and 559 controls were recruited from the inpatient cardiology department of the Affiliated Jiangning Hospital and the First Affiliated Hospital of Nanjing Medical University. 559 controls included 395 CAD cases and 164 non-CAD cases. We used a 17-item sleep factors questionnaire (SFQ) to evaluate sleep habits comprehensively by face-to-face interview. The severity of CAD was assessed by Gensini score in AMI and CAD groups. The effects of sleep factors on AMI risk and Gensini score were examined by unconditional logistic regression. Results After mutually adjustment for other sleep factors and demographic characteristics, the timing of sleep (24:00 and after) and morning waking (after 7:00) and sleep duration ( 65 years, the frequency of night-time waking (3 times) was associated with increased risk of AMI (OR = 3.467, P = 0.035). Short sleep duration was correlated with increased risk of high Gensini score (OR = 2.374, P

Anthropogenic activities cause heavy metal pollution, such as cadmium (Cd). Na+/H+ antiporter (NHX1) transgenic duckweed showed Cd tolerance in our previous study, and the signal mechanism needs to be explored. As an important signal molecule, nitric oxide (NO) is involved in a number of functions under abiotic stress response. This study analyzed the levels of endogenous NO in wild-type (WT) duckweed and NHX1 duckweed under Cd treatment. The results showed that after 24 h Cd treatment, the endogenous NO level of WT duckweed decreased, which was significantly lower than that in NHX1 duckweed. Studies have proved that NHX1 influences pH. The level of NO in this study has been investigated at different pH. The NO level was the highest in the duckweed cultured with pH 5.3. Nitrate reductase gene expression was down-regulated and NO synthesis was decreased under Cd stress in WT duckweed. This study showed that NO level has been modified in NHX1 duckweed, which could be influcened by pH.

Abstract Background The immune responses play significant roles in the onset, progression, and outcome of oral squamous cell carcinoma (OSCC). CD4+ regulatory T cells (Tregs) significantly impact tumor immunity. However, their role in OSCC development remains elusive. Methods In a carcinogen‐induced mouse OSCC model, interleukin‐23 receptor (IL‐23R) expression on Tregs and Treg function were determined by flow cytometry. IL‐23R overexpression in Tregs was achieved by lentiviral infection, followed by evaluation of the expression of Forkhead box P3 (Foxp3), T‐bet, retineic‐acid‐receptor‐related orphan nuclear receptor gamma t, and cytokines by flow cytometry. Adoptive transfer assays were applied to analyze the function of IL‐23R−overexpressing Tregs in vivo. The cellular sources of IL‐23 were also determined by flow cytometry. Results IL‐23R− Tregs and IL‐23R+ Tregs were found in the tongues but not spleens of OSCC‐bearing mice. IL‐23R+ Tregs expressed lower Foxp3 but higher T‐bet than IL‐23R− Tregs. IL‐23R− Tregs produced abundant IL‐10 and transforming growth factor (TGF)‐β, while IL‐23R+ Tregs produced lower IL‐10 and TGF‐β but remarkably higher interferon (IFN)‐γ. Furthermore, IL‐23R+ Tregs possessed more phosphorylated signal transducer and activator of transcription (STAT3) and STAT4 than IL‐23R− Tregs. IL‐23R+ Tregs were less immunosuppressive than IL‐23R− Tregs, as evidenced by weaker inhibition of activated conventional T cells. IL‐23R overexpression in splenic Tregs remarkably reduced the expression of IL‐10 and TGF‐β but increased IFN‐γ expression when Tregs were adoptively transferred into OSCC‐bearing mice. In the OSCC microenvironment, macrophages, dendritic cells, and malignant OSCC cells produced IL‐23 which might modulate the function of IL‐23R+ Tregs. Conclusions This study unveils Treg heterogeneity, thus deepening the understanding of Treg biology and tumor immunity in OSCC.

AbstractThe fractured carbonate rock reservoir is widespread in Sichuan Basin, and the characteristics of different areas are different. The development of natural fractures is varying degrees, lost circulations occur frequently, and the formation heterogeneity is strong, which causes the formation not sufficiently stimulated by acidizing. It may affect the effectiveness of reservoir stimulation. To advance the whole stimulation effect of the heterogeneous fractured carbonate reservoir, a new solution for determining the invasion radius during drilling in a fracture network reservoir is presented, which is based on solute transport and convection-diffusion equations. It can predict the invasion radius caused by mud loss and determines the range of mud loss invasion, which clarify the scope and degree of reservoir damage. The formation of skin factors polluted by mud loss was calculated. The experiments verified that the acidizing technology can remove reservoir damage and reduce the polluted formation of skin factors. The opening pressure of the nature fracture closed is calculated which can control the acidizing area. It is confirmed how many fractures in the carbonate reservoir can be opened under the wellhead pressure limit, which meets the construction conditions of acidizing fractured reservoirs. The framework of network-fracture deep acidizing technology was established, which can efficiently break through the detrimental zone caused by lost circulation, break down the natural fracture network, and decrease the formation of the skin. The restart pressure of natural fractures was calculated, and the design parameters such as pump pressure and displacement were optimized to quantify the scope of reservoir stimulation and the scale of acid fluid. The technique of network-fracture deep acidizing was applied for well A, the formation of skin after acidizing can be reduced to -4, and the testing production of well A was 58.87×104 m3/d. The technique of network-fracture deep acidizing can quantify the acid scale and sweep area in acid fracturing design, which develops the fracturing efficiency and improves the fracturing engineering.