This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based...This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based on the internal model principle,a distributed dynamic output feedback control law is proposed to achieve both robust output regulation of the closed-loop system and plant input sharing among the actuators.A practical example of five motors cooperatively driving an uncertain shaft under an external load torque is presented to show the effectiveness of the proposed control law.展开更多
To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on int...To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on integer linear programming(ILP)with flow balance constraints,ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions.In the established gridmap,a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme.It uses the greedy algorithm and the A*algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory of each USV.The greedy algorithm enables fast local traversal of unvisited grids,while the A*algorithm ensures navigation to escape from deadlock areas and maintains global path continuity.The comparison of task allocation results proves that the task allocation algorithm based on ILP improves the mapping efficiency and task distribution fairness.The proposed allocation method and result analysis provide a certain reference for the practical application ofMulti-USV to perform survey tasks collaboratively.展开更多
Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory v...Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory versus degenerative dichotomy.This was based on a broad misconception regarding essentially all neurodegenerative conditions,depicting the degenerative process as passive and immune-independent occurring as a late byproduct of active inflammation in the central nervous system(CNS),which is(solely)systemically driven.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between differ...To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.展开更多
Formation control of multiple spacecraft has attracted extensive research attention.However,achieving reliable performance under sensor failures remains a significant challenge.This paper develops an integrated framew...Formation control of multiple spacecraft has attracted extensive research attention.However,achieving reliable performance under sensor failures remains a significant challenge.This paper develops an integrated framework that jointly designs distributed observers and local controllers to ensure robust formation control in the presence of external disturbances and sensor malfunctions.Treating the spacecraft formation as a single interconnected system,each spacecraft constructs a distributed observer that estimates the overall system state by incorporating both its own measurements and the predicted control information shared among the spacecraft.Based on the observer estimates,a local control law is synthesized to maintain the desired formation.Rigorous theoretical analysis and numerical simulations demonstrate that the proposed integrated approach effectively guarantees formation stability and resilience against sensor failures and disturbances.展开更多
Acidic electrochemical CO_(2) reduction(CO_(2) RR)mitigates CO_(2) loss and energy inefficiencies but suffers from limited selectivity.Insufficient understanding of the interfacial microenvironment and cation specific...Acidic electrochemical CO_(2) reduction(CO_(2) RR)mitigates CO_(2) loss and energy inefficiencies but suffers from limited selectivity.Insufficient understanding of the interfacial microenvironment and cation specificity hinders the development of efficient interfacial design methods.Here,we integrate ab initio-derived reaction kinetics with mass transfer modeling into a multiscale framework that reproduces the bell-shaped Faradaic efficiency profile inaccessible to the Butler-Volmer equations.Our results emphasize the role of hydrogen bonding in CO_(2) activation and reveal a potential-dependent shift in the rate-determining steps.We also demonstrate that cations inhibit competing hydrogen evolution by strengthening the interfacial electric field and disrupting the hydrogen-bond network.However,their accumulation near the outer Helmholtz plane induces strong steric effects,impeding CO_(2) supply.Furthermore,the parametric analysis highlights the critical role of strategies such as pressurization and pore-confined electrolyte control in overcoming interfacial CO_(2) transport limitations,enhancing selectivity,and broadening the operating potential window.This work advances a multiscale perspective on interfacial mass transfer and cation effects,establishing a unified framework for reaction interface design in acidic CO_(2) RR.展开更多
Driven by the trend of device miniaturization and high-density integration,the interaction between adjacent electrodes has become a critical factor affecting the interfacial reliability of thermoelectric(TE)structures...Driven by the trend of device miniaturization and high-density integration,the interaction between adjacent electrodes has become a critical factor affecting the interfacial reliability of thermoelectric(TE)structures.This study investigates the influence of adjoining electrode interactions on the interfacial response of a multi-electrode/TE substrate structure,including interfacial stresses and stress intensity factors at the electrode ends.To solve the corresponding boundary-value problem,the Fourier transforms are adopted to derive a governing integro-differential equation for the interfacial shear stress in multi-electrode systems,incorporating the TE effects as generalized forces on the right-hand side.The results show that both the interfacial tension and transverse stress in the electrodes are significantly affected by the presence of adjacent electrodes.The interaction between neighboring electrodes diminishes as their spacing increases or when an adhesive interlayer is introduced.Furthermore,the softer and thinner electrodes,the softer and thicker adhesive interlayer,and the smaller TE loads are found to be beneficial for improving the interfacial performance.These findings may contribute to the accurate measurement in surface sensors and layout design of multi-point health monitoring systems for TE structures.展开更多
BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte...BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.展开更多
AIM:To investigate the changes of retinal vascular parameters and retinal layer thickness in patients with multiple sclerosis(MS).METHODS:This single-centered case-control study was performed on a MS group of 42 patie...AIM:To investigate the changes of retinal vascular parameters and retinal layer thickness in patients with multiple sclerosis(MS).METHODS:This single-centered case-control study was performed on a MS group of 42 patients diagnosed with MS and a control group of 43 healthy hospital staff matched in terms of age and sex at Iran University,department of neurology and ophthalmology from March 2020 to March 2021.The ophthalmic parameters of each patient were recorded,and optical coherence tomography was used to evaluate the retinal thickness in the layers.RESULTS:This study enrolled a total of 85 participants,with a mean age of 40.44±11.52 years,including 61 females(72%).The control group consisted of 43 individuals with a mean age of 39.49±11.07 years,while the MS group comprised 42 participants with a mean age of 41.40±12.01 years.The mean disease duration in the MS group was 8.45±6.04 a.The thickness of the ganglion cell layer in the right eye was significantly lower in the MS group compared to the control group(P=0.034).In addition,except for the left nasal sector(P=0.106),the mean peripapillary neurofibrillation in all examined sectors were significantly lower in the MS group than in the control group(P<0.05).The average vessel density in both the deep and superficial capillary plexuses across all regions of both eyes was lower in the MS group than in the control group,with all comparisons for the superficial capillary plexus showing statistical significance(P<0.05 for all except the left nasal sector).CONCLUSION:The thickness of the retina of patients with MS is significantly reduced.Therefore,optical coherence tomography results can be used as a reliable tool to evaluate disease progression and prognosis in MS patients.展开更多
Multiple sclerosis is a severe autoimmune disorder that is mainly mediated by pathogenic cluster of CD4^(+)T cell subsets.Despite advancements in the management of multiple sclerosis,there is a critical need for more ...Multiple sclerosis is a severe autoimmune disorder that is mainly mediated by pathogenic cluster of CD4^(+)T cell subsets.Despite advancements in the management of multiple sclerosis,there is a critical need for more effective and safer treatments.In the present study,we administered Lycium barbarum glycopeptide to a mouse model of experimental autoimmune encephalomyelitis-an animal model of multiple sclerosis-and evaluated its effects on pathogenic CD4^(+)T cell activation both in vivo and in vitro.Lycium barbarum glycopeptide significantly mitigated the clinical severity of experimental autoimmune encephalomyelitis,as demonstrated by reduced demyelination and neuroinflammation.Moreover,Lycium barbarum glycopeptide treatment decreased the infiltration of peripheral leukocytes into the central nervous system and suppressed pro-inflammatory cytokine expression.Lycium barbarum glycopeptide also modulated pathogenic CD4^(+)T cell activation by inhibiting T helper 1/T helper 17 cell differentiation while promoting regulatory T cell expansion.Notably,no side effects were observed,suggesting the long-term safety and tolerability of Lycium barbarum glycopeptide.Furthermore,RNA sequencing data indicated that Lycium barbarum glycopeptide inhibits activator protein-1,an essential regulator of T cell activation and differentiation.This finding was supported by the reversal of T helper/T helper 17 cell response suppression upon AP-1 blockade.Collectively,these results highlight the potential of Lycium barbarum glycopeptide as an innovative therapeutic agent for CD4^(+)T cell-associated autoimmune or inflammatory diseases,such as multiple sclerosis.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple rol...Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple roles of mononuclear macrophages in the neuroinflammatory process. Monocytes play a significant role in neuroinflammation, and managing neuroinflammation by manipulating peripheral monocytes stands out as an effective strategy for the treatment of multiple sclerosis, leading to improved patient outcomes. This review outlines the steps involved in the entry of myeloid monocytes into the central nervous system that are targets for effective intervention: the activation of bone marrow hematopoiesis, migration of monocytes in the blood, and penetration of the blood–brain barrier by monocytes. Finally, we summarize the different monocyte subpopulations and their effects on the central nervous system based on phenotypic differences. As activated microglia resemble monocyte-derived macrophages, it is important to accurately identify the role of monocyte-derived macrophages in disease. Depending on the roles played by monocyte-derived macrophages at different stages of the disease, several of these processes can be interrupted to limit neuroinflammation and improve patient prognosis. Here, we discuss possible strategies to target monocytes in neurological diseases, focusing on three key aspects of monocyte infiltration into the central nervous system, to provide new ideas for the treatment of neurodegenerative diseases.展开更多
BACKGROUND The incidence of malignant gastrointestinal(GI)tumors is increasing,and advancements in medical care have significantly improved patient survival rates.As a result,the number of cases involving multiple pri...BACKGROUND The incidence of malignant gastrointestinal(GI)tumors is increasing,and advancements in medical care have significantly improved patient survival rates.As a result,the number of cases involving multiple primary cancers(MPC)has also increased.The rarity of MPC and the absence of sensitive and specific dia-gnostic markers often lead to missed or incorrect diagnoses.It is,therefore,of vital importance to improve the vigilance of clinicians and the accurate diagnosis of this disease.Patients with GI malignancies face a higher relative risk of deve-loping additional primary malignant tumors compared to those with other systemic tumors.Vigilant monitoring and follow-up are crucial,especially for high-risk groups,which include older adults,men,those with addictions to alcohol and tobacco,those with a family history of tumors,and those who have undergone radiotherapy.CASE SUMMARY In this article,we report three cases of MPC,each involving malignant tumors of the GI tract as the initial primary carcinoma,offering insights that may aid in effectively managing similar cases.CONCLUSION Patients with GI malignancies face a higher MPC risk.Developing screening and follow-up protocols may enhance detection and treatment outcomes.展开更多
The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides ide...The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers.First,the gamma spectra of 12 target nuclides(including the background case)were measured to create training datasets.The characteristic energies,obtained through energy calibration and full-energy peak addresses,are utilized as input features for a neural network.A large number of single-and multiple-nuclide training datasets are generated using random combinations and small-range drifting.Subsequently,a multi-label classification neural network based on a binary cross-entropy loss function is applied to export the existence probability of certain nuclides.The designed algorithm effectively reduces the computation time and storage space required by the neural network and has been successfully implemented in a portable gamma spectrometer with a running time of t_(r)<2 s.Results show that,in both validation and actual tests,the identification accuracy of the designed algorithm reaches 94.8%,for gamma spectra with a dose rate of d≈0.5μSv∕h and a measurement time t_(m)=60 s.This improves the ability to perform rapid on-site nuclide identification at important sites.展开更多
Steel slag(SS)accumulates unavoidably due to its complex and unstable composition,high production volumes,and limited value-added resource utilization.Single or multiple interface modifiers were proposed to enhance th...Steel slag(SS)accumulates unavoidably due to its complex and unstable composition,high production volumes,and limited value-added resource utilization.Single or multiple interface modifiers were proposed to enhance the properties of SS through high-speed dispersion,transforming its inherent hydrophilic and oleophobic characteristics into hydrophily and lipophilicity.The modification effects were innovatively assessed by observing the color changes of modified steel slag solutions following the dissolution-settlement equilibrium constant.This approach avoided human-induced errors and improved estimated accuracy in conformance with conventional methods such as oil absorption value,activation index,sedimentation volume,and lipophilicity.The hydrolysis of 3-aminopropyltriethoxysilane(KH)generated–Si(OH)_(3)structure to form hydrogen or covalent bonds with active substances(OH groups)from SS.Concurrently,SS underwent encapsulation via Si–O–Si structure resulting from the dehydration of–Si(OH)_(3).The stearic acid coupling agent(SA),aluminate coupling agent(AC),and titanate coupling agent(TN)underwent chemical reactions with Ca(OH)_(2),Al(OH)_(3),and CaCO_(3)in SS.The acidic SA primarily created stable chemical bonds and acted as a supplement due to its package,reducing surface activity and hydrophilicity while enhancing lipophilicity.Specifically,the optimal modification effect was obtained at 3 wt.%SA.Consequently,3 wt.%SA was established as the benchmark for multiple modifiers and the most effective combination was 3 wt.%SA and 3 wt.%AC.Compared with a single interface modifier,SA corroded the SS surface to provide numerous active sites for further modification by KH,AC,or TN,resulting in a more densely packed structure.In addition,more organic groups on SS prevent the proximity of other particles from agglomerating to achieve dispersion and a synergistic modification,laying a theoretical foundation of SS in a new pathway for organic composite materials.展开更多
Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-targe...Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the“therapeutic module”of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.展开更多
This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
Multiple quantum well(MQW) Ⅲ-nitride diodes that can simultaneously emit and detect light feature an overlapping region between their electroluminescence and responsivity spectra, which allows them to be simultaneous...Multiple quantum well(MQW) Ⅲ-nitride diodes that can simultaneously emit and detect light feature an overlapping region between their electroluminescence and responsivity spectra, which allows them to be simultaneously used as both a transmitter and a receiver in a wireless light communication system. Here, we demonstrate a mobile light communication system using a time-division multiplexing(TDM) scheme to achieve bidirectional data transmission via the same optical channel.Two identical blue MQW diodes are defined by software as a transmitter or a receiver. To address the light alignment issue, an image identification module integrated with a gimbal stabilizer is used to automatically detect the locations of moving targets;thus, underwater audio communication is realized via a mobile blue-light TDM communication mode. This approach not only uses a single link but also integrates mobile nodes in a practical network.展开更多
基金Supported by the Shenzhen Key Laboratory of Control Theory and Intelligent Systems (ZDSYS20220330161800001)the National Natural Science Foundation of China (62303207)the Guangdong Basic and Applied Basic Research Foundation (2024A1515010725)。
文摘This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based on the internal model principle,a distributed dynamic output feedback control law is proposed to achieve both robust output regulation of the closed-loop system and plant input sharing among the actuators.A practical example of five motors cooperatively driving an uncertain shaft under an external load torque is presented to show the effectiveness of the proposed control law.
基金supported in part by the International Science and Technology Project of Guangzhou Development District under Grant 2023GH08the Science and Technology Development Fund,MSAR,under Grants 0029/2022/AGJ and 0029/2023/RIA1the Program of Guangdong under Grant 2023A0505020003.
文摘To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on integer linear programming(ILP)with flow balance constraints,ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions.In the established gridmap,a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme.It uses the greedy algorithm and the A*algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory of each USV.The greedy algorithm enables fast local traversal of unvisited grids,while the A*algorithm ensures navigation to escape from deadlock areas and maintains global path continuity.The comparison of task allocation results proves that the task allocation algorithm based on ILP improves the mapping efficiency and task distribution fairness.The proposed allocation method and result analysis provide a certain reference for the practical application ofMulti-USV to perform survey tasks collaboratively.
文摘Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory versus degenerative dichotomy.This was based on a broad misconception regarding essentially all neurodegenerative conditions,depicting the degenerative process as passive and immune-independent occurring as a late byproduct of active inflammation in the central nervous system(CNS),which is(solely)systemically driven.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金funded by Ministry of Education Humanities and Social Science Research Project,grant number 23YJAZH034The Postgraduate Research and Practice Innovation Program of Jiangsu Province,grant number SJCX25_17National Computer Basic Education Research Project in Higher Education Institutions,grant number 2024-AFCEC-056,2024-AFCEC-057.
文摘To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.
基金supported by the National Natural Science Foundation of China(62088101,62522307,62273045,U2341213)Beijing Nova Program(20230484481)。
文摘Formation control of multiple spacecraft has attracted extensive research attention.However,achieving reliable performance under sensor failures remains a significant challenge.This paper develops an integrated framework that jointly designs distributed observers and local controllers to ensure robust formation control in the presence of external disturbances and sensor malfunctions.Treating the spacecraft formation as a single interconnected system,each spacecraft constructs a distributed observer that estimates the overall system state by incorporating both its own measurements and the predicted control information shared among the spacecraft.Based on the observer estimates,a local control law is synthesized to maintain the desired formation.Rigorous theoretical analysis and numerical simulations demonstrate that the proposed integrated approach effectively guarantees formation stability and resilience against sensor failures and disturbances.
基金supported by the National Natural Science Foundation of China(52394202 and 52476056)key project of the Joint Fund for Innovation and Development of Chongqing Natural Science Foundation(CSTB2022NSCQ-LZX0013)+1 种基金the Innovative Research Group Project of the National Natural Science Foundation of China(52021004)the Natural Science Foundation of Chongqing,China(CSTB2024NSCQ-MSX0915).
文摘Acidic electrochemical CO_(2) reduction(CO_(2) RR)mitigates CO_(2) loss and energy inefficiencies but suffers from limited selectivity.Insufficient understanding of the interfacial microenvironment and cation specificity hinders the development of efficient interfacial design methods.Here,we integrate ab initio-derived reaction kinetics with mass transfer modeling into a multiscale framework that reproduces the bell-shaped Faradaic efficiency profile inaccessible to the Butler-Volmer equations.Our results emphasize the role of hydrogen bonding in CO_(2) activation and reveal a potential-dependent shift in the rate-determining steps.We also demonstrate that cations inhibit competing hydrogen evolution by strengthening the interfacial electric field and disrupting the hydrogen-bond network.However,their accumulation near the outer Helmholtz plane induces strong steric effects,impeding CO_(2) supply.Furthermore,the parametric analysis highlights the critical role of strategies such as pressurization and pore-confined electrolyte control in overcoming interfacial CO_(2) transport limitations,enhancing selectivity,and broadening the operating potential window.This work advances a multiscale perspective on interfacial mass transfer and cation effects,establishing a unified framework for reaction interface design in acidic CO_(2) RR.
基金Project supported by the National Natural Science Foundation of China(Nos.12502117,12272269,11972257)the Natural Science Foundation of Ningxia of China(No.2024AAC03018)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Shanghai Gaofeng Project for University Academic Program Development。
文摘Driven by the trend of device miniaturization and high-density integration,the interaction between adjacent electrodes has become a critical factor affecting the interfacial reliability of thermoelectric(TE)structures.This study investigates the influence of adjoining electrode interactions on the interfacial response of a multi-electrode/TE substrate structure,including interfacial stresses and stress intensity factors at the electrode ends.To solve the corresponding boundary-value problem,the Fourier transforms are adopted to derive a governing integro-differential equation for the interfacial shear stress in multi-electrode systems,incorporating the TE effects as generalized forces on the right-hand side.The results show that both the interfacial tension and transverse stress in the electrodes are significantly affected by the presence of adjacent electrodes.The interaction between neighboring electrodes diminishes as their spacing increases or when an adhesive interlayer is introduced.Furthermore,the softer and thinner electrodes,the softer and thicker adhesive interlayer,and the smaller TE loads are found to be beneficial for improving the interfacial performance.These findings may contribute to the accurate measurement in surface sensors and layout design of multi-point health monitoring systems for TE structures.
基金Supported by Chongqing Medical Scientific Research Project(Joint Project of Chongqing Health Commission and Science and Technology Bureau),No.2023MSXM060.
文摘BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.
文摘AIM:To investigate the changes of retinal vascular parameters and retinal layer thickness in patients with multiple sclerosis(MS).METHODS:This single-centered case-control study was performed on a MS group of 42 patients diagnosed with MS and a control group of 43 healthy hospital staff matched in terms of age and sex at Iran University,department of neurology and ophthalmology from March 2020 to March 2021.The ophthalmic parameters of each patient were recorded,and optical coherence tomography was used to evaluate the retinal thickness in the layers.RESULTS:This study enrolled a total of 85 participants,with a mean age of 40.44±11.52 years,including 61 females(72%).The control group consisted of 43 individuals with a mean age of 39.49±11.07 years,while the MS group comprised 42 participants with a mean age of 41.40±12.01 years.The mean disease duration in the MS group was 8.45±6.04 a.The thickness of the ganglion cell layer in the right eye was significantly lower in the MS group compared to the control group(P=0.034).In addition,except for the left nasal sector(P=0.106),the mean peripapillary neurofibrillation in all examined sectors were significantly lower in the MS group than in the control group(P<0.05).The average vessel density in both the deep and superficial capillary plexuses across all regions of both eyes was lower in the MS group than in the control group,with all comparisons for the superficial capillary plexus showing statistical significance(P<0.05 for all except the left nasal sector).CONCLUSION:The thickness of the retina of patients with MS is significantly reduced.Therefore,optical coherence tomography results can be used as a reliable tool to evaluate disease progression and prognosis in MS patients.
基金supported by the National Natural Science Foundational of China,Nos.U24A20692(to CJZ),82371355(to CJZ),and 82101414(to MH)National NaturalScience Foundational of China for Excellent Young Scholars,No.82022019(to CJZ)+5 种基金Sichuan Special Fund for Distinguished Young Scholars,No.24NSFJQ0052(to CJZ)The Innovationand Entrepreneurial Team of Sichuan Tianfu Emei Program,No.CZ2024018(to CJZ)Funding for Distinguished Young Scholars of Sichuan Provincial People’sHospital,No.30420230005Funding for Distinguished Young Scholars of University of Electronic Science and Technology of China,No.A1098531023601381(toCJZ)Sichuan Science and Technology Support Project,No.2023YFS0212(to BH)Project of Sichuan Provincial Health Commission,No.19PJ265(to LD).
文摘Multiple sclerosis is a severe autoimmune disorder that is mainly mediated by pathogenic cluster of CD4^(+)T cell subsets.Despite advancements in the management of multiple sclerosis,there is a critical need for more effective and safer treatments.In the present study,we administered Lycium barbarum glycopeptide to a mouse model of experimental autoimmune encephalomyelitis-an animal model of multiple sclerosis-and evaluated its effects on pathogenic CD4^(+)T cell activation both in vivo and in vitro.Lycium barbarum glycopeptide significantly mitigated the clinical severity of experimental autoimmune encephalomyelitis,as demonstrated by reduced demyelination and neuroinflammation.Moreover,Lycium barbarum glycopeptide treatment decreased the infiltration of peripheral leukocytes into the central nervous system and suppressed pro-inflammatory cytokine expression.Lycium barbarum glycopeptide also modulated pathogenic CD4^(+)T cell activation by inhibiting T helper 1/T helper 17 cell differentiation while promoting regulatory T cell expansion.Notably,no side effects were observed,suggesting the long-term safety and tolerability of Lycium barbarum glycopeptide.Furthermore,RNA sequencing data indicated that Lycium barbarum glycopeptide inhibits activator protein-1,an essential regulator of T cell activation and differentiation.This finding was supported by the reversal of T helper/T helper 17 cell response suppression upon AP-1 blockade.Collectively,these results highlight the potential of Lycium barbarum glycopeptide as an innovative therapeutic agent for CD4^(+)T cell-associated autoimmune or inflammatory diseases,such as multiple sclerosis.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Natural Science Foundation of China,Nos.82060219,82271234the Natural Science Foundation of Jiangxi Province,Nos.20212ACB216009,20212BAB216048+1 种基金Jiangxi Province Thousands of Plans,No.jxsq2019201023Youth Team Project of the Second Affiliated Hospital of Nanchang University,No.2019YNTD12003(all to FH)。
文摘Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple roles of mononuclear macrophages in the neuroinflammatory process. Monocytes play a significant role in neuroinflammation, and managing neuroinflammation by manipulating peripheral monocytes stands out as an effective strategy for the treatment of multiple sclerosis, leading to improved patient outcomes. This review outlines the steps involved in the entry of myeloid monocytes into the central nervous system that are targets for effective intervention: the activation of bone marrow hematopoiesis, migration of monocytes in the blood, and penetration of the blood–brain barrier by monocytes. Finally, we summarize the different monocyte subpopulations and their effects on the central nervous system based on phenotypic differences. As activated microglia resemble monocyte-derived macrophages, it is important to accurately identify the role of monocyte-derived macrophages in disease. Depending on the roles played by monocyte-derived macrophages at different stages of the disease, several of these processes can be interrupted to limit neuroinflammation and improve patient prognosis. Here, we discuss possible strategies to target monocytes in neurological diseases, focusing on three key aspects of monocyte infiltration into the central nervous system, to provide new ideas for the treatment of neurodegenerative diseases.
基金Supported by Gansu Provincial Natural Science Foundation,No.21JR1RA010In-Hospital Research Fund of Gansu Provincial Hospital,No.23GSSYD-5.
文摘BACKGROUND The incidence of malignant gastrointestinal(GI)tumors is increasing,and advancements in medical care have significantly improved patient survival rates.As a result,the number of cases involving multiple primary cancers(MPC)has also increased.The rarity of MPC and the absence of sensitive and specific dia-gnostic markers often lead to missed or incorrect diagnoses.It is,therefore,of vital importance to improve the vigilance of clinicians and the accurate diagnosis of this disease.Patients with GI malignancies face a higher relative risk of deve-loping additional primary malignant tumors compared to those with other systemic tumors.Vigilant monitoring and follow-up are crucial,especially for high-risk groups,which include older adults,men,those with addictions to alcohol and tobacco,those with a family history of tumors,and those who have undergone radiotherapy.CASE SUMMARY In this article,we report three cases of MPC,each involving malignant tumors of the GI tract as the initial primary carcinoma,offering insights that may aid in effectively managing similar cases.CONCLUSION Patients with GI malignancies face a higher MPC risk.Developing screening and follow-up protocols may enhance detection and treatment outcomes.
文摘The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers.First,the gamma spectra of 12 target nuclides(including the background case)were measured to create training datasets.The characteristic energies,obtained through energy calibration and full-energy peak addresses,are utilized as input features for a neural network.A large number of single-and multiple-nuclide training datasets are generated using random combinations and small-range drifting.Subsequently,a multi-label classification neural network based on a binary cross-entropy loss function is applied to export the existence probability of certain nuclides.The designed algorithm effectively reduces the computation time and storage space required by the neural network and has been successfully implemented in a portable gamma spectrometer with a running time of t_(r)<2 s.Results show that,in both validation and actual tests,the identification accuracy of the designed algorithm reaches 94.8%,for gamma spectra with a dose rate of d≈0.5μSv∕h and a measurement time t_(m)=60 s.This improves the ability to perform rapid on-site nuclide identification at important sites.
基金supported by the National Natural Science Foundation of China(U23A20605)Anhui Graduate Innovation and Entrepreneurship Practice Project(2022cxcysj090)+2 种基金China Baowu Low Carbon Metallurgy Innovation Foundation(BWLCF202202)the University Synergy Innovation Program of Anhui Province(GXXT-2020-072)the Outstanding Youth Fund of Anhui Province(2208085J19).
文摘Steel slag(SS)accumulates unavoidably due to its complex and unstable composition,high production volumes,and limited value-added resource utilization.Single or multiple interface modifiers were proposed to enhance the properties of SS through high-speed dispersion,transforming its inherent hydrophilic and oleophobic characteristics into hydrophily and lipophilicity.The modification effects were innovatively assessed by observing the color changes of modified steel slag solutions following the dissolution-settlement equilibrium constant.This approach avoided human-induced errors and improved estimated accuracy in conformance with conventional methods such as oil absorption value,activation index,sedimentation volume,and lipophilicity.The hydrolysis of 3-aminopropyltriethoxysilane(KH)generated–Si(OH)_(3)structure to form hydrogen or covalent bonds with active substances(OH groups)from SS.Concurrently,SS underwent encapsulation via Si–O–Si structure resulting from the dehydration of–Si(OH)_(3).The stearic acid coupling agent(SA),aluminate coupling agent(AC),and titanate coupling agent(TN)underwent chemical reactions with Ca(OH)_(2),Al(OH)_(3),and CaCO_(3)in SS.The acidic SA primarily created stable chemical bonds and acted as a supplement due to its package,reducing surface activity and hydrophilicity while enhancing lipophilicity.Specifically,the optimal modification effect was obtained at 3 wt.%SA.Consequently,3 wt.%SA was established as the benchmark for multiple modifiers and the most effective combination was 3 wt.%SA and 3 wt.%AC.Compared with a single interface modifier,SA corroded the SS surface to provide numerous active sites for further modification by KH,AC,or TN,resulting in a more densely packed structure.In addition,more organic groups on SS prevent the proximity of other particles from agglomerating to achieve dispersion and a synergistic modification,laying a theoretical foundation of SS in a new pathway for organic composite materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.:32271292,31872723,32200778,and 22377089)the Jiangsu Students Innovation and Entrepre-neurship Training Program,China(Program No.:202210285081Z)+6 种基金the Project of MOE Key Laboratory of Geriatric Diseases and Immunology,China(Project No.:JYN202404)Proj-ect Funded by the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions,Natural Science Foundation of Jiangsu Province,China(Project No.:BK20220494)Suzhou Medical and Health Technology Innovation Project,China(Grant No.:SKY2022107)the Clinical Research Center of Neuro-logical Disease in The Second Affiliated Hospital of Soochow University,China(Grant No.:ND2022A04)State Key Laboratory of Drug Research(Grant No.:SKLDR-2023-KF-05)Jiangsu Shuang-chuang Program for Doctor,Young Science Talents Promotion Project of Jiangsu Science and Technology Association(Program No.:TJ-2023-019)Young Science Talents Promotion Project of Suzhou Science and Technology Association,Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases,and startup funding(Grant Nos.:NH21500221,NH21500122,and NH21500123)to Qifei Cong.
文摘Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the“therapeutic module”of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金jointly supported by the National Natural Science Foundation of China (U21A20495)Natural Science Foundation of Jiangsu Province (BG2024023)+1 种基金National Key Research and Development Program of China (2022YFE0112000)111 Project (D17018)。
文摘Multiple quantum well(MQW) Ⅲ-nitride diodes that can simultaneously emit and detect light feature an overlapping region between their electroluminescence and responsivity spectra, which allows them to be simultaneously used as both a transmitter and a receiver in a wireless light communication system. Here, we demonstrate a mobile light communication system using a time-division multiplexing(TDM) scheme to achieve bidirectional data transmission via the same optical channel.Two identical blue MQW diodes are defined by software as a transmitter or a receiver. To address the light alignment issue, an image identification module integrated with a gimbal stabilizer is used to automatically detect the locations of moving targets;thus, underwater audio communication is realized via a mobile blue-light TDM communication mode. This approach not only uses a single link but also integrates mobile nodes in a practical network.
