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Abstract Acylation stands as a fundamental process in both biological pathways and synthetic chemical reactions, with acylated saccharides and their derivatives holding diverse applications ranging from bioactive agents to synthetic building blocks. A longstanding objective in organic synthesis has been the site-selective acylation of saccharides without extensive pre-protection of alcohol units. In this study, we demonstrate that by simply altering the chirality of N-heterocyclic carbene (NHC) organic catalysts, the site-selectivity of saccharide acylation reactions can be effectively modulated. Our investigation reveals that this intriguing selectivity shift stems from a combination of factors, including chirality match/mismatch and inter- / intramolecular hydrogen bonding between the NHC catalyst and saccharide substrates. These findings provide valuable insights into catalyst design and reaction engineering, highlighting potential applications in glycoside analysis, such as fluorescent labelling, α/β identification, orthogonal reactions, and selective late-stage modifications.

Abstract. Background:. Lung cancer is the most common cancer and a leading cause of cancer-related deaths globally. The aim of this study was to evaluate the incidence and mortality of lung cancer worldwide in 2022 and to project the number of new cases and deaths due to lung cancer in China and the United States in 2050. Methods:. In this study, data from the GLOBCAN 2022 database were used to analyze lung cancer incidence and mortality. The current status of lung cancer incidence and deaths was described by country/region, sex, age, and the human development index (HDI), and future lung cancer incidence and deaths in China and the United States were projected for 2050. Results:. Globally, an estimated 2,480,675 new lung cancer cases and 1,817,469 lung cancer-related deaths occurred in 2022, with age-standardized incidence rates (ASIRs) and age-standardized mortality rates (ASMRs) of 23.6/100,000 and 16.8/100,000, respectively. In China, the ASIR and ASMR for male lung cancer patients were approximately 1.7 times and 2.7 times greater than those for female lung cancer patients, respectively. The ASIR and ASMR in high-HDI countries were approximately 8.5 times and 6.5 times those in low-HDI countries, respectively. It is estimated that in 2050, there will be approximately 1120 thousand new cases and 960 thousand deaths among Chinese men, 680 thousand new cases and 450 thousand deaths among Chinese women, approximately 170 thousand new cases and 110 thousand deaths among American men, and 160 thousand new cases and 90 thousand deaths among American women. Conclusions:. There are significant differences in the incidence and mortality of lung cancer among different regions and sexes. Therefore, sex factors need to be considered in the prevention, screening, and treatment strategies of lung cancer, and the implementation of tertiary prevention measures for lung cancer, especially primary and secondary prevention, needs to be actively promoted.

Abstract Human‐machine cooperative control has become an important area of intelligent driving, where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision‐making and control. In this paper, an online learning method is proposed for human‐machine cooperative control, which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety conditions. Firstly, a two‐layer LSTM‐based sequence prediction algorithm is proposed to recognise the driver's lane change (LC) intention for human‐machine cooperative steering control. Secondly, an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving safety. The driver‐in‐the‐loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non‐optimal driving actions. The experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL‐based control authority allocation algorithm and its effectiveness in improving driving safety.

Abstract Mechanochemistry studies the effect of mechanical force on chemical bonds, bringing opportunities for synthesizing alloys, ceramics, organics, polymers, and biomaterials. A vital issue of applying macro-scale mechanical force to manipulate crystal structures is finding ways to precisely adjust the force directions to break micro-scale target chemical bonds. Inspired by a common technique of driving a wedge into the wood to make wood chopping much easier, a wedging strategy of splitting three-dimensional structured crystalline frameworks and then converting them to nanosheets was proposed, where specific molecules were wedged into crystalline frameworks to drive the directional transmission of mechanical force to break chemical bonds. As a result, various crystalline framework nanosheets including metal−organic framework nanosheets, covalent organic framework nanosheets, and coordination polymer nanosheets were fabricated. This wedging crystal strategy exhibits advantages of operability, flexibility and designability, and furthermore, it is expected to expand mechanochemistry applications in material preparation.

This study was conducted to explore the aging technology of high-grade beef, a total of 12 high-grade fattening Angus longissimus dorsi were selected and aged in dry or wet environment for 3, 5 and 7 d, and the meat basic physicochemical indexes, free amino acids and volatile flavor compounds were compared and analyzed. The results showed that the pH of beef with dry-aging for 3 and 5 days was significantly lower than that with wet-aging, the pH of beef with dry-aging for 7 d was significantly higher than that with wet-aging (P

Objective Glucocorticoid (GC) overuse is strongly associated with steroid‐induced osteonecrosis of the femoral head (SINFH). However, the underlying mechanism of SINFH remains unclear. This study aims to investigate the effect of dexamethasone (Dex)‐induced oxidative stress on osteocyte apoptosis and the underlying mechanisms. Methods Ten patients with SINFH and 10 patients with developmental dysplasia of the hips (DDH) were enrolled in our study. Sixty rats were randomly assigned to the Control, Dex, Dex + N‐Acetyl‐L‐cysteine (NAC), Dex + Dibenziodolium chloride (DPI), NAC, and DPI groups. Magnetic resonance imaging (MRI) was used to examine edema in the femoral head of rats. Histopathological staining was performed to assess osteonecrosis. Immunofluorescence staining with TUNEL and 8‐OHdG was conducted to evaluate osteocyte apoptosis and oxidative damage. Immunohistochemical staining was carried out to detect the expression of NOX1, NOX2, and NOX4. Viability and apoptosis of MLO‐Y4 cells were measured using the CCK‐8 assay and TUNEL staining. 8‐OHdG staining was conducted to detect oxidative stress. 2′,7′‐Dichlorodihydrofluorescein diacetate (DCFH‐DA) staining was performed to measure reactive oxygen species (ROS). The expression of NOX1, NOX2, and NOX4 in MLO‐Y4 cells was analyzed by Western blotting. Multiple comparisons were performed using one‐way analysis of variance (ANOVA). Results In patients and the rat model, hematoxylin–eosin (HE) staining revealed a significantly higher rate of empty lacunae in the SINFH group than in the DDH group. Immunofluorescence staining indicated a significant increase in TUNEL‐positive cells and 8‐OHdG‐positive cells in the SINFH group compared to the DDH group. Immunohistochemical staining demonstrated a significant increase in the expression of NOX1, NOX2, and NOX4 proteins in SINFH patients compared to DDH patients. Moreover, immunohistochemical staining showed a significant increase in the proportion of NOX2‐positive cells compared to the Control group in the femoral head of rats. In vitro, Dex significantly inhibited the viability of osteocyte cells and induced apoptosis. After Dex treatment, the intracellular ROS level increased. However, Dex treatment did not alter the expression of NOX proteins in vitro. Additionally, NAC and DPI inhibited the generation of intracellular ROS and partially alleviated osteocyte apoptosis in vivo and in vitro. Conclusion This study demonstrates that GC promotes apoptosis of osteocyte cells through ROS‐induced oxidative stress. Furthermore, we found that the increased expression of NOXs induced by GC serves as an important source of ROS generation.

Abstract Although previous studies have made some clear leap in learning latent dynamics from high‐dimensional representations, the performances in terms of accuracy and inference time of long‐term model prediction still need to be improved. In this study, a deep convolutional network based on the Koopman operator (CKNet) is proposed to model non‐linear systems with pixel‐level measurements for long‐term prediction. CKNet adopts an autoencoder network architecture, consisting of an encoder to generate latent states and a linear dynamical model (i.e., the Koopman operator) which evolves in the latent state space spanned by the encoder. The decoder is used to recover images from latent states. According to a multi‐step ahead prediction loss function, the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini‐batch manner. In this manner, gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self‐adaptively tune the latent state space in the training process, and the resulting model is time‐invariant in the latent space. Therefore, the proposed CKNet has the advantages of less inference time and high accuracy for long‐term prediction. Experiments are performed on OpenAI Gym and Mujoco environments, including two and four non‐linear forced dynamical systems with continuous action spaces. The experimental results show that CKNet has strong long‐term prediction capabilities with sufficient precision.

Reconstructing the three-dimensional thermal structure of the ocean under typhoon conditions presents significant challenges due to the scarcity of observational data, particularly in subsurface regions, and the limitations of existing observation methods in terms of spatial and temporal resolution. Accurate reconstruction of these structures is crucial for understanding the energy exchange between the ocean and typhoons, as this exchange directly influences typhoon intensity and trajectory. To address these challenges, this study introduces a fully connected transformer network (FCT), which integrates fully connected layers with a transformer model. The FCT model leverages the attention mechanisms inherent in the transformer architecture to effectively extract and integrate multi-scale ocean dynamical features. Using data from Typhoon Lekima in 2019, this study reconstructs ocean thermal structures at various depths and achieves an RMSE of 1.03 °C and an MAE of 0.83 °C when validated against Argo data. Furthermore, the model’s robustness was demonstrated through five-fold cross-validation, with the validation loss exhibiting minor fluctuations across folds but remaining stable overall, with an average validation loss of 0.986 °C, indicating the model’s generalizability. Sensitivity analysis also revealed the model’s resilience to variations in key input variables, showing minimal impact on output even with perturbations of up to 10% in input data. In addition, the study incorporates content analysis of typhoon-related news reports from 2011 to 2020, revealing a predominance of political topics, which underscores the central role of government in disaster response, with economic and ecological topics following. This integrated approach not only enhances our understanding of the interactions between ocean thermal structures and typhoon dynamics but also provides valuable insights into the societal impacts of typhoons, as reflected in media coverage, contributing to improved disaster management strategies.

Abstract Background The relationship between aging and osteoporosis is well established. However, the relationship between the body's physiological age, i.e. epigenetic age, and osteoporosis is not known. Our goal is to analyze the bidirectional causal relationship between epigenetic clocks and osteoporosis using a bidirectional Mendelian randomization study. Methods We used SNPs closely associated with GrimAge, Hannum, PhenoAge, and HorvathAge in epigenetic age and SNPs closely associated with femoral neck bone mineral density, lumbar spine bone mineral density, and forearm bone mineral density as instrumental variables, respectively, using the inverse variance weighting method and several other MR methods to assess the bidirectional causal relationship between epigenetic age and osteoporosis. Result There was no evidence of a clear causal relationship of epigenetic age (GrimAge, Hannum, PhenoAge, and HorvathAge) on femoral neck bone mineral density, lumbar spine bone mineral density, and forearm bone mineral density. In reverse Mendelian randomization analysis showed a significant causal effect of lumbar spine bone mineral density on GrimAge: odds ratio (OR) = 0.692, 95% confidence interval (CI) = (0.538–0.890), p = 0.004. The results suggest that a decrease in lumbar spine bone mineral density promotes an acceleration of GrimAge. Conclusion There was no significant bidirectional causal relationship between epigenetic age and osteoporosis A decrease in lumbar spine bone density may lead to an acceleration of the epigenetic clock "GrimAge". Our study provides partial evidence for a bidirectional causal effect between epigenetic age and Osteoporosis.

Abstract Hydrogen spillover is the migration of activated hydrogen atoms from a metal particle onto the surface of catalyst support, which has made significant progress in heterogeneous catalysis. The phenomenon has been well researched on oxide supports, yet its occurrence, detection method and mechanism on non-oxide supports such as metal–organic frameworks (MOFs) remain controversial. Herein, we develop a facile strategy for efficiency enhancement of hydrogen spillover on various MOFs with the aid of water molecules. By encapsulating platinum (Pt) nanoparticles in MOF-801 for activating hydrogen and hydrogenation of C=C in the MOF ligand as activated hydrogen detector, a research platform is built with Pt@MOF-801 to measure the hydrogenation region for quantifying the efficiency and spatial extent of hydrogen spillover. A water-assisted hydrogen spillover path is found with lower migration energy barrier than the traditional spillover path via ligand. The synergy of the two paths explains a significant boost of hydrogen spillover in MOF-801 from imperceptible existence to spanning at least 100-nm-diameter region. Moreover, such strategy shows universality in different MOF and covalent organic framework materials for efficiency promotion of hydrogen spillover and improvement of catalytic activity and antitoxicity, opening up new horizons for catalyst design in porous crystalline materials.

Metal-organic frameworks (MOFs) have been widely considered as ideal platforms for the preparation of biomimetic catalysts, but it remains challenging to fabricate MOF-based enzyme-like catalysts with optimal activity. Here, we leverage the inherent flexibility of MOFs and propose a novel trans-functionalization strategy to construct a carbonic anhydrase (CA) mimic by the structural transformation from ZIF-L to ZIF-8. Theoretical and experimental results reveal that during the structural transformation, the hydroxyl group will preferentially coordinate with the interlayer Zn clusters to form the CA-like active center Zn-N3-OH. Therefore, more accessible active centers are generated on the as-prepared ZIF-8-OH, resulting in substantially enhanced catalytic activity in the hydrolysis of para-nitrophenyl acetate.

Fault detection and diagnosis technologies for unmanned ground vehicles are important for ensuring safety and reliability. Due to the complexity and uncertainty of unmanned ground vehicles, it is challenging to realize accurate and fast fault detection and diagnosis. For the purpose of solving the data-driven fault detection and diagnosis problems of unmanned ground vehicles, improving the diagnostic accuracy and shortening the training time, a novel fault detection and diagnosis method is proposed, which is called JITGP-ELM. In the proposed method, a model estimator based on the just-in-time Gaussian process is designed for the online residual generation to cope with the dynamics and nonlinearity of systems. A fault classifier using Extreme Learning Machine is designed for fault identification with residuals extracted by the just-in-time Gaussian process modelling. The proposed method has online adaptability, noise-resistant ability, and high generalization. A field test on a real unmanned ground vehicle's steering-by-wire system demonstrates the effectiveness of the proposed method.

The purpose of this meta‐analysis was to identify if patient‐specific instrumentation (PSI) could increase the accuracy of the correction in high tibial osteotomy (HTO) and to explore the assessment indices and the necessity of using a PSI in HTO. A systematic search was carried out using online databases. A total of 466 patients were included in 11 papers that matched the inclusion criteria. To evaluate the accuracy of PSI‐assisted HTO, the weight bearing line ratio (WBL%), hip‐knee‐ankle angle (HKA), mechanical medial proximal tibial angle (mMPTA), and posterior tibial slope angle (PTSA) were measured preoperatively and postoperatively and compared to the designed target values. Statistical analysis was performed after strict data extraction with Review Manager (version 5.4). Significant differences were detected in WBL% (MD = −36.41; 95% CI: −42.30 to −30.53; p

Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning.

IntroductionThe causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study.MethodsWe conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR–Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran’s Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method.ResultsThe primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses.ConclusionThe results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent.

Nitriles have proven to be useful functional groups in pharmaceuticals and polymers, both as active moieties themselves or as easy precursors to further transformations, but synthesis of benzonitriles with axial chirality has often been difficult. Here the authors make axially chiral benzonitriles via N-heterocyclic carbene organocatalysis via a dynamic kinetic resolution process.

Intelligent operation is the core business of the intelligent pipeline network system, and the assistant decision system is the backbone supporting the intelligent operation of pipeline. Based on the top-level design and overall framework of the former PetroChina Pipeline Co. Ltd. for intelligent pipeline network, the intelligent operation and development scheme was piloted on China-Myanmar Crude Oil Pipeline based on its characteristics of large terrain undulation, complex geological conditions and the sensitive surrounding environment. By analyzing the construction framework and practice of intelligent pipeline of the former PetroChina Pipeline Co. Ltd. and foreign countries, the development scheme of assistant decision system, comprising operation management, safety control and full life cycle integrity management, was put forward based on the building of IoT sharing system, digital restoration system and cloud data center. Definitely, the development difficulties were analyzed and the technical route of overall planning and step-by-step implementation was discussed, to achieve the connotation goals of the intelligent operation of pipeline including real-time autonomous optimization of pipeline operation management, controllable safety prediction and early warning, and high intelligence of full life cycle integrity management.

As Lancang River crossing is the "throat project" of the entire China-Myanmar Crude Oil Pipeline, it is very important to guarantee its safe operation. Based on the outline of the two-dimensional river map of the downstream of the Lancang River crossing section, a two-dimensional calculation domain for the downstream river course of the oil spill accident in the crossing section was established. Using the SST k-omega turbulence model and combined with the VOF method of tracking the multiphase flow interface, simulation calculations were performed for a total of 12 leakage scenarios of different river water periods, different leakage locations, and different leakage modes. In the 12 leakage scenarios, the amount of crude oil leakage in the Lancang River was considered as the maximum, the diffusion and distribution of crude oil on the surface of the river and the diffusion distance of the crude oil in the direction of the river were analyzed, and the migration and diffusion laws of crude oil in Lancang River with the influence of various factors were summarized. The simulation results are of strong reference significance for predicting the location of crude oil diffusion and the release of oil booms on site, and could provide strong technical support for emergency treatment of oil spills and environment protection in the Lancang River crossing section.

Machine learning and computational intelligence are currently high-profile research areas attracting the attention of many researchers [...]

Abstract With the development of flexible devices, the demand for wearable power sources has increased and gradually become imperative. Zinc–air batteries (ZABs) have attracted lots of research interest due to their high theoretical energy density and excellent safety properties, which can meet the wearable energy supply requirements. Here, the flexibility of energy storage devices is discussed first, followed by the chemistries and development of flexible ZABs. The design of flexible electrodes, the properties of solid‐state electrolytes (SSEs), and the construction of deformable structures are discussed in depth. The researchers working on flexible energy storage devices will benefit from the work.

In the process of unmanned aerial vehicle (UAV) visual-navigation-algorithm design and accuracy verification, the question of how to develop a high-precision and high-reliability semi-physical simulation platform has become a significant engineering problem. In this study, a new UAV semi-physical-simulation-platform architecture is proposed, which includes a six-degree-of-freedom mechanical structure, a real-time control system and real-time animation-simulation software. The mechanical structure can realistically simulate the flight attitude of a UAV in a three-dimensional space of 4 × 2 × 1.4 m. Based on the designed mechanical structure and its dynamics, the control system and the UAV real-time flight-animation simulation were designed. Compared with the conventional simulation system, this system enables real-time flight-attitude simulation in a real physical environment and simultaneous flight-attitude simulation in virtual-animation space. The test results show that the repeated positioning accuracy of the three-axis rotary table reaches 0.006°, the repeated positioning accuracy of the three-axis translation table reaches 0.033 mm, and the dynamic-positioning accuracy reaches 0.04° and 0.4 mm, which meets the simulation requirements of high-precision visual UAV navigation.

Today, the more electric aircraft (MEA) concept is gaining tremendous popularity. As a key component of the MEA, a more electric auxiliary power unit (MEAPU) integrated model with high accuracy and real-time performance is essential when conducting cooperative control and hardware-in-the-loop (HIL) test research. This paper proposes a novel MEAPU integrated model consisting of a MEAPU component-level-model (CLM) and a starter-generator (SG) model. Firstly, a MEAPU CLM was built and a continuous scaling method for the component characteristic map in the CLM is proposed to improve the model’s accuracy. Then, a double winding induction starter-generator (DWISG) model based on the electromagnetic theory, which is quite time consuming, was simplified using the pulse width modulation (PWM) rectifier linearization method. Finally, considering the coupling relationship between the MEAPU CLM and DWISG, an accurate real-time MEAPU integrated model was built and its simulation results were analyzed. Compared with the test results, the error of the proposed model was less than 0.5%; meanwhile its single-step simulation time was less than 20 ms, which can meet the demands of cooperative control and HIL test research. Furthermore, the continuous scaling method and PWM rectifier linearization method were found to be effective for modeling other MEAPUs and more electric engines (MEE).

Influenza viruses usually cause seasonal influenza epidemics and influenza pandemics, resulting in acute respiratory illness and, in severe cases, multiple organ complications and even death, posing a serious global and human health burden. Compared with other countries, China has a large population base and a large number of influenza cases and deaths. Currently, influenza vaccination remains the most cost-effective and efficient way to prevent and control influenza, which can significantly reduce the risk of influenza virus infection and serious complications. The antigenicity of the influenza vaccine exhibits good protective efficacy when matched to the seasonal epidemic strain. However, when influenza viruses undergo rapid and sustained antigenic drift resulting in a mismatch between the vaccine strain and the epidemic strain, the protective effect is greatly reduced. As a result, the flu vaccine must be reformulated and readministered annually, causing a significant drain on human and financial resources. Therefore, the development of a universal influenza vaccine is necessary for the complete fight against the influenza virus. By statistically analyzing cases related to influenza virus infection and death in China in recent years, this paper describes the existing marketed vaccines, vaccine distribution and vaccination in China and summarizes the candidate immunogens designed based on the structure of influenza virus, hoping to provide ideas for the design and development of new influenza vaccines in the future.

Autonomous decision making and motion planning in complex dynamic traffic environments, such as left-turn without traffic signals and multi-lane merging from side-ways, are still challenging tasks for intelligent vehicles. It is difficult to generate optimized behavior decisions while considering the motion capabilities and dynamic properties of intelligent vehicles. Aiming at the above problems, this article proposes a hierarchical reinforcement learning approach for autonomous decision making and motion planning in complex dynamic traffic scenarios. The proposed approach consists of two layers. At the higher layer, a kernel-based least-squares policy iteration algorithm with uneven sampling and pooling strategy (USP-KLSPI) is presented for solving the decision-making problems. The motion capabilities of the ego vehicle and the surrounding vehicles are evaluated with a high-fidelity dynamic model in the decision-making layer. By doing so, the consistency between the decisions generated at the higher layer and the operations in the lower planning layer can be well guaranteed. The lower layer addresses the motion-planning problem in the lateral direction using a dual heuristic programming (DHP) algorithm learned in a batch-mode manner, while the velocity profile in the longitudinal direction is inherited from the higher layer. Extensive simulations are conducted in complex traffic conditions including left-turn without traffic signals and multi-lane merging from side-ways scenarios. The results demonstrate the effectiveness and efficiency of the proposed approach in realizing optimized decision making and motion planning in complex environments.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently widespread throughout the world, accompanied by a rising number of people infected and breakthrough infection of variants, which make the virus highly transmissible and replicable. A comprehensive understanding of the molecular virological events and induced immunological features during SARS-CoV-2 replication can provide reliable targets for vaccine and drug development. Among the potential targets, subgenomic RNAs and their encoded proteins involved in the life cycle of SARS-CoV-2 are extremely important in viral duplication and pathogenesis. Subgenomic RNAs employ a range of coping strategies to evade immune surveillance from replication to translation, which allows RNAs to synthesize quickly, encode structural proteins efficiently and complete the entire process of virus replication and assembly successfully. This review focuses on the characteristics and functions of SARS-CoV-2 subgenomic RNAs and their encoded proteins and explores in depth the role of subgenomic RNAs in the replication and infection of host cells to provide important clues to the mechanism of COVID-19 pathogenesis.

Intelligent vehicle-following control presents a great challenge in autonomous driving. In vehicle-intensive roads of city environments, frequent starting and stopping of vehicles is one of the important cause of front-end collision accidents. Therefore, this paper proposes a subsection proximal policy optimization method (Subsection-PPO), which divides the vehicle-following process into the start–stop and steady stages and provides control at different stages with two different actor networks. It improves security in the vehicle-following control using the proximal policy optimization algorithm. To improve the training efficiency and reduce the variance of advantage function, the weighted importance sampling method is employed instead of the importance sampling method to estimate the data distribution. Finally, based on the TORCS simulation engine, the advantages and robustness of the method in vehicle-following control is verified. The results show that compared with other deep learning learning, the Subsection-PPO algorithm has better algorithm efficiency and higher safety than PPO and DDPG in vehicle-following control.

Medical data contains multiple records of patient data that are important for subsequent treatment and future research. However, it needs to be stored and shared securely to protect the privacy of the data. Blockchain is widely used in the management of healthcare data because of its decentralized and tamper-proof features. In order to study the development of blockchain in healthcare, this paper evaluates it from various perspectives. We analyze blockchain-based approaches from different application scenarios. These are blockchain-based electronic medical record sharing, blockchain and the Internet of Medical Things and blockchain-based federal learning. The results show that blockchain and smart contracts have a natural advantage in the field of medical data since they are tamper-proof and traceable. Finally, the challenges and future directions of blockchain in healthcare are discussed, which can help drive the field forward.

Aim: Isocitrate dehydrogenase 1 (IDH1) is key enzyme involved in cellular metabolism and DNA repair. Mutations in IDH1 occur in up to 25% of cholangiocarcinomas. The present study aimed to explore the features of cellosaurus REB cells with mutant and wide-type IDH1.Methods: To compare the features of IDH1 knockout and mutation in cholangiocarcinoma, we firstly constructed the IDH1 knockout and IDH1 mutation cell lines. We then evaluated the viability of these cell lines using the cell count assay and MTT assay. Next, we determined cell migration and invasion using the Transwell assay. Additionally, to evaluate the effects of IDH1 on cellular metabolism, the levels of α-ketoglutarate (α-KG) and nicotinamide adenine dinucleotide phosphate (NADPH) were determined using enzyme-linked immunosorbent assay. We then applied ChIPbase dataset to explore the genes that were regulated by IDH1.Results: High frequency of mutated IDH1 was observed in the cholangiocarcinoma and IDH1 R132C was presented in more than 80% of mutations. The results showed that IDH1 knockout decreased cell proliferation, migration and invasion, whereas the overexpression of IDH1 in IDH1 knockout cell line recovered its proliferation, migration and invasion capacities. Additionally, IDH1 mutation reduced the levels of NADPH and α-KG. Furthermore, investigation into the underlying mechanisms revealed that IDH1 overexpression induced the expression of aldehyde dehydrogenase 1 thereby promoting cell proliferation, migration and invasion.Conclusion:IDH1 plays an important role in cholangiocarcinoma and its mutation impairs tumor progression in part by inhibition of isocitrate metabolism.

The electro-hydrostatic actuator (EHA) has a high energy density and a compact structure so it is widely used in all/more electric aircraft. To gain energy-saving capability, the hydraulic lock can be applied in the EHA system, which can overcome the change of load after the position control is complete, reducing the motor’s need to work continuously. Due to the use of a hydraulic lock in the EHA system, there is a more significant position output delay compared with the EHA system. The delay would lead to a dynamic response error. Therefore, an electro-hydrostatic actuator with a bidirectional hydraulic lock (BHL-EHA) model is established that can reflect the position output delay and it can be used to analyze its influence factors. According to the analysis, the factors affecting the delay are pump speed, external load, hydraulic lock cracking pressure, friction load, pump displacement, fluid volume of the system, and effective bulk modulus; thus, an equation that can be used to calculate the delay is proposed. At the same time, the experimental system of the BHL-EHA system is established, and the parameters of the experimental facility are completely consistent with the simulation model. Finally, a delay compensation control method is proposed, and the control time parameter t0 is calculated based on the equation proposed above. The results of the simulation and the experiment show that the method can compensate for the delay caused by the hydraulic lock. Combined with a feedforward control, the method can dismiss the dynamic response error of 0.6 Hz sinusoidal tracking characteristics.

This paper presents a fault-tolerant control scheme for the sensor fault in the acceleration process of the variable cycle engine. Firstly, an adaptive equilibrium manifold model with multiple inputs and multiple outputs is established. Combined with the Kalman filter bank, sensor fault diagnosis is carried out to realize the diagnosis and signal reconstruction of the engine in the case of a single sensor and double sensor faults. On this basis, isolation and group isolation are used to diagnose sensor faults and reconstruct signal in speed closed-loop control. Then, the control plan of the acceleration process is optimized based on the target shooting method, aiming to simulate the variation of various variables in the engine acceleration process more accurately, so as to verify the feasibility of the sensor fault-tolerant control scheme. Finally, a hardware-in-loop simulation platform is built based on the idea of distributed control, and the fault-tolerant control scheme of the sensor proposed previously is verified based on this platform. The results show that the proposed scheme can accurately diagnose the sensor faults and reconstruct the signal within 0.2 s, and the actual speed can rise from 67.87% to 99.9% in 4 s, ensuring the safe and rapid completion of the acceleration process.

The main data source for the verification of surge detection methods still rely on test rigs of the compressor or the whole engine, which makes the development of models of the whole engine surge process an urgent need to replace the high-cost and high-risk surge test. In this paper, a novel real-time surge model based on the surge mechanism is proposed. Firstly, the turboshaft engine component level model (CLM) and the classic surge dynamic model, Moore-Greitzer (MG) model is established. Then the stability of the MG model is analyzed and the compressor characteristics in the classical MG model are extended to establish the extended MG model. Finally, this paper considers the coupling relationship of the compressor’s rotor speed, mass flow and pressure between CLM and the extended MG model to establish the real-time model of the turboshaft engine with surge process. The simulation results show that this model can realize the whole surge process of the turboshaft engine under multiple operating states. The change characteristics of the rotor speed, compressor outlet pressure, mass flow, exhaust gas temperature and other parameters are consistent with the test data, which means that the model proposed can be further applied to the research of surge detection and anti-surge control.

This paper presents a fault tolerant control (FTC) design for the actuator faults in a variable cycle engine (VCE). Ensured by the multiple variable geometries structure of VCE, the design is realized by distributing the control effort among the unfaulty actuators with the “functional redundancy” idea. The FTC design consists of two parts: the fault reconstruction part and the fault tolerant control part, which use a sliding mode observer (SMO) and a sliding mode control (SMC) scheme respectively. Considering the inaccuracy of the fault reconstruction result, the proposed design requires only inaccurate fault information. The stability of the closed-loop control system is proved and the existence condition for the proposed control law is analyzed. This work also reveals its relation to the sliding mode control allocation design and the adaptive SMC design. An application case is then studied for tolerating the loss of effectiveness fault of the nozzle area actuator. Results show that the FTC design is able to tolerate the fault and achieves the same control goal as in the fault-free situation. Finally, a hardware-in-the-loop test is carried out to verify the design in a real-time distributed control system, which demonstrates its use from the engineering perspective.

Polyphenol oxidase (PPO) and peroxidase (POD) were extracted from a new loquat fruit cultivar (Ninghaibai) and characterised using reliable spectrophotometric methods. In both cases, the optimum pH for PPO was 4.5 and 5 for POD, and their optimum temperatures were 30 and 35°C, respectively. Both enzymes followed Michaelis-Menten kinetics, showing Km= 47.11 mmol/l for PPO with catechol as the substrate and Km = 153.00 mmol/l for POD with guaiacol as the substrate. PPO was much more thermolabile than POD, losing more than 40% of relative activity after 30 min of heating at 40°C. PPO activation energy was much lower than POD activation energy (ΔE# = 39.74 and 94.65 kJ/mol for PPO and POD, respectively): Both enzymes activities showed decreasing patterns as the compound concentration in the assay medium increased. 4-hexylresorcinol (4-HR), oxalic acid, and l-cysteine showed strongly inhibitive effects on the enzymes. Changes in L*, a*, and b* values were chosen to describe the browning of loquat pulp. Only PPO displayed a higher negative correlation with L* values, which indicated that PPO plays an important role in the browning of stored loquat cv. Ninghaibai.

The action of fluoroacetate as a broad-spectrum mammalian pesticide depends on the ‘lethal synthesis’ of fluorocitrate by citrate synthase, through a subtle enantioselective enolization of fluoroacetyl-coenzyme A. In this work, we demonstrate how a projection-based embedding method can be applied to calculate coupled cluster (CCSD(T)) reaction profiles from quantum mechanics/molecular mechanics optimized pathways for this enzyme reaction. Comparison of pro-R and pro-S proton abstraction in citrate synthase at the CCSD(T)-in-DFT//MM level yields the correct enantioselectivity. We thus demonstrate the potential of projection-based embedding for determining stereoselectivity in enzymatic systems. We further show that the method is simple to apply, eliminates variability due to the choice of density functional theory functional and allows the efficient calculation of CCSD(T) quality enzyme reaction barriers.

The application of thermal energy storage (TES) is an effective way of improving the power load regulation capability of combined heat and power (CHP) generating units. In this paper, a theoretical investigation on the thermal energy storage system of a CHP unit that employs the microencapsulated phase change material slurry (MPCMS) as the working fluid is carried out. The results indicate that the microcapsule particle internal melting rate is progressively small; 90% latent heat can be absorbed in 63% total melting time. The melting time of particles in micron is very short, and the diameter is an important factor for microcapsule melting. For the MPCMS flow in a circular tube, the temperature distribution between laminar flows and turbulent flows is different. In a turbulent flow, there is an approximate isothermal section along the tube, which cannot be found in a laminar flow. Additionally, a thermal storage system with MPCMS as heat transfer fluid for a CHP unit is proposed. A case study for a 300 MW CHP unit found that the use of an MPSMS thermal energy storage system increases the power peak shaving capacity by 81.4%. This indicates that the thermal storage system increases the peak shaving capacity of cogeneration units.