Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part c...Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.展开更多
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
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
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
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
BACKGROUND Colorectal cancer(CRC)is the third most frequent and the second most fatal cancer.The search for more effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CRC develop...BACKGROUND Colorectal cancer(CRC)is the third most frequent and the second most fatal cancer.The search for more effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies.Ubiquitin-specific peptidases(USPs),the largest group of the deubiquitinase protein family,have long been implicated in various cancers.There have been numerous studies on the role of USPs in CRC;however,a comprehensive view of this role is lacking.AIM To provide a systematic review of the studies investigating the roles and functions of USPs in CRC.METHODS We systematically queried the MEDLINE(via PubMed),Scopus,and Web of Science databases.RESULTS Our study highlights the pivotal role of various USPs in several processes implicated in CRC:Regulation of the cell cycle,apoptosis,cancer stemness,epithelial–mesenchymal transition,metastasis,DNA repair,and drug resistance.The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC.The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms.CONCLUSION Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.展开更多
We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently develo...We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.展开更多
Thallium has been used geochemical exploration of gold deposits. However, as an indicator element in searching for hydrothermal the T1 minerals and mineralization are rare in nature. Lorandite T1AsS2, a relatively un...Thallium has been used geochemical exploration of gold deposits. However, as an indicator element in searching for hydrothermal the T1 minerals and mineralization are rare in nature. Lorandite T1AsS2, a relatively uncommon mineral, has been dominantly discovered in some Carlin gold deposits, and minor Sb- Hg, U and Pb-Zn-Ag deposits.展开更多
Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support Syste...Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support System(BITOLA)to screen out the candidate intermediate molecular(CIM)"Gene or Gene Product”that are related to AAA and DM.The dataset of GSE13760,GSE7084,GSE57691,GSE47472 were used to analyze the differentially expressed genes(DEGs)of AAA and DM compared to the healthy status.We used the online tool ofVenny 2.1 assisted by manual checking to identify the overlapped DEGs with the CIMs.The Human eFP Browser was applied to examine the tissue specific expression levels of the detected genes in order to recognize strong expressed genes in both human artery and pancreatic tissue.Results There were 86 CIMs suggested by the closed BITOLA system.Among all the DEGs of AAA and DM,8 genes in GSE7084(ISG20,ITGAX,DSTN,CCL5,CCR5,AGTR1,CD19,CD44)and 2 genes in GSE 13760(PSMD12,FAS)were found to be overlapped with the 86 CIMs.By manual checking and comparing with tissuespecific gene data through Human eFP Browser,the gene PSMD12(proteasome 26S subunit,non-ATPase 12)was recognized to be strongly expressed in both the aorta and pancreatic tissue.Conclusion We proposed a hypothesis through text mining that PSMD12 might be involved or potentially involved in the interconnection between AAA and DM,which may provide a new clue for studies on novel therapeutic strategies for the two diseases.展开更多
A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and know...A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.展开更多
A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonl...A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.展开更多
Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been de...Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.展开更多
Metabolomics has emerged as a valuable tool in drug discovery and development,providing new insights into the mechanisms of action and toxicity of potential therapeutic agents.Metabolomics focuses on the comprehensive...Metabolomics has emerged as a valuable tool in drug discovery and development,providing new insights into the mechanisms of action and toxicity of potential therapeutic agents.Metabolomics focuses on the comprehensive analysis of primary as well as secondary metabolites,within biological systems.Metabolomics provides a comprehensive understanding of the metabolic changes that occur within microbial pathogens when exposed to therapeutic agents,thus allowing for the identification of unique metabolic targets that can be exploited for therapeutic intervention.This approach can also uncover key metabolic pathways essential for survival,which can serve as potential targets for novel antibiotics.By analyzing the metabolites produced by diverse microbial communities,metabolomics can guide the discovery of previously unexplored sources of antibiotics.This review explores some examples that enable medicinal chemists to optimize drug structure,enhancing efficacy and minimizing toxicity via metabolomic approaches.展开更多
This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and fa...This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.展开更多
The solute carrier family 12(SLC12)of cation-chloride cotransporters(CCCs)comprises potassium chloride cotransporters(KCCs,e.g.KCC1,KCC2,KCC3,and KCC4)-mediated Cl^(-)extrusion,and sodium potassium chloride cotranspor...The solute carrier family 12(SLC12)of cation-chloride cotransporters(CCCs)comprises potassium chloride cotransporters(KCCs,e.g.KCC1,KCC2,KCC3,and KCC4)-mediated Cl^(-)extrusion,and sodium potassium chloride cotransporters(N[K]CCs,NKCC1,NKCC2,and NCC)-mediated Cl^(-)loading.The CCCs play vital roles in cell volume regulation and ion homeostasis.Gain-of-function or loss-of-function of these ion transporters can cause diseases in many tissues.In recent years,there have been considerable advances in our understanding of CCCs'control mechanisms in cell volume regulations,with many techniques developed in studying the functions and activities of CCCs.Classic approaches to directly measure CCC activity involve assays that measure the transport of potassium substitutes through the CCCs.These techniques include the ammonium pulse technique,radioactive or nonradioactive rubidium ion uptakeassay,and thallium ion-uptake assay.CCCs'activity can also be indirectly observed by measuring gaminobutyric acid(GABA)activity with patch-clamp electrophysiology and intracellular chloride concentration with sensitive microelectrodes,radiotracer^(36)Cl^(-),and fluorescent dyes.Other techniques include directly looking at kinase regulatory sites phosphorylation,flame photometry,22Nat uptake assay,structural biology,molecular modeling,and high-throughput drug screening.This review summarizes the role of CCCs in genetic disorders and cell volume regulation,current methods applied in studying CCCs biology,and compounds developed that directly or indirectly target the CCCs for disease treatments.展开更多
Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth d...Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.展开更多
Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. On...Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.展开更多
文摘Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.
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.
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 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 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.
文摘BACKGROUND Colorectal cancer(CRC)is the third most frequent and the second most fatal cancer.The search for more effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies.Ubiquitin-specific peptidases(USPs),the largest group of the deubiquitinase protein family,have long been implicated in various cancers.There have been numerous studies on the role of USPs in CRC;however,a comprehensive view of this role is lacking.AIM To provide a systematic review of the studies investigating the roles and functions of USPs in CRC.METHODS We systematically queried the MEDLINE(via PubMed),Scopus,and Web of Science databases.RESULTS Our study highlights the pivotal role of various USPs in several processes implicated in CRC:Regulation of the cell cycle,apoptosis,cancer stemness,epithelial–mesenchymal transition,metastasis,DNA repair,and drug resistance.The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC.The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms.CONCLUSION Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.
基金supported by the NSF(No.DMS-1813071)(Chou)and the AFSOR(No.FA9550-22-1-0011)(Xiu).
文摘We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.
基金supported by the National Science Foundation of China(grants No.41372090 and 41573042)the National Special Research Programs for Non-Profit Trades (grant No.201311136)Basic Scientific Research Operation Cost of State-Leveled Public Welfare Scientific Research Courtyard(grant No.K1203)
文摘Thallium has been used geochemical exploration of gold deposits. However, as an indicator element in searching for hydrothermal the T1 minerals and mineralization are rare in nature. Lorandite T1AsS2, a relatively uncommon mineral, has been dominantly discovered in some Carlin gold deposits, and minor Sb- Hg, U and Pb-Zn-Ag deposits.
文摘Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support System(BITOLA)to screen out the candidate intermediate molecular(CIM)"Gene or Gene Product”that are related to AAA and DM.The dataset of GSE13760,GSE7084,GSE57691,GSE47472 were used to analyze the differentially expressed genes(DEGs)of AAA and DM compared to the healthy status.We used the online tool ofVenny 2.1 assisted by manual checking to identify the overlapped DEGs with the CIMs.The Human eFP Browser was applied to examine the tissue specific expression levels of the detected genes in order to recognize strong expressed genes in both human artery and pancreatic tissue.Results There were 86 CIMs suggested by the closed BITOLA system.Among all the DEGs of AAA and DM,8 genes in GSE7084(ISG20,ITGAX,DSTN,CCL5,CCR5,AGTR1,CD19,CD44)and 2 genes in GSE 13760(PSMD12,FAS)were found to be overlapped with the 86 CIMs.By manual checking and comparing with tissuespecific gene data through Human eFP Browser,the gene PSMD12(proteasome 26S subunit,non-ATPase 12)was recognized to be strongly expressed in both the aorta and pancreatic tissue.Conclusion We proposed a hypothesis through text mining that PSMD12 might be involved or potentially involved in the interconnection between AAA and DM,which may provide a new clue for studies on novel therapeutic strategies for the two diseases.
文摘A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.
文摘A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.
基金funded by the National Natural Science Foundation of China (NSFC, 31900046, 81972085, 82172465 and 32161133022)the Guangdong Provincial Key Laboratory of Advanced Biomaterials (2022B1212010003)+7 种基金the National Science and Technology Innovation 2030 Major Program (2022ZD0211900)the Shenzhen Key Laboratory of Computer Aided Drug Discovery (ZDSYS20201230165400001)the Chinese Academy of Science President’s International Fellowship Initiative (PIFI)(2020FSB0003)the Guangdong Retired Expert (granted by Guangdong Province)the Shenzhen Pengcheng ScientistNSFC-SNSF Funding (32161133022)Alpha Mol&SIAT Joint LaboratoryShenzhen Government Top-talent Working Funding and Guangdong Province Academician Work Funding。
文摘Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.
文摘Metabolomics has emerged as a valuable tool in drug discovery and development,providing new insights into the mechanisms of action and toxicity of potential therapeutic agents.Metabolomics focuses on the comprehensive analysis of primary as well as secondary metabolites,within biological systems.Metabolomics provides a comprehensive understanding of the metabolic changes that occur within microbial pathogens when exposed to therapeutic agents,thus allowing for the identification of unique metabolic targets that can be exploited for therapeutic intervention.This approach can also uncover key metabolic pathways essential for survival,which can serve as potential targets for novel antibiotics.By analyzing the metabolites produced by diverse microbial communities,metabolomics can guide the discovery of previously unexplored sources of antibiotics.This review explores some examples that enable medicinal chemists to optimize drug structure,enhancing efficacy and minimizing toxicity via metabolomic approaches.
基金This paper is supported by Research Grant Number:PP-FTSM-2022.
文摘This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.
基金We are very grateful for the financial support from the National Natural Science Foundation of China(Grant Nos.:82170406,81970238,and 32111530119)Shanghai Municipal Science and Technology Major Project,China(Grant No.:2018SHZDZX01)+1 种基金The Royal Society UK(Grant No.:IEC\NSFC\201094)the Commonwealth Scholarship Commission UK(Grant No.:NGCA-2020-43).
文摘The solute carrier family 12(SLC12)of cation-chloride cotransporters(CCCs)comprises potassium chloride cotransporters(KCCs,e.g.KCC1,KCC2,KCC3,and KCC4)-mediated Cl^(-)extrusion,and sodium potassium chloride cotransporters(N[K]CCs,NKCC1,NKCC2,and NCC)-mediated Cl^(-)loading.The CCCs play vital roles in cell volume regulation and ion homeostasis.Gain-of-function or loss-of-function of these ion transporters can cause diseases in many tissues.In recent years,there have been considerable advances in our understanding of CCCs'control mechanisms in cell volume regulations,with many techniques developed in studying the functions and activities of CCCs.Classic approaches to directly measure CCC activity involve assays that measure the transport of potassium substitutes through the CCCs.These techniques include the ammonium pulse technique,radioactive or nonradioactive rubidium ion uptakeassay,and thallium ion-uptake assay.CCCs'activity can also be indirectly observed by measuring gaminobutyric acid(GABA)activity with patch-clamp electrophysiology and intracellular chloride concentration with sensitive microelectrodes,radiotracer^(36)Cl^(-),and fluorescent dyes.Other techniques include directly looking at kinase regulatory sites phosphorylation,flame photometry,22Nat uptake assay,structural biology,molecular modeling,and high-throughput drug screening.This review summarizes the role of CCCs in genetic disorders and cell volume regulation,current methods applied in studying CCCs biology,and compounds developed that directly or indirectly target the CCCs for disease treatments.
文摘Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.
文摘Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.