Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been...Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments,ongoing progress is required to ensure these are effective,economical,and accessible for the globally ageing population.As such,continued identification of new potential drug targets and biomarkers is critical."Big data"studies,such as proteomics,can generate information on thousands of possible new targets for dementia diagnostics and therapeutics,but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development.In this review,we discuss current tauopathy biomarkers and therapeutics,and highlight areas in need of improvement,particularly when addressing the needs of frail,comorbid and cognitively impaired populations.We highlight biomarkers which have been developed from proteomic data,and outline possible future directions in this field.We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development,and demonstrate its application to our group's recent tau interactome dataset as an example.展开更多
The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data.This data can be extremely valuable for executing organizations because the data al...The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data.This data can be extremely valuable for executing organizations because the data allows constant monitoring,analyzing,and improving the underlying processes,which leads to the reduction of cost and the improvement of the quality.Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours.This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints.By investigating and analyzing their order handling pro-cesses,this study aims at learning a model that gives insight inspection of the processes and performance analysis.Furthermore,the animation is also performed for the better inspection,diagnostics,and compliance-related questions to specify the system.The configuration of the system and the conformance checking for further enhancement is also addressed in this research.To achieve the objectives,this research uses process mining techniques,i.e.process discovery in the form of formal Petri nets models with the help of process maps,and process refinement through conformance checking and enhancement.Initially,the identified executed process is reconstructed by using the process discovery techniques.Following the reconstruction,we perform a deep analysis for the underlying process to ensure the process improvement and redesigning.Finally,some recommendations are made to improve the enterprise management system processes.展开更多
Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects an...Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.展开更多
The continued expansion of the world population,increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding.Fortunately,the increasing ability to discover and manip...The continued expansion of the world population,increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding.Fortunately,the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars.Many genes have been described in papers as being beneficial for yield increase.However,few of them have been translated into increased yield on farms.In contrast,commercial breeders are facing gene decidophobia,i.e.,puzzled about which gene to choose for breeding among the many identified,a huge chasm between gene discovery and cultivar innovation.The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation.The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds,experimental validation in relevant environments,appropriate crop management,and data reusability.The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security.展开更多
Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extr...Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.展开更多
In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work invo...In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.展开更多
Marine natural products(MNPs)are valuable resources for drug development.To date,17 drugs from marine sources are in clinical use,and 33 pharmaceutical compounds are in clinical trials.Presently the success of drug de...Marine natural products(MNPs)are valuable resources for drug development.To date,17 drugs from marine sources are in clinical use,and 33 pharmaceutical compounds are in clinical trials.Presently the success of drug development from the marine resources is higher than the industry average.It is a feasible strategy to conduct the discovery of druglead compounds based on marine chemical ecology by fully exploiting the pharmacological potential of marine chemical defense matters.In the search for bioactive MNPs,our group has constructed a biological resources library including more than 1500 strains of fungi.Focusing on the strategy of Blue Drug Library,we have discovered a series of novel MNPs with abundant biological functions.Highly efficient and scalable total synthesis of(+)-aniduquinolone A(44)and pesimquinolone I(48)have been completed,which will facilitate access to sufficient quantities of candidates for in vivo pharmacological and toxicological studies.As a nucleoprotein(NP)inhibitor,QLA(75)possesses significant anti-influenza A virus(IAV)activities both in vitro and in vivo.CHNQD-00803(76)is a potent and selective AMP-activated kinase(AMPK)activator that can effectively inhibit metabolic disorders and metabolic dysfunction-associated steatohepatitis(MASH)progression.Moreover,we identified two new candidate molecules with potent anti-hepatocellular carcinoma effects.Particularly,as a natural guanine-nucleotide exchange factors for ADP-ribosylation factor GTPases(Arf-GEFs)inhibitor prodrug,CHNQD-01255(78)is qualified to be developed as a targeted candidate anticancer drug,which may be promising to apply for cancer immunotherapy.Hence,it is evident that MNPs play an important role in drug development.展开更多
It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly eval...It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.展开更多
基金supported by funding from the Bluesand Foundation,Alzheimer's Association(AARG-21-852072 and Bias Frangione Early Career Achievement Award)to EDan Australian Government Research Training Program scholarship and the University of Sydney's Brain and Mind Centre fellowship to AH。
文摘Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments,ongoing progress is required to ensure these are effective,economical,and accessible for the globally ageing population.As such,continued identification of new potential drug targets and biomarkers is critical."Big data"studies,such as proteomics,can generate information on thousands of possible new targets for dementia diagnostics and therapeutics,but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development.In this review,we discuss current tauopathy biomarkers and therapeutics,and highlight areas in need of improvement,particularly when addressing the needs of frail,comorbid and cognitively impaired populations.We highlight biomarkers which have been developed from proteomic data,and outline possible future directions in this field.We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development,and demonstrate its application to our group's recent tau interactome dataset as an example.
文摘The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data.This data can be extremely valuable for executing organizations because the data allows constant monitoring,analyzing,and improving the underlying processes,which leads to the reduction of cost and the improvement of the quality.Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours.This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints.By investigating and analyzing their order handling pro-cesses,this study aims at learning a model that gives insight inspection of the processes and performance analysis.Furthermore,the animation is also performed for the better inspection,diagnostics,and compliance-related questions to specify the system.The configuration of the system and the conformance checking for further enhancement is also addressed in this research.To achieve the objectives,this research uses process mining techniques,i.e.process discovery in the form of formal Petri nets models with the help of process maps,and process refinement through conformance checking and enhancement.Initially,the identified executed process is reconstructed by using the process discovery techniques.Following the reconstruction,we perform a deep analysis for the underlying process to ensure the process improvement and redesigning.Finally,some recommendations are made to improve the enterprise management system processes.
文摘Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.
基金supported by the Sichuan province Science&Technology Department Crops Breeding Project(2021YFYZ0002)。
文摘The continued expansion of the world population,increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding.Fortunately,the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars.Many genes have been described in papers as being beneficial for yield increase.However,few of them have been translated into increased yield on farms.In contrast,commercial breeders are facing gene decidophobia,i.e.,puzzled about which gene to choose for breeding among the many identified,a huge chasm between gene discovery and cultivar innovation.The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation.The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds,experimental validation in relevant environments,appropriate crop management,and data reusability.The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152102 and 92152202)the Advanced Jet Propulsion Innovation Center/AEAC(Grant No.HKCX2022-01-010)。
文摘Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.
基金supported in part by the National Natural Science Foundations of CHINA(Grant No.61771392,No.61771390,No.61871322 and No.61501373)Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(Grant No.201955053002 and No.20185553035)。
文摘In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.
基金supported by the Shandong Province Special Fund ‘Frontier Technology and Free Exploration’ from Laoshan Laboratory (No. 8-01)the National Natural Science Foundation of China (No. 42376116)+3 种基金the Special Funds of Shandong Province for Qingdao National Laboratory of Marine Science and Technology (No. 2022QN LM030003)the State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Guangxi Normal University (No. CMEMR2023-B16)the National Key Research and Development Program of China (No. 2022YFC2601305)the Innovation Center for Academicians of Hainan Province, and the Fundamental Research Funds for the Central Universities (No. 202461059)
文摘Marine natural products(MNPs)are valuable resources for drug development.To date,17 drugs from marine sources are in clinical use,and 33 pharmaceutical compounds are in clinical trials.Presently the success of drug development from the marine resources is higher than the industry average.It is a feasible strategy to conduct the discovery of druglead compounds based on marine chemical ecology by fully exploiting the pharmacological potential of marine chemical defense matters.In the search for bioactive MNPs,our group has constructed a biological resources library including more than 1500 strains of fungi.Focusing on the strategy of Blue Drug Library,we have discovered a series of novel MNPs with abundant biological functions.Highly efficient and scalable total synthesis of(+)-aniduquinolone A(44)and pesimquinolone I(48)have been completed,which will facilitate access to sufficient quantities of candidates for in vivo pharmacological and toxicological studies.As a nucleoprotein(NP)inhibitor,QLA(75)possesses significant anti-influenza A virus(IAV)activities both in vitro and in vivo.CHNQD-00803(76)is a potent and selective AMP-activated kinase(AMPK)activator that can effectively inhibit metabolic disorders and metabolic dysfunction-associated steatohepatitis(MASH)progression.Moreover,we identified two new candidate molecules with potent anti-hepatocellular carcinoma effects.Particularly,as a natural guanine-nucleotide exchange factors for ADP-ribosylation factor GTPases(Arf-GEFs)inhibitor prodrug,CHNQD-01255(78)is qualified to be developed as a targeted candidate anticancer drug,which may be promising to apply for cancer immunotherapy.Hence,it is evident that MNPs play an important role in drug development.
基金supported by the National Natural Science Foundation of China (12072365)the Natural Science Foundation of Hunan Province of China (2020JJ4657)。
文摘It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
