Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demand...Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource utilization and performance optimization.Scheduling strategy optimization,as a key direction for improving Spark’s execution efficiency,has attracted widespread attention.This paper first introduces the basic theories of Spark,compares several default scheduling strategies,and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency.Subsequently,existing scheduling optimization schemes are summarized based on three scheduling modes:load characteristics,cluster characteristics,and matching of both,and representative algorithms are analyzed in terms of performance indicators and applicable scenarios,comparing the advantages and disadvantages of different scheduling modes.The article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production environments.Finally,the limitations of the existing schemes are analyzed,and prospects are envisioned.展开更多
Classification of quantum phases is one of the most important areas of research in condensed matter physics.In this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning.Fi...Classification of quantum phases is one of the most important areas of research in condensed matter physics.In this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning.Firstly,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave model.The unsupervised learning results match well with those obtained through traditional numerical diagonalization.Finally,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.展开更多
BACKGROUND Although thymopoietin(TMPO)has been elucidated to be overexpressed in cancers,its underlying mechanisms are not yet fully understood.AIM To investigate the expression and clinical significance of TMPO in pa...BACKGROUND Although thymopoietin(TMPO)has been elucidated to be overexpressed in cancers,its underlying mechanisms are not yet fully understood.AIM To investigate the expression and clinical significance of TMPO in papillary thyroid carcinoma(PTC).METHODS Databases such as Gene Expression Omnibus,The Cancer Genome Atlas Proand summary receiver operating characteristic curves were plotted to evaluate diagnostic performance.A Gene Set Enrichment Analysis enrichment analysis was conducted to identify TMPO-related signaling pathways.A protein interaction network was constructed to identify hub genes.The impact of TMPO on PTC cell proliferation and the effects of its knockout were analyzed using clustered regularly interspaced short palindromic repeats(CRISPR)knockout screening and the Cancer Cell Line Encyclopedia database.RESULTS The TMPO protein was significantly overexpressed in PTC tissues,primarily localized in the cytoplasm and nuclear membrane.The mRNA level analysis showed mild overexpression of TMPO in PTC tissues,with a certain discriminatory value(area under the curve=0.66).TMPO may promote cancer through involvement in cell adhesion,focal adhesion,leukocyte migration,and multiple cancer-related signaling pathways.Additionally,CRISPR gene knockout experiments confirmed that TMPO knockout significantly inhibited the proliferation of PTC cell lines,indicating its important role in tumor growth.CONCLUSION TMPO is overexpressed in PTC and may serve as a therapeutic target and molecular biomarker for PTC.展开更多
BACKGROUND Laryngeal squamous cell carcinoma(LSCC)is a prevalent head and neck malignancy with suboptimal survival rates due to late detection and therapeutic resistance.AIM To investigate chaperonin-containing TCP1 s...BACKGROUND Laryngeal squamous cell carcinoma(LSCC)is a prevalent head and neck malignancy with suboptimal survival rates due to late detection and therapeutic resistance.AIM To investigate chaperonin-containing TCP1 subunit 3(CCT3)expression and its clinical implications,and its effects on LSCC cell growth.METHODS Systematic data on CCT3 mRNA expression were collected from biomedical databases,and integrated further based on the standardized mean difference and the summary receiver operating characteristic curve.Single-cell RNA-seq data were mined to validate the expression level of CCT3 mRNA.In-house immunohistochemistry was performed to explore the CCT3 protein levels of clinical LSCC samples and their relationship with clinical parameters.The growth function of LSCC cell was analyzed using CRISPR knockout screening.CCT3-related signaling pathway analyses were conducted using gene set enrichment analysis.Protein-protein interaction network construction was performed to identify hub genes.RESULTS CCT3 mRNA was significantly overexpressed in 269 LSCC tissues cases across multiple independent datasets(standardized mean difference=32,area under the curve=0.93);At the translational level,the in-house immunohistochemical analysis further demonstrated the consistent upregulation of CCT3 protein in 88 cases of LSCC samples(58 non-LSCC samples vs 30 LSCC samples,P=1.4e^(-14)).Analysis of clinical parameters showed no significant differences among subgroup.Functional characterization with clustered regularly interspaced short palindromic repeats--mediated gene knockout revealed that depletion of CCT3 potently suppressed LSCC cell viability in vitro.Gene set enrichment analysis indicated that CCT3 was markedly associated with several key oncogenic pathways,including extracellular matrix receptor interaction and cell cycle regulation pathways.CONCLUSION CCT3 upregulation in LSCC may influence cellular growth by regulating related pathways,indicating its potential as a biomarker and therapeutic target for LSCC.展开更多
The hardness, electronic, and elastic properties of 5d transition metal dibofides with ReB2 structure are studied theoretically by using the first principles calculations. The calculated results are in good agreement ...The hardness, electronic, and elastic properties of 5d transition metal dibofides with ReB2 structure are studied theoretically by using the first principles calculations. The calculated results are in good agreement with the previous experimental and theoretical results. Empirical formulas for estimating the hardness and partial number of effective free electrons for each bond in multibond compounds with metallicity are presented. Based on the formulas, IrB2 has the largest hardness of 21.8 GPa, followed by OsB2 （21.0 GPa） and ReB2 （19.7 GPa）, indicating that they are good candidates as hard materials.展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
Automatically answer math word problems is a challenging task in artificial intelligence.Previous solvers constructed mathematical expressions in sequence or binary tree.However,these approaches may suffer from the fo...Automatically answer math word problems is a challenging task in artificial intelligence.Previous solvers constructed mathematical expressions in sequence or binary tree.However,these approaches may suffer from the following issues:Models relying on such structures exhibit fixed-order reasoning(e.g.,left-to-right),limiting flexibility and increasing error susceptibility;prior models rely on autoregressive reasoning in a single pass,accumulating minor errors(e.g.,incorrect math symbols)during generation,resulting in reduced accuracy.To address the above issues,we emulate the human“check and modify”process in reasoning and propose a unified M-tree self-correction solver(UTSCSolver)by iterative inference with self-correction mechanism.First,we use an iterative,non-autoregressive process for generating mathematical expressions,free from fixed generation orders to handle complex and diverse problems.Additionally,we design a self-correction mechanism based on alternating execution between a generator and a discriminator.This module iteratively detects and rectifies errors in generated expressions,leveraging previous iteration information for subsequent generation guidance.Experimental results show that our UTSC-Solver outperforms traditional models in accuracy on two popular datasets,while it improves the interpretability of mathematical reasoning.展开更多
AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions ...AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.展开更多
BACKGROUND ANAPC1,a key regulator of the ubiquitination in tumour development,has not been thoroughly studied in hepatocellular carcinoma(HCC).AIM To elucidate the expression of ANAPC1 in HCC and its potential regulat...BACKGROUND ANAPC1,a key regulator of the ubiquitination in tumour development,has not been thoroughly studied in hepatocellular carcinoma(HCC).AIM To elucidate the expression of ANAPC1 in HCC and its potential regulatory mechanism related to ubiquitination.METHODS Bulk RNA(RNA sequencing and microarrays),immunohistochemistry(IHC)tissues,and single-cell RNA sequencing(scRNA-seq)data were integrated to comprehensively investigate ANAPC1 expression in HCC.Clustered regularly interspaced short palindromic repeats analysis was performed to assess growth in HCC cell lines following ANAPC1 knockout.Enrichment analyses were conducted to explore the functions of ANAPC1.ScRNA-seq data was used to examine the cell cycle and metabolic levels.CellChat analysis was applied to investigate the interactions between ANAPC1 and different cell types.The relationship between ANAPC1 expression and drug concentration was analyzed.RESULTS ANAPC1 messenger RNA was found to be upregulated in bulk RNA,IHC tissues samples and malignant hepatocytes.The proliferation of JHH2 cell lines was most significantly inhibited after ANAPC1 knockdown.In biological pathways,the development of HCC was found to be linked to the regulation of ubiquitin-mediated proteolysis.Additionally,scRNA-seq results indicated that highly expressed ANAPC1 was in the G2/M phase,with increased glycolysis/gluconeogenesis activity.A CellChat analysis showed that ANAPC1 was associated with the regulation of the migration inhibitory factor-(cluster of differentiation 74+C-X-C chemokine receptor type 4)pathway.Higher ANAPC1 expression correlated with stronger effects of sorafenib,dasatinib,ibrutinib,lapatinib,nilotinib and afatinib.CONCLUSION The high expression level of ANAPC1 may regulate the cell cycle and metabolic levels of HCC through the ubiquitination-related pathway,thereby promoting disease progression.展开更多
BACKGROUND The prevalence of colorectal cancer(CRC)in younger people is increasing.Despite advances in precision medicine,the challenges of drug resistance and high costs persist.Nitidine chloride(NC)has pharmacologic...BACKGROUND The prevalence of colorectal cancer(CRC)in younger people is increasing.Despite advances in precision medicine,the challenges of drug resistance and high costs persist.Nitidine chloride(NC)has pharmacological potential,and kinesin family member 20A(KIF20A)is overexpressed in various tumors;however,their interaction in CRC remains unexplored.AIM To investigate the KIF20A expression characteristics in CRC cells and determine whether it is a potential target gene for NC in inhibiting CRC treatment.METHODS Single-cell RNA sequencing(scRNA-seq),spatial transcriptomics,and mRNA expression profiling were used to analyze KIF20A expression in CRC cells.Immunohistochemical staining was used to verify KIF20A expression in 416 clinical samples(208 CRC tissue samples and 208 noncancerous control tissue samples).Clustered regularly interspaced short palindromic repeats(CRISPR)technology was used to evaluate the impact of knocking out KIF20A on CRC cell growth.Molecular docking was applied to analyze NC–KIF20A binding.Finally,RNA sequencing and functional enrichment analysis were performed to explore the mechanism of action of NC in CRC cells.RESULTS Treating HCT116 cells with NC was found to significantly downregulate KIF20A(P<0.05),and the molecular docking analysis revealed high-affinity binding between NC and KIF20A(binding energy=-9.6 kcal/mol).The scRNA-seq,spatial transcriptomics,and mRNA expression profiling results confirmed the significantly high expression of KIF20A in CRC tissues(standardized mean difference=1.33,95%confidence interval:0.885-1.77,summary receiver operating characteristic curve area=0.94).The immunohistochemical analysis of the clinical samples showed high KIF20A expression in the CRC tissues(P<0.05),with significant correlation between the level of expression and gender,tumor size,and tumor grade(P<0.05).Knocking out KIF20A significantly inhibited the growth of various CRC cell lines(CRISPR score<-0.3).The functional enrichment analysis indicated that NC may inhibit CRC by disrupting several biological processes,such as mitotic nuclear division,chromosome segregation,and microtubule binding.CONCLUSION Our results indicate that NC binds to KIF20A with high affinity and downregulates its expression in CRC cells,leading to reduced proliferation.Hence,NC has promise as a therapeutic agent in the treatment of CRC,and targeting KIF20A also has potential as a therapeutic strategy.Further KIF20A knockout studies are needed to confirm the binding specificity and mechanistic roles of NC in CRC.展开更多
The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks ...The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units （GPU）. This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is a major health challenge with high incidence and poor survival rates in China.Systemic therapies,particularly tyrosine kinase inhibitors(TKIs),are the first-line treatment fo...BACKGROUND Hepatocellular carcinoma(HCC)is a major health challenge with high incidence and poor survival rates in China.Systemic therapies,particularly tyrosine kinase inhibitors(TKIs),are the first-line treatment for advanced HCC,but resistance is common.The Rho GTPase family member Rho GTPase activating protein 12(ARHGAP12),which regulates cell adhesion and invasion,is a potential therapeutic target for overcoming TKI resistance in HCC.However,no studies on the expression of ARHGAP12 in HCC and its role in resistance to TKIs have been reported.AIM To unveil the expression of ARHGAP12 in HCC,its role in TKI resistance and its potential associated pathways.METHODS This study used single-cell RNA sequencing(scRNA-seq)to evaluate ARHGAP12 mRNA levels and explored its mechanisms through enrichment analysis.CellChat was used to investigate focal adhesion(FA)pathway regulation.We integrated bulk RNA data(RNA-seq and microarray),immunohistochemistry and proteomics to analyze ARHGAP12 mRNA and protein levels,correlating with clinical outcomes.We assessed ARHGAP12 expression in TKI-resistant HCC,integrated conventional HCC to explore its mechanism,identified intersecting FA pathway genes with scRNA-seq data and evaluated its response to TKI and immunotherapy.RESULTS ARHGAP12 mRNA was found to be highly expressed in malignant hepatocytes and to regulate FA.In malignant hepatocytes in high-score FA groups,MDK-[integrin alpha 6(ITGA6)+integrinβ-1(ITGB1)]showed specificity in ligand-receptor interactions.ARHGAP12 mRNA and protein were upregulated in bulk RNA,immunohistochemistry and proteomics,and higher expression was associated with a worse prognosis.ARHGAP12 was also found to be a TKI resistance gene that regulated the FA pathway.ITGB1 was identified as a crossover gene in the FA pathway in both scRNA-seq and bulk RNA.High expression of ARHGAP12 was associated with adverse reactions to sorafenib,cabozantinib and regorafenib,but not to immunotherapy.CONCLUSION ARHGAP12 expression is elevated in HCC and TKI-resistant HCC,and its regulatory role in FA may underlie the TKI-resistant phenotype.展开更多
Background Eye tracking te chnology is receiving increased attention in the field of virtual reality.Specifically,future gaze prediction is crucial in pre-computation for many applications such as gaze-contingent rend...Background Eye tracking te chnology is receiving increased attention in the field of virtual reality.Specifically,future gaze prediction is crucial in pre-computation for many applications such as gaze-contingent rendering,advertisement placement,and content-based design.To explore future gaze prediction,it is necessary to analyze the temporal continuity of visual attention in immersive virtual reality.Methods In this paper,the concept of temporal continuity of visual attention is presented.Subsequently,an autocorrelation function method is proposed to evaluate the temporal continuity.Thereafter,the temporal continuity is analyzed in both free-viewing and task-oriented conditions.Results Specifically,in free-viewing conditions,the analysis of a free-viewing gaze dataset indicates that the temporal continuity performs well only within a short time interval.A task-oriented game scene condition was created and conducted to collect users'gaze data.An analysis of the collected gaze data finds the temporal continuity has a similar performance with that of the free-viewing conditions.Temporal continuity can be applied to future gaze prediction and if it is good,users'current gaze positions can be directly utilized to predict their gaze positions in the future.Conclusions The current gaze's future prediction performances are further evaluated in both free-viewing and task-oriented conditions and discover that the current gaze can be efficiently applied to the task of short-term future gaze prediction.The task of long-term gaze prediction still remains to be explored.展开更多
A Verilog-VHDL translating method directed by simulation semantics is presented. Based on the analysis and comparison, three steps are taken to implement the translation. Through semantic analyzing and syntax tree rec...A Verilog-VHDL translating method directed by simulation semantics is presented. Based on the analysis and comparison, three steps are taken to implement the translation. Through semantic analyzing and syntax tree reconstructing before translation, the main part of Verilog is supported. According to the level in the design hierarchy, the modules are translated in down-top order, and that results in a correct VHDL declaration-reference order. The translation rules of assignment statements and delay/timing constructs are also explained in detail. This method has been successfully implemented in the translator developed by the authors. The correctness has been validated by many examples.展开更多
The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between con...The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to (-M,+N).The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of Mand Naffect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90.31%, and 89.62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.展开更多
How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paperpresents a new event detection method for the current new event det...How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paperpresents a new event detection method for the current new event detection system, based on sorted subtopic matching algorithm and constructs the entire design framework. In this p^per, the subtopics contained in old topics （or news stories） are sorted in descending order according to their importance to the topic（or news stories）, and form a sorted subtopic sequence. In the process of subtopic matching, subtopic scoring matrix is used to determine whether a new story is reporting a new event. Experimental results show that the sorted subtopic matching model improved the accuracy and effectiveness ofthenew event detection system in cyberspace.展开更多
In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the bio...In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.展开更多
Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that...Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.展开更多
Unipolar memristive devices are an important kind of resistive switching devices. However, few circuit models of them have been proposed. In this paper, we propose the SPICE modeling of flux-controlled unipolar memris...Unipolar memristive devices are an important kind of resistive switching devices. However, few circuit models of them have been proposed. In this paper, we propose the SPICE modeling of flux-controlled unipolar memristive devices based on the memristance versus state map. Using our model, the flux thresholds, ON and OFF resistance, and compliance current can easily be set as model parameters. We simulate the model in HSPICE using model parameters abstracted from real devices, and the simulation results show that the proposed model caters to the real device data very well, thus demonstrating that the model is correct. Using the same modeling methodology, the SPICE model of charge-controlled unipolar memristive devices could also be developed. The proposed model could be used to model resistive memory cells, logical gates as well as synapses in artificial neural networks.展开更多
基金supported in part by the Key Research and Development Program of Shaanxi under Grant 2023-ZDLGY-34.
文摘Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource utilization and performance optimization.Scheduling strategy optimization,as a key direction for improving Spark’s execution efficiency,has attracted widespread attention.This paper first introduces the basic theories of Spark,compares several default scheduling strategies,and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency.Subsequently,existing scheduling optimization schemes are summarized based on three scheduling modes:load characteristics,cluster characteristics,and matching of both,and representative algorithms are analyzed in terms of performance indicators and applicable scenarios,comparing the advantages and disadvantages of different scheduling modes.The article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production environments.Finally,the limitations of the existing schemes are analyzed,and prospects are envisioned.
基金Project supported by the Natural Science Foundation of Nanjing University of Posts and Telecommunications(Grant Nos.NY223109,NY220119,and NY221055)China Postdoctoral Science Foundation(Grant No.2022M721693)the National Natural Science Foundation of China(Grant No.12404365)。
文摘Classification of quantum phases is one of the most important areas of research in condensed matter physics.In this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning.Firstly,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave model.The unsupervised learning results match well with those obtained through traditional numerical diagonalization.Finally,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.
基金Supported by Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project,No.Z-A20220521Guangxi Higher Education Undergraduate Teaching Reform Project,No.2022JGA147The National College Students’Innovation and Entrepreneurship Training Program,No.202310598042.
文摘BACKGROUND Although thymopoietin(TMPO)has been elucidated to be overexpressed in cancers,its underlying mechanisms are not yet fully understood.AIM To investigate the expression and clinical significance of TMPO in papillary thyroid carcinoma(PTC).METHODS Databases such as Gene Expression Omnibus,The Cancer Genome Atlas Proand summary receiver operating characteristic curves were plotted to evaluate diagnostic performance.A Gene Set Enrichment Analysis enrichment analysis was conducted to identify TMPO-related signaling pathways.A protein interaction network was constructed to identify hub genes.The impact of TMPO on PTC cell proliferation and the effects of its knockout were analyzed using clustered regularly interspaced short palindromic repeats(CRISPR)knockout screening and the Cancer Cell Line Encyclopedia database.RESULTS The TMPO protein was significantly overexpressed in PTC tissues,primarily localized in the cytoplasm and nuclear membrane.The mRNA level analysis showed mild overexpression of TMPO in PTC tissues,with a certain discriminatory value(area under the curve=0.66).TMPO may promote cancer through involvement in cell adhesion,focal adhesion,leukocyte migration,and multiple cancer-related signaling pathways.Additionally,CRISPR gene knockout experiments confirmed that TMPO knockout significantly inhibited the proliferation of PTC cell lines,indicating its important role in tumor growth.CONCLUSION TMPO is overexpressed in PTC and may serve as a therapeutic target and molecular biomarker for PTC.
基金Supported by the National Natural Science Foundation of China,No.82160213 and No.U22A2022the Guangxi Natural Science Foundation,No.2023GXNSFAA026029,No.2024GXNSFBA010059,and No.2024GXNSFAA010079。
文摘BACKGROUND Laryngeal squamous cell carcinoma(LSCC)is a prevalent head and neck malignancy with suboptimal survival rates due to late detection and therapeutic resistance.AIM To investigate chaperonin-containing TCP1 subunit 3(CCT3)expression and its clinical implications,and its effects on LSCC cell growth.METHODS Systematic data on CCT3 mRNA expression were collected from biomedical databases,and integrated further based on the standardized mean difference and the summary receiver operating characteristic curve.Single-cell RNA-seq data were mined to validate the expression level of CCT3 mRNA.In-house immunohistochemistry was performed to explore the CCT3 protein levels of clinical LSCC samples and their relationship with clinical parameters.The growth function of LSCC cell was analyzed using CRISPR knockout screening.CCT3-related signaling pathway analyses were conducted using gene set enrichment analysis.Protein-protein interaction network construction was performed to identify hub genes.RESULTS CCT3 mRNA was significantly overexpressed in 269 LSCC tissues cases across multiple independent datasets(standardized mean difference=32,area under the curve=0.93);At the translational level,the in-house immunohistochemical analysis further demonstrated the consistent upregulation of CCT3 protein in 88 cases of LSCC samples(58 non-LSCC samples vs 30 LSCC samples,P=1.4e^(-14)).Analysis of clinical parameters showed no significant differences among subgroup.Functional characterization with clustered regularly interspaced short palindromic repeats--mediated gene knockout revealed that depletion of CCT3 potently suppressed LSCC cell viability in vitro.Gene set enrichment analysis indicated that CCT3 was markedly associated with several key oncogenic pathways,including extracellular matrix receptor interaction and cell cycle regulation pathways.CONCLUSION CCT3 upregulation in LSCC may influence cellular growth by regulating related pathways,indicating its potential as a biomarker and therapeutic target for LSCC.
文摘The hardness, electronic, and elastic properties of 5d transition metal dibofides with ReB2 structure are studied theoretically by using the first principles calculations. The calculated results are in good agreement with the previous experimental and theoretical results. Empirical formulas for estimating the hardness and partial number of effective free electrons for each bond in multibond compounds with metallicity are presented. Based on the formulas, IrB2 has the largest hardness of 21.8 GPa, followed by OsB2 （21.0 GPa） and ReB2 （19.7 GPa）, indicating that they are good candidates as hard materials.
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
基金supported by the National Natural Science Foundation of China(62106244)the Fundamental Research Funds for the Central Universities(WK2150110021)the University Synergy Innovation Program of Anhui Province(GXXT-2022-042).
文摘Automatically answer math word problems is a challenging task in artificial intelligence.Previous solvers constructed mathematical expressions in sequence or binary tree.However,these approaches may suffer from the following issues:Models relying on such structures exhibit fixed-order reasoning(e.g.,left-to-right),limiting flexibility and increasing error susceptibility;prior models rely on autoregressive reasoning in a single pass,accumulating minor errors(e.g.,incorrect math symbols)during generation,resulting in reduced accuracy.To address the above issues,we emulate the human“check and modify”process in reasoning and propose a unified M-tree self-correction solver(UTSCSolver)by iterative inference with self-correction mechanism.First,we use an iterative,non-autoregressive process for generating mathematical expressions,free from fixed generation orders to handle complex and diverse problems.Additionally,we design a self-correction mechanism based on alternating execution between a generator and a discriminator.This module iteratively detects and rectifies errors in generated expressions,leveraging previous iteration information for subsequent generation guidance.Experimental results show that our UTSC-Solver outperforms traditional models in accuracy on two popular datasets,while it improves the interpretability of mathematical reasoning.
基金funded by the Taiwan Comprehensive University System and the National Science and Technology Council of Taiwan under grant number NSTC 111-2410-H-019-006-MY3Additionally,this work was financially/partially supported by the Advanced Institute of Manufacturing with High-tech Innovations(AIM-HI)from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan+1 种基金the National Natural Science Foundation of China,No.62402444the Zhejiang Provincial Natural Science Foundation of China,No.LQ24F020012.
文摘AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.
基金Co-first authors:Yu-Xing Tang 0000-0003-4382-4942Co-first authors:Wei-Zi Wu+8 种基金Corresponding author:Gang Chen,MD,Professor,Department of Pathology,The First Affiliated Hospital of Guangxi Medical University,No.6 Shuangyong Road,Nanning 530021,Guangxi Zhuang Autonomous Region,China.chengang@gxmu.edu.cn,0000-0003-2402-2987Co-corresponding authors:Yan-Ting ZhanSheng-Sheng Zhou,0000-0003-2414-460XDa-Tong Zeng,0000-0002-3338-4122Guang-Cai Zheng,0009-0001-5921-6688Rong-Quan He,0000-0002-7752-2080Di-Yuan Qin,0009-0003-3214-4762Wan-Ying Huang,0000-0002-8314-5963Yu-Lu Tang,0009-0004-0462-618X。
文摘BACKGROUND ANAPC1,a key regulator of the ubiquitination in tumour development,has not been thoroughly studied in hepatocellular carcinoma(HCC).AIM To elucidate the expression of ANAPC1 in HCC and its potential regulatory mechanism related to ubiquitination.METHODS Bulk RNA(RNA sequencing and microarrays),immunohistochemistry(IHC)tissues,and single-cell RNA sequencing(scRNA-seq)data were integrated to comprehensively investigate ANAPC1 expression in HCC.Clustered regularly interspaced short palindromic repeats analysis was performed to assess growth in HCC cell lines following ANAPC1 knockout.Enrichment analyses were conducted to explore the functions of ANAPC1.ScRNA-seq data was used to examine the cell cycle and metabolic levels.CellChat analysis was applied to investigate the interactions between ANAPC1 and different cell types.The relationship between ANAPC1 expression and drug concentration was analyzed.RESULTS ANAPC1 messenger RNA was found to be upregulated in bulk RNA,IHC tissues samples and malignant hepatocytes.The proliferation of JHH2 cell lines was most significantly inhibited after ANAPC1 knockdown.In biological pathways,the development of HCC was found to be linked to the regulation of ubiquitin-mediated proteolysis.Additionally,scRNA-seq results indicated that highly expressed ANAPC1 was in the G2/M phase,with increased glycolysis/gluconeogenesis activity.A CellChat analysis showed that ANAPC1 was associated with the regulation of the migration inhibitory factor-(cluster of differentiation 74+C-X-C chemokine receptor type 4)pathway.Higher ANAPC1 expression correlated with stronger effects of sorafenib,dasatinib,ibrutinib,lapatinib,nilotinib and afatinib.CONCLUSION The high expression level of ANAPC1 may regulate the cell cycle and metabolic levels of HCC through the ubiquitination-related pathway,thereby promoting disease progression.
基金Supported by the Promoting Project of Basic Capacity for Young and Middle-aged University Teachers in Guangxi,No.2025KY0164Youth Science Foundation of Guangxi Medical University,No.GXMUYSF202423Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project,No.Z-A20220415 and No.Z20210442.
文摘BACKGROUND The prevalence of colorectal cancer(CRC)in younger people is increasing.Despite advances in precision medicine,the challenges of drug resistance and high costs persist.Nitidine chloride(NC)has pharmacological potential,and kinesin family member 20A(KIF20A)is overexpressed in various tumors;however,their interaction in CRC remains unexplored.AIM To investigate the KIF20A expression characteristics in CRC cells and determine whether it is a potential target gene for NC in inhibiting CRC treatment.METHODS Single-cell RNA sequencing(scRNA-seq),spatial transcriptomics,and mRNA expression profiling were used to analyze KIF20A expression in CRC cells.Immunohistochemical staining was used to verify KIF20A expression in 416 clinical samples(208 CRC tissue samples and 208 noncancerous control tissue samples).Clustered regularly interspaced short palindromic repeats(CRISPR)technology was used to evaluate the impact of knocking out KIF20A on CRC cell growth.Molecular docking was applied to analyze NC–KIF20A binding.Finally,RNA sequencing and functional enrichment analysis were performed to explore the mechanism of action of NC in CRC cells.RESULTS Treating HCT116 cells with NC was found to significantly downregulate KIF20A(P<0.05),and the molecular docking analysis revealed high-affinity binding between NC and KIF20A(binding energy=-9.6 kcal/mol).The scRNA-seq,spatial transcriptomics,and mRNA expression profiling results confirmed the significantly high expression of KIF20A in CRC tissues(standardized mean difference=1.33,95%confidence interval:0.885-1.77,summary receiver operating characteristic curve area=0.94).The immunohistochemical analysis of the clinical samples showed high KIF20A expression in the CRC tissues(P<0.05),with significant correlation between the level of expression and gender,tumor size,and tumor grade(P<0.05).Knocking out KIF20A significantly inhibited the growth of various CRC cell lines(CRISPR score<-0.3).The functional enrichment analysis indicated that NC may inhibit CRC by disrupting several biological processes,such as mitotic nuclear division,chromosome segregation,and microtubule binding.CONCLUSION Our results indicate that NC binds to KIF20A with high affinity and downregulates its expression in CRC cells,leading to reduced proliferation.Hence,NC has promise as a therapeutic agent in the treatment of CRC,and targeting KIF20A also has potential as a therapeutic strategy.Further KIF20A knockout studies are needed to confirm the binding specificity and mechanistic roles of NC in CRC.
基金Project supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No.60921062)the National Natural Science Foundation of China (Grant No.60873014)the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos.61003082 and 60903059)
文摘The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units （GPU）. This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.
基金Supported by National Natural Science Foundation of China,No.82260581Guangxi Zhuang Autonomous Region Health Committee Scientific Research Project,No.Z20201147+3 种基金Guangxi Medical University Education and Teaching Reform Project,No.2021XJGA02Undergraduate Teaching Reform Project of Guangxi Higher Education,No.2023JGB163Guangxi Medical University Teacher Teaching Ability Development Project,No.2202JFA20China Undergraduate Innovation and Entrepreneurship Training Program,No.S202310598170.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a major health challenge with high incidence and poor survival rates in China.Systemic therapies,particularly tyrosine kinase inhibitors(TKIs),are the first-line treatment for advanced HCC,but resistance is common.The Rho GTPase family member Rho GTPase activating protein 12(ARHGAP12),which regulates cell adhesion and invasion,is a potential therapeutic target for overcoming TKI resistance in HCC.However,no studies on the expression of ARHGAP12 in HCC and its role in resistance to TKIs have been reported.AIM To unveil the expression of ARHGAP12 in HCC,its role in TKI resistance and its potential associated pathways.METHODS This study used single-cell RNA sequencing(scRNA-seq)to evaluate ARHGAP12 mRNA levels and explored its mechanisms through enrichment analysis.CellChat was used to investigate focal adhesion(FA)pathway regulation.We integrated bulk RNA data(RNA-seq and microarray),immunohistochemistry and proteomics to analyze ARHGAP12 mRNA and protein levels,correlating with clinical outcomes.We assessed ARHGAP12 expression in TKI-resistant HCC,integrated conventional HCC to explore its mechanism,identified intersecting FA pathway genes with scRNA-seq data and evaluated its response to TKI and immunotherapy.RESULTS ARHGAP12 mRNA was found to be highly expressed in malignant hepatocytes and to regulate FA.In malignant hepatocytes in high-score FA groups,MDK-[integrin alpha 6(ITGA6)+integrinβ-1(ITGB1)]showed specificity in ligand-receptor interactions.ARHGAP12 mRNA and protein were upregulated in bulk RNA,immunohistochemistry and proteomics,and higher expression was associated with a worse prognosis.ARHGAP12 was also found to be a TKI resistance gene that regulated the FA pathway.ITGB1 was identified as a crossover gene in the FA pathway in both scRNA-seq and bulk RNA.High expression of ARHGAP12 was associated with adverse reactions to sorafenib,cabozantinib and regorafenib,but not to immunotherapy.CONCLUSION ARHGAP12 expression is elevated in HCC and TKI-resistant HCC,and its regulatory role in FA may underlie the TKI-resistant phenotype.
基金the National Key R&D Program of China(2017 YFB 0203000)National Natural Science Foundation of China(61632003,61661146002,61631001).
文摘Background Eye tracking te chnology is receiving increased attention in the field of virtual reality.Specifically,future gaze prediction is crucial in pre-computation for many applications such as gaze-contingent rendering,advertisement placement,and content-based design.To explore future gaze prediction,it is necessary to analyze the temporal continuity of visual attention in immersive virtual reality.Methods In this paper,the concept of temporal continuity of visual attention is presented.Subsequently,an autocorrelation function method is proposed to evaluate the temporal continuity.Thereafter,the temporal continuity is analyzed in both free-viewing and task-oriented conditions.Results Specifically,in free-viewing conditions,the analysis of a free-viewing gaze dataset indicates that the temporal continuity performs well only within a short time interval.A task-oriented game scene condition was created and conducted to collect users'gaze data.An analysis of the collected gaze data finds the temporal continuity has a similar performance with that of the free-viewing conditions.Temporal continuity can be applied to future gaze prediction and if it is good,users'current gaze positions can be directly utilized to predict their gaze positions in the future.Conclusions The current gaze's future prediction performances are further evaluated in both free-viewing and task-oriented conditions and discover that the current gaze can be efficiently applied to the task of short-term future gaze prediction.The task of long-term gaze prediction still remains to be explored.
文摘A Verilog-VHDL translating method directed by simulation semantics is presented. Based on the analysis and comparison, three steps are taken to implement the translation. Through semantic analyzing and syntax tree reconstructing before translation, the main part of Verilog is supported. According to the level in the design hierarchy, the modules are translated in down-top order, and that results in a correct VHDL declaration-reference order. The translation rules of assignment statements and delay/timing constructs are also explained in detail. This method has been successfully implemented in the translator developed by the authors. The correctness has been validated by many examples.
文摘The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to (-M,+N).The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of Mand Naffect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90.31%, and 89.62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.
基金Funded by the Planning Project of National Language Committee in the "12th 5-year Plan"(No.YB125-49)the Foundation for Key Program of Ministry of Education,China(No.212167)the Fundamental Research Funds for the Central Universities(No.SWJTU12CX096)
文摘How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paperpresents a new event detection method for the current new event detection system, based on sorted subtopic matching algorithm and constructs the entire design framework. In this p^per, the subtopics contained in old topics （or news stories） are sorted in descending order according to their importance to the topic（or news stories）, and form a sorted subtopic sequence. In the process of subtopic matching, subtopic scoring matrix is used to determine whether a new story is reporting a new event. Experimental results show that the sorted subtopic matching model improved the accuracy and effectiveness ofthenew event detection system in cyberspace.
文摘In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(Grant Nos.61003082 and 60903059)the National Natural Science Foundation of China(Grant No.60873014)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.60921062)
文摘Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.
基金the National Natural Science Foundation of China(Grant Nos.60921062,61003082,and 61272146)
文摘Unipolar memristive devices are an important kind of resistive switching devices. However, few circuit models of them have been proposed. In this paper, we propose the SPICE modeling of flux-controlled unipolar memristive devices based on the memristance versus state map. Using our model, the flux thresholds, ON and OFF resistance, and compliance current can easily be set as model parameters. We simulate the model in HSPICE using model parameters abstracted from real devices, and the simulation results show that the proposed model caters to the real device data very well, thus demonstrating that the model is correct. Using the same modeling methodology, the SPICE model of charge-controlled unipolar memristive devices could also be developed. The proposed model could be used to model resistive memory cells, logical gates as well as synapses in artificial neural networks.
