Mature T-and natural killer(NK)-cell lymphomas are heterogeneous groups of malignant lymphoid neoplasms arising from T and NK cells. The incidence of mature T-and NK-cell lymphomas is 2.1 per 100,000 people, according...Mature T-and natural killer(NK)-cell lymphomas are heterogeneous groups of malignant lymphoid neoplasms arising from T and NK cells. The incidence of mature T-and NK-cell lymphomas is 2.1 per 100,000 people, according to a US report~1.展开更多
Substance use disorders(SUDs)impact an estimated 300 million people worldwide,significantly impairing both health and social functioning.These disorders are marked by an inability to regulate substance use,despite the...Substance use disorders(SUDs)impact an estimated 300 million people worldwide,significantly impairing both health and social functioning.These disorders are marked by an inability to regulate substance use,despite the harmful consequences.Addiction affects various neurotransmitter systems,including dopamine,serotonin,γ-aminobutyric acid(GABA),and glutamate,each of which plays a role in the reward,stress,and self-control pathways of the brain(Koob&Volkow,2016).While significant advances have been made in neuroscience,our understanding of how these neurotransmitter systems interact and contribute to addiction is still evolving.This knowledge gap represents a significant challenge in the formulation of effective treatments for SUDs.At present,the US Food and Drug Administration(FDA)has approved pharmacological treatments for alcohol,nicotine,and opioid use disorders(Vasiliu,2022);however,no such treatments have been authorized for SUDs in general,or specifically for stimulant use disorders,such as cocaine and methamphetamine addiction.Notably,the FDA has not approved any new drugs for SUD treatment in the past 40 years.展开更多
Drug addiction refers to a state of dependence that arises from habitual drug intake and can result in specific withdrawal symptoms upon cessation.The most commonly abused substances include psychostimulants,cannabino...Drug addiction refers to a state of dependence that arises from habitual drug intake and can result in specific withdrawal symptoms upon cessation.The most commonly abused substances include psychostimulants,cannabinoids,and opioids.When drugs are consumed,they stimulate the release of dopamine,a neurotransmitter crucial for the pleasure and reward centers of the brain.With repeated drug use,the brain undergoes various changes,leading to tolerance,dependence,and addiction(Lüscher et al.,2020).The mechanisms involved in drug addiction are highly complex and involve diverse cell types within the brain.展开更多
Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the chall...Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the challenges of long-term preparation,high costs and associated risks.Experimental studies,on the other hand,offer a safe and cost-effective means of exploring the mechanisms of hydrate dissociation and optimizing exploitation conditions.Gas hydrate decomposition is a complicated process along with intrinsic kinetics,mass transfer and heat transfer,which are the influencing factors for hydrate decomposition rate.The identification of the rate-limiting factor for hydrate dissociation during depressurization varies with the scale of the reservoir,making it challenging to extrapolate findings from laboratory experiments to the actual exploitation.This review aims to summarize current knowledge of investigations on hydrate decomposition on the subject of the research scale(core scale,middle scale,large scale and field tests)and to analyze determining factors for decomposition rate,considering the various research scales and their associated influencing factors.展开更多
The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and ...The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.展开更多
The kinematic equivalent model of an existing ankle-rehabilitation robot is inconsistent with the anatomical structure of the human ankle,which influences the rehabilitation effect.Therefore,this study equates the hum...The kinematic equivalent model of an existing ankle-rehabilitation robot is inconsistent with the anatomical structure of the human ankle,which influences the rehabilitation effect.Therefore,this study equates the human ankle to the UR model and proposes a novel three degrees of freedom(3-DOF)generalized spherical parallel mechanism for ankle rehabilitation.The parallel mechanism has two spherical centers corresponding to the rotation centers of tibiotalar and subtalar joints.Using screw theory,the mobility of the parallel mechanism,which meets the requirements of the human ankle,is analyzed.The inverse kinematics are presented,and singularities are identified based on the Jacobian matrix.The workspaces of the parallel mechanism are obtained through the search method and compared with the motion range of the human ankle,which shows that the parallel mechanism can meet the motion demand of ankle rehabilitation.Additionally,based on the motion-force transmissibility,the performance atlases are plotted in the parameter optimal design space,and the optimum parameter is obtained according to the demands of practical applications.The results show that the parallel mechanism can meet the motion requirements of ankle rehabilitation and has excellent kinematic performance in its rehabilitation range,which provides a theoretical basis for the prototype design and experimental verification.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts(SACs)is crucial for catalyzing the CO_(2)reduction reaction(CO_(2)RR).However,it rem...The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts(SACs)is crucial for catalyzing the CO_(2)reduction reaction(CO_(2)RR).However,it remains a major challenge.While density-functional theory calculations serve as a powerful tool for catalyst screening,their time-consuming nature poses limitations.This paper presents a machine learning(ML)model based on easily accessible intrinsic descriptors to enable rapid,cost-effective,and high-throughput screening of efficient SACs in complex systems.Our ML model comprehensively captures the influences of interactions between 3 and 5d metal centers and 8 C,N-based coordination environments on CO_(2)RR activity and selectivity.We reveal the electronic origin of the different activity trends observed in early and late transition metals during coordination with N atoms.The extreme gradient boosting regression model shows optimal performance in predicting binding energy and limiting potential for both HCOOH and CO production.We confirm that the product of the electronegativity and the valence electron number of metals,the radius of metals,and the average electronegativity of neighboring coordination atoms are the critical intrinsic factors determining CO_(2)RR activity.Our developed ML models successfully predict several high-performance SACs beyond the existing database,demonstrating their potential applicability to other systems.This work provides insights into the low-cost and rational design of high-performance SACs.展开更多
This study focuses on a DN50 pipeline-type Savonius hydraulic turbine.The torque variation of the turbine in a rotation cycle is analyzed theoretically in the framework of the plane potential flow theory.Related numer...This study focuses on a DN50 pipeline-type Savonius hydraulic turbine.The torque variation of the turbine in a rotation cycle is analyzed theoretically in the framework of the plane potential flow theory.Related numerical simulations show that the change in turbine torque is consistent with the theoretical analysis,with the main power zone and the secondary power zone exhibiting a positive torque.In contrast,the primary resistance zone and the secondary resistance zone are characterized by a negative torque.Analytical relationships between the turbine’s internal flow angleθ,the deflector’s inclination angleα0,and the coverage angleαof the power zone are introduced,and a method for calculating the optimal number of blades is proposed to maximize the power zone.Results are presented about performance tests conducted on five groups of hydraulic turbines with the blade number ranging from 3 to 7.Such results indicate that both the turbine’s recovery power and efficiency attain the highest values when the blade number is 4,which is in agreement with the number of blades calculated by the proposed method.Additionally,the study examines the effects of the flow rate on turbine parameters and the projected energy generation and cost savings for a specific pipeline configuration.展开更多
Objective:This paper focuses on the research and discussion of the efficacy of electromyographic biofeedback combined with swallowing training on post-stroke dysphagia.Methods:This study randomly sampled and analyzed ...Objective:This paper focuses on the research and discussion of the efficacy of electromyographic biofeedback combined with swallowing training on post-stroke dysphagia.Methods:This study randomly sampled and analyzed 68 patients with post-stroke dysphagia from January 2023 to December 2023,34 cases of swallowing training intervention were grouped as the control group,and 34 cases of electromyography biofeedback combined with swallowing training intervention were grouped as the study group,and the clinical therapeutic effects of the two groups of patients after receiving the two different modes of intervention were compared.Results:The swallowing function of patients in both groups improved,and the VFSS score of patients in the seminar group was significantly higher than that of the control group,indicating that the clinical efficacy of the seminar group was more significant.The nasal feeding tube extraction rate,extraction time,and quality of life scores of the seminar group were better than those of the control group(P<0.05),which is of research value.Conclusion:For patients with post-stroke dysphagia,treatment with electromyography biofeedback combined with swallowing training mode can significantly improve their swallowing function.This effective intervention can not only shorten the time for patients to remove the nasal feeding tube but also help to improve the quality of life of patients,which is worth using.展开更多
Although their cost-effectiveness and intrinsic safety,aqueous zinc-ion batteries suffer from notorious side reactions including hydrogen evolution reaction,Zn corrosion and passivation,and Zn dendrite formation on th...Although their cost-effectiveness and intrinsic safety,aqueous zinc-ion batteries suffer from notorious side reactions including hydrogen evolution reaction,Zn corrosion and passivation,and Zn dendrite formation on the anode.Despite numerous strategies to alleviate these side reactions have been demonstrated,they can only provide limited performance improvement from a single aspect.Herein,a triple-functional additive with trace amounts,ammonium hydroxide,was demonstrated to comprehensively protect zinc anodes.The results show that the shift of electrolyte pH from 4.1 to 5.2 lowers the HER potential and encourages the in situ formation of a uniform ZHS-based solid electrolyte interphase on Zn anodes.Moreover,cationic NH^(4+)can preferentially adsorb on the Zn anode surface to shield the“tip effect”and homogenize the electric field.Benefitting from this comprehensive protection,dendrite-free Zn deposition and highly reversible Zn plating/stripping behaviors were realized.Besides,improved electrochemical performances can also be achieved in Zn//MnO_(2)full cells by taking the advantages of this triple-functional additive.This work provides a new strategy for stabilizing Zn anodes from a comprehensive perspective.展开更多
With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provi...With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.展开更多
The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its...The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .展开更多
Saponins are the main triterpenoid ingredients from Panax notoginseng,a well-known Chinese medicine,and are important sources for producing drugs to prevent and treat cerebrovascular and cardiovascular diseases.Howeve...Saponins are the main triterpenoid ingredients from Panax notoginseng,a well-known Chinese medicine,and are important sources for producing drugs to prevent and treat cerebrovascular and cardiovascular diseases.However,the transcriptional regulatory network of saponin biosynthesis in P.notoginseng is largely unknown.In the present study we demonstrated that one R2R3-MYB transcription factor,designated PnMYB4,acts as a repressor of saponin accumulation.Suppression of PnMYB4 in P.notoginseng calli significantly increased the saponin content and the expression level of saponin biosynthetic genes.PnMYB4 directly bound to the promoters of key saponin biosynthetic genes,including PnSS,PnSE,and PnDS,to repress saponin accumulation.PnMYB4 and the activator PnMYB1 could inter-acted with PnbHLH,which is a positive regulator of saponin biosynthesis,to modulate the biosynthesis of saponin.PnMYB4 competed with PnMYB1 for binding to PnbHLH,repressing activation of the promoters of saponin structural genes induced by the PnMYB1-PnbHLH complex.Our study reveals that a complex regulatory module of saponin biosynthesis is associated with positive and negative MYB transcriptional regulators and provides a theoretical basis for improving the content of saponins and efficacy of P.notoginseng.展开更多
Objective:CAR-T/NK cells have had limited success in the treatment of solid tumors,such as colorectal cancer(CRC),in part because of the heterogeneous nature of tumor-associated antigens that lead to antigen-negative ...Objective:CAR-T/NK cells have had limited success in the treatment of solid tumors,such as colorectal cancer(CRC),in part because of the heterogeneous nature of tumor-associated antigens that lead to antigen-negative relapse after the initial response.This barrier might be overcome by enhancing the recruitment and durability of endogenous immune cells.Methods:Immunohistochemistry and flow cytometry were used to assess the expression of CD133 antigen in tissue microarrays and cell lines,respectively.Retroviral vector transduction was used to generate CBLB502-secreting CAR133-NK92 cells(CAR133-i502-NK92).The tumor killing capacity of CAR133-NK92 cells in vitro and in vivo were quantified via LDH release,the RTCA assay,and the degranulation test,as well as measuring tumor bioluminescence signal intensity in mice xenografts.Results:We engineered CAR133-i502-NK92 cells and demonstrated that those cells displayed enhanced proliferation(9.0×10^(4)cells vs.7.0×10^(4)cells)and specific anti-tumor activities in vitro and in a xenogeneic mouse model,and were well-tolerated.Notably,CBLB502 secreted by CAR133-i502-NK92 cells effectively activated endogenous immune cells.Furthermore,in hCD133+/hCD133−mixed cancer xenograft models,CAR133-i502-NK92 cells suppressed cancer growth better than the counterparts(n=5,P=0.0297).Greater T-cell infiltration was associated with greater anti-tumor potency(P<0.0001).Conclusions:Armed with a CBLB502 TLR5 agonist,CAR133-NK92 cells were shown to be capable of specifically eliminating CD133-positive colon cancer cells in a CAR133-dependent manner and indirectly eradicating CD133-negative colon cancer cells in a CBLB502-specific endogenous immune response manner.This study describes a novel technique for optimizing CAR-T/NK cells for the treatment of antigenically-diverse solid tumors.展开更多
Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of dat...Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.展开更多
The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger mu...The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion ability.Native graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing tasks.However,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids.In this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the graph.Based on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,etc.Specifically,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation.Based on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database.It promotes the efficiency of PIoT when handling multi-source heterogeneous data.An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines.The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.展开更多
Copper-based nanomaterials have been widely used in catalysis,electrodes,and other applications due to their unique electron-transfer properties.In this work,an efficient electrochemical sensor based on an electrode m...Copper-based nanomaterials have been widely used in catalysis,electrodes,and other applications due to their unique electron-transfer properties.In this work,an efficient electrochemical sensor based on an electrode modified with one-dimensional Cu(OH)_(2)/carboxymethyl cellulose(CMC)composite nanofibers was fabricated and investigated for the detection of aspirin.Scanning electron microscopy was employed to examine the morphological characteristics of these composite nanofibers.Cyclic voltammetry and electrochemical impedance spectroscopy were used to assess the electrochemical performance of a Cu(OH)_(2)/CMC composite nanofiber-modified electrode.The findings indicate that the modified electrode has a very high sensitivity to aspirin.The observed enhanced performance could be a result of the high surface-to-volume ratio of the composite nanofibers and their superior electron-transport characteristics,which may hasten electron transfer between aspirin and the surfaces of the modified electrode.This detection technique also demonstrated strong selectivity for aspirin.These findings imply that the technique can be applied as a highly effective and selective approach to aspirin measurement in biological science.展开更多
With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power...With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.展开更多
基金supported by the Construction Project of Cancer Precision Diagnosis and Drug Treatment Technology(Grant No. ZLJZZDYYWZL04)the Clinical Oncology Research Fund of CSCO(Grant No. Y-SY2021MS-0240)+2 种基金the Haihe Yingcai(Tianjin)Project(Grant No. TJSJMYXYC-D2-039)the Tianjin Key Medical Discipline(Specialty)Construction Project grant(Grant No. TJYXZDXK-009A)the CACA-BeiGene Lymphoma Research Foundation(Grant No.CORP-117)。
文摘Mature T-and natural killer(NK)-cell lymphomas are heterogeneous groups of malignant lymphoid neoplasms arising from T and NK cells. The incidence of mature T-and NK-cell lymphomas is 2.1 per 100,000 people, according to a US report~1.
基金supported by the National Science Foundation of China(T2350008,T2341003,22207103)STI2030-Major Projects(2021ZD0203000(2021ZD0203003))。
文摘Substance use disorders(SUDs)impact an estimated 300 million people worldwide,significantly impairing both health and social functioning.These disorders are marked by an inability to regulate substance use,despite the harmful consequences.Addiction affects various neurotransmitter systems,including dopamine,serotonin,γ-aminobutyric acid(GABA),and glutamate,each of which plays a role in the reward,stress,and self-control pathways of the brain(Koob&Volkow,2016).While significant advances have been made in neuroscience,our understanding of how these neurotransmitter systems interact and contribute to addiction is still evolving.This knowledge gap represents a significant challenge in the formulation of effective treatments for SUDs.At present,the US Food and Drug Administration(FDA)has approved pharmacological treatments for alcohol,nicotine,and opioid use disorders(Vasiliu,2022);however,no such treatments have been authorized for SUDs in general,or specifically for stimulant use disorders,such as cocaine and methamphetamine addiction.Notably,the FDA has not approved any new drugs for SUD treatment in the past 40 years.
基金supported by the STI2030-Major Projects(2021ZD0203000(2021ZD0203003))National Science Foundation of China(22207105)+1 种基金Beijing National Laboratory for Molecular Sciences(BNLMS202108)Chinese Academy of Sciences Pioneer Hundred Talents Program。
文摘Drug addiction refers to a state of dependence that arises from habitual drug intake and can result in specific withdrawal symptoms upon cessation.The most commonly abused substances include psychostimulants,cannabinoids,and opioids.When drugs are consumed,they stimulate the release of dopamine,a neurotransmitter crucial for the pleasure and reward centers of the brain.With repeated drug use,the brain undergoes various changes,leading to tolerance,dependence,and addiction(Lüscher et al.,2020).The mechanisms involved in drug addiction are highly complex and involve diverse cell types within the brain.
基金Financial support received from the National Natural Science Foundation of China(22178379)the National Key Research and Development Program of China(2021YFC2800902)is gratefully acknowledged.
文摘Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the challenges of long-term preparation,high costs and associated risks.Experimental studies,on the other hand,offer a safe and cost-effective means of exploring the mechanisms of hydrate dissociation and optimizing exploitation conditions.Gas hydrate decomposition is a complicated process along with intrinsic kinetics,mass transfer and heat transfer,which are the influencing factors for hydrate decomposition rate.The identification of the rate-limiting factor for hydrate dissociation during depressurization varies with the scale of the reservoir,making it challenging to extrapolate findings from laboratory experiments to the actual exploitation.This review aims to summarize current knowledge of investigations on hydrate decomposition on the subject of the research scale(core scale,middle scale,large scale and field tests)and to analyze determining factors for decomposition rate,considering the various research scales and their associated influencing factors.
基金supported by the National Natural Science Foundation of China(U23A6005 and 32171721)State Key Laboratory of Pulp and Paper Engineering(202305,2023ZD01,2023C02)+1 种基金Guangdong Province Basic and Application Basic Research Fund(2023B1515040013)the Fundamental Research Funds for the Central Universities(2023ZYGXZR045).
文摘The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.
基金Supported by National Natural Science Foundation of China(Grant No.52075145)S&T Program of Hebei Province of China(Grant Nos.20281805Z,E2020103001)Central Government Guides Basic Research Projects of Local Science and Technology Development Funds of China(Grant No.206Z1801G).
文摘The kinematic equivalent model of an existing ankle-rehabilitation robot is inconsistent with the anatomical structure of the human ankle,which influences the rehabilitation effect.Therefore,this study equates the human ankle to the UR model and proposes a novel three degrees of freedom(3-DOF)generalized spherical parallel mechanism for ankle rehabilitation.The parallel mechanism has two spherical centers corresponding to the rotation centers of tibiotalar and subtalar joints.Using screw theory,the mobility of the parallel mechanism,which meets the requirements of the human ankle,is analyzed.The inverse kinematics are presented,and singularities are identified based on the Jacobian matrix.The workspaces of the parallel mechanism are obtained through the search method and compared with the motion range of the human ankle,which shows that the parallel mechanism can meet the motion demand of ankle rehabilitation.Additionally,based on the motion-force transmissibility,the performance atlases are plotted in the parameter optimal design space,and the optimum parameter is obtained according to the demands of practical applications.The results show that the parallel mechanism can meet the motion requirements of ankle rehabilitation and has excellent kinematic performance in its rehabilitation range,which provides a theoretical basis for the prototype design and experimental verification.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金the National Key R&D Program of China(No.2022YFE0102000)the National Natural Science Foundation of China(Nos.22121004,U22A20409,22250008,and 22108197)+2 种基金the Haihe Laboratory of Sustainable Chemical Transformations,the Natural Science Foundation of Tianjin City(No.21JCZXJC00060)the Program of Introducing Talents of Discipline to Universities(No.BP0618007)the XPLORER PRIZE for financial support。
文摘The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts(SACs)is crucial for catalyzing the CO_(2)reduction reaction(CO_(2)RR).However,it remains a major challenge.While density-functional theory calculations serve as a powerful tool for catalyst screening,their time-consuming nature poses limitations.This paper presents a machine learning(ML)model based on easily accessible intrinsic descriptors to enable rapid,cost-effective,and high-throughput screening of efficient SACs in complex systems.Our ML model comprehensively captures the influences of interactions between 3 and 5d metal centers and 8 C,N-based coordination environments on CO_(2)RR activity and selectivity.We reveal the electronic origin of the different activity trends observed in early and late transition metals during coordination with N atoms.The extreme gradient boosting regression model shows optimal performance in predicting binding energy and limiting potential for both HCOOH and CO production.We confirm that the product of the electronegativity and the valence electron number of metals,the radius of metals,and the average electronegativity of neighboring coordination atoms are the critical intrinsic factors determining CO_(2)RR activity.Our developed ML models successfully predict several high-performance SACs beyond the existing database,demonstrating their potential applicability to other systems.This work provides insights into the low-cost and rational design of high-performance SACs.
基金Gansu Outstanding Youth Fund(20JR10RA203)Gansu Province Youth Doctor Fund(2023QB-033)+1 种基金National Natural Science Foundation of China(52169019)the Gansu Industry-University Support Fund(2020C-20).
文摘This study focuses on a DN50 pipeline-type Savonius hydraulic turbine.The torque variation of the turbine in a rotation cycle is analyzed theoretically in the framework of the plane potential flow theory.Related numerical simulations show that the change in turbine torque is consistent with the theoretical analysis,with the main power zone and the secondary power zone exhibiting a positive torque.In contrast,the primary resistance zone and the secondary resistance zone are characterized by a negative torque.Analytical relationships between the turbine’s internal flow angleθ,the deflector’s inclination angleα0,and the coverage angleαof the power zone are introduced,and a method for calculating the optimal number of blades is proposed to maximize the power zone.Results are presented about performance tests conducted on five groups of hydraulic turbines with the blade number ranging from 3 to 7.Such results indicate that both the turbine’s recovery power and efficiency attain the highest values when the blade number is 4,which is in agreement with the number of blades calculated by the proposed method.Additionally,the study examines the effects of the flow rate on turbine parameters and the projected energy generation and cost savings for a specific pipeline configuration.
文摘Objective:This paper focuses on the research and discussion of the efficacy of electromyographic biofeedback combined with swallowing training on post-stroke dysphagia.Methods:This study randomly sampled and analyzed 68 patients with post-stroke dysphagia from January 2023 to December 2023,34 cases of swallowing training intervention were grouped as the control group,and 34 cases of electromyography biofeedback combined with swallowing training intervention were grouped as the study group,and the clinical therapeutic effects of the two groups of patients after receiving the two different modes of intervention were compared.Results:The swallowing function of patients in both groups improved,and the VFSS score of patients in the seminar group was significantly higher than that of the control group,indicating that the clinical efficacy of the seminar group was more significant.The nasal feeding tube extraction rate,extraction time,and quality of life scores of the seminar group were better than those of the control group(P<0.05),which is of research value.Conclusion:For patients with post-stroke dysphagia,treatment with electromyography biofeedback combined with swallowing training mode can significantly improve their swallowing function.This effective intervention can not only shorten the time for patients to remove the nasal feeding tube but also help to improve the quality of life of patients,which is worth using.
基金supported by the National Key Research and Development Program of China(2019YFE0114400)the Guangdong Basic and Applied Basic Research Foundation(2021B1515120005)+7 种基金the National Natural Science Foundation of China(32171721)the Guangdong Basic and Applied Basic Research Foundation(2021B151512000)the Guangzhou Science and Technology Plan Project(202102020262)the State Key Laboratory of Pulp&Paper Engineering(2022C01),the State Key Laboratory of Pulp&Paper Engineering(202208)the Engineering and Physical Sciences Research Council(EPSRCEP/V027433/1EP/V027433/2EP/Y008707/1)。
文摘Although their cost-effectiveness and intrinsic safety,aqueous zinc-ion batteries suffer from notorious side reactions including hydrogen evolution reaction,Zn corrosion and passivation,and Zn dendrite formation on the anode.Despite numerous strategies to alleviate these side reactions have been demonstrated,they can only provide limited performance improvement from a single aspect.Herein,a triple-functional additive with trace amounts,ammonium hydroxide,was demonstrated to comprehensively protect zinc anodes.The results show that the shift of electrolyte pH from 4.1 to 5.2 lowers the HER potential and encourages the in situ formation of a uniform ZHS-based solid electrolyte interphase on Zn anodes.Moreover,cationic NH^(4+)can preferentially adsorb on the Zn anode surface to shield the“tip effect”and homogenize the electric field.Benefitting from this comprehensive protection,dendrite-free Zn deposition and highly reversible Zn plating/stripping behaviors were realized.Besides,improved electrochemical performances can also be achieved in Zn//MnO_(2)full cells by taking the advantages of this triple-functional additive.This work provides a new strategy for stabilizing Zn anodes from a comprehensive perspective.
基金supported by the National Natural Science Foundation of China under Grant 52077146.
文摘With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.
基金This work was supported by Science and Technology Project of State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .
基金This study was supported by Beijing Science and Technology Planning Project(Z201100005420005,China).
文摘Saponins are the main triterpenoid ingredients from Panax notoginseng,a well-known Chinese medicine,and are important sources for producing drugs to prevent and treat cerebrovascular and cardiovascular diseases.However,the transcriptional regulatory network of saponin biosynthesis in P.notoginseng is largely unknown.In the present study we demonstrated that one R2R3-MYB transcription factor,designated PnMYB4,acts as a repressor of saponin accumulation.Suppression of PnMYB4 in P.notoginseng calli significantly increased the saponin content and the expression level of saponin biosynthetic genes.PnMYB4 directly bound to the promoters of key saponin biosynthetic genes,including PnSS,PnSE,and PnDS,to repress saponin accumulation.PnMYB4 and the activator PnMYB1 could inter-acted with PnbHLH,which is a positive regulator of saponin biosynthesis,to modulate the biosynthesis of saponin.PnMYB4 competed with PnMYB1 for binding to PnbHLH,repressing activation of the promoters of saponin structural genes induced by the PnMYB1-PnbHLH complex.Our study reveals that a complex regulatory module of saponin biosynthesis is associated with positive and negative MYB transcriptional regulators and provides a theoretical basis for improving the content of saponins and efficacy of P.notoginseng.
基金supported by the Technology Innovation and Application Developnent Key Program of Chongqing(Grant No.CSTC2021jscx-gksb-N0026)the National Natural Science Foundation of China(Grant No.31540016)+1 种基金the Basic Research and Frontier Exploration Projects of Chongqing(Grant No.cstc2018jcyjAX0075)the Subsidy Fund for the Development of National Silk in Chongqing(Grant No.CQ2018JSCE05).
文摘Objective:CAR-T/NK cells have had limited success in the treatment of solid tumors,such as colorectal cancer(CRC),in part because of the heterogeneous nature of tumor-associated antigens that lead to antigen-negative relapse after the initial response.This barrier might be overcome by enhancing the recruitment and durability of endogenous immune cells.Methods:Immunohistochemistry and flow cytometry were used to assess the expression of CD133 antigen in tissue microarrays and cell lines,respectively.Retroviral vector transduction was used to generate CBLB502-secreting CAR133-NK92 cells(CAR133-i502-NK92).The tumor killing capacity of CAR133-NK92 cells in vitro and in vivo were quantified via LDH release,the RTCA assay,and the degranulation test,as well as measuring tumor bioluminescence signal intensity in mice xenografts.Results:We engineered CAR133-i502-NK92 cells and demonstrated that those cells displayed enhanced proliferation(9.0×10^(4)cells vs.7.0×10^(4)cells)and specific anti-tumor activities in vitro and in a xenogeneic mouse model,and were well-tolerated.Notably,CBLB502 secreted by CAR133-i502-NK92 cells effectively activated endogenous immune cells.Furthermore,in hCD133+/hCD133−mixed cancer xenograft models,CAR133-i502-NK92 cells suppressed cancer growth better than the counterparts(n=5,P=0.0297).Greater T-cell infiltration was associated with greater anti-tumor potency(P<0.0001).Conclusions:Armed with a CBLB502 TLR5 agonist,CAR133-NK92 cells were shown to be capable of specifically eliminating CD133-positive colon cancer cells in a CAR133-dependent manner and indirectly eradicating CD133-negative colon cancer cells in a CBLB502-specific endogenous immune response manner.This study describes a novel technique for optimizing CAR-T/NK cells for the treatment of antigenically-diverse solid tumors.
基金supported by the National Key R&D Program of China(2020YFB0905900).
文摘Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.
基金supported by the National Key R&D Program of China(2020YFB0905900).
文摘The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion ability.Native graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing tasks.However,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids.In this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the graph.Based on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,etc.Specifically,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation.Based on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database.It promotes the efficiency of PIoT when handling multi-source heterogeneous data.An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines.The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.
基金The authors wish to acknowledge financial support from the Science and Technology Projects in Jilin Province Department of Education(Grant No.JJKH20220239KJ).
文摘Copper-based nanomaterials have been widely used in catalysis,electrodes,and other applications due to their unique electron-transfer properties.In this work,an efficient electrochemical sensor based on an electrode modified with one-dimensional Cu(OH)_(2)/carboxymethyl cellulose(CMC)composite nanofibers was fabricated and investigated for the detection of aspirin.Scanning electron microscopy was employed to examine the morphological characteristics of these composite nanofibers.Cyclic voltammetry and electrochemical impedance spectroscopy were used to assess the electrochemical performance of a Cu(OH)_(2)/CMC composite nanofiber-modified electrode.The findings indicate that the modified electrode has a very high sensitivity to aspirin.The observed enhanced performance could be a result of the high surface-to-volume ratio of the composite nanofibers and their superior electron-transport characteristics,which may hasten electron transfer between aspirin and the surfaces of the modified electrode.This detection technique also demonstrated strong selectivity for aspirin.These findings imply that the technique can be applied as a highly effective and selective approach to aspirin measurement in biological science.
基金supported by the Science and Technology Project of the State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.