Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classificatio...Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.展开更多
In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary cha...In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.展开更多
Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal in...Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.展开更多
Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.I...Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).展开更多
[Objectives]The present study was conducted to investigate the change rule ofβ-fructofuranosidase gene expression and its enzyme activity in the midgut of 5 th instar silkworm(Bombyx mori),in order to provide a refer...[Objectives]The present study was conducted to investigate the change rule ofβ-fructofuranosidase gene expression and its enzyme activity in the midgut of 5 th instar silkworm(Bombyx mori),in order to provide a reference for illustrating the enzymatic mechanism of usingβ-fructofuranosidase to absorb sucrose nutrition from mulberry leaves.[Methods]Real-time fluorescent quantitative PCR was applied to analyze the expression of BmSuc1 and BmSuc2 in midgut of 5 th-instar silkworm larvae,meanwhile the activities ofβ-fructofuranosidase was determined.[Results]BmSuc1 was expressed in the midgut of 5 th-instar silkworm larvae at different developmental stages.Its expression was upregulated at the beginning of the 5 th instar and during the peak feeding period,whereas BmSuc2 expression remained very low throughout the entire 5 th instar.The activity ofβ-fructofuranosidase was relatively high during the peak feeding period of 5 th-instar larvae,showing a trend of increasing first and then decreasing.[Conclusions]The expression pattern of the BmSuc1 gene and the changes inβ-fructofuranosidase activity were generally consistent with the physiological process of sugar nutrient absorption and utilization from mulberry leaves in 5 th-instar silkworms.It suggests that BmSuc1,as a sucrose hydrolase gene,plays a major role in the digestion and absorption of sucrose nutrients from mulberry leaves in the midgut tissue.展开更多
DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expres...DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.展开更多
DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we presen...DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine.展开更多
For the classification of the various species of fishes in the genus Spinibarbus,molecular identification and phylogenetic analysis were conducted for five species of fishes in Spinibarbus based on the mitochondrial C...For the classification of the various species of fishes in the genus Spinibarbus,molecular identification and phylogenetic analysis were conducted for five species of fishes in Spinibarbus based on the mitochondrial COI gene.The results showed that the interspecific genetic distances were greater than 0.02,whereas the intraspecific genetic distances were less than 0.02.The phylogenetic tree revealed that the S.caldwelli and the S.hollandi clustered into clade I.The S.yunnanensis and the S.denticulatus clustered in a branch,and then clustered with the S.sinensis to form clade II.Clades I and II together formed the genus Spinibarbus.The S.caldwelli and S.hollandi clustered in two separate subclades,and the genetic distances between them were greater than the genetic distances between each of them and other species within the same subclade.The results of this study indicated that the mitochondrial COI gene sequences were effective for species identification of fishes in the genus Spinibarbus and could be used to explore the phylogeny of this genus.展开更多
Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive...Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive thyroid-related cell lines cultured under simulated microgravity.Methods:Five thyroid-related cell lines—normal thyrocytes(Nthy-ori 3-1),papillary thyroid cancer(PTC)cells(SNU-790,TPC-1),poorly differentiated thyroid cancer cell(BCPAP),and anaplastic thyroid cancer cell(SNU-80)—were cultured under simulated microgravity(10-3 g)using a clinostat.Differentially expressed genes(DEGs)were analyzed using cDNA microarray,followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.Results:DEG analysis under simulated microgravity revealed distinct gene expression profiles by gravity condition,with 2980 DEGs in SNU-790,1033 in BCPAP,562 in TPC-1,477 in Nthy-ori 3-1,and 246 in SNU-80,as confirmed by hierarchical clustering.In PTC cell lines(SNU-790,TPC-1),G2–M phase–related genes were upregulated.In non-PTC cell lines(BCPAP,SNU-80),genes associated with innate immune response,Toll-like receptor signaling,were upregulated,whereas Hypoxia-Inducible Factor 1-alpha(HIF-1α)signaling-related genes were downregulated.Additionally,under simulated microgravity,significant migration was observed in SNU-790(3×104 cells)and BCPAP(2×104 and 3×104),while significant invasion occurred in SNU-790,Nthy-ori 3-1,and BCPAP at a seeding density of 2×104.Other conditions showed no significant differences.Conclusion:This study comprehensively evaluates the effects of simulated microgravity using a diverse panel of thyroid-related cell lines.Thesefindings provide valuable insight into how microgravity could influence cancer biology,emphasizing the importance of further research on cancer behavior in space environments and its implications for human health during long-term space missions.展开更多
Drugs and pesticide residues in broiler feed can compromise the therapeutic and production benefits of antibiotic(ANT)application and affect gene expression.In this study,we analyzed the expression of 13 key pancreati...Drugs and pesticide residues in broiler feed can compromise the therapeutic and production benefits of antibiotic(ANT)application and affect gene expression.In this study,we analyzed the expression of 13 key pancreatic genes and blood physiology parameters after administering one maximum residue limit of herbicide glyphosate(GLY),two ANTs,and one anticoccidial drug(AD).A total of 260 Ross 308 broilers aged 1-40 d were divided into the following four groups of 65 birds each:control group,which was fed the main diet(MD),and three experimental groups,which were fed MD supplemented with GLY,GLY+ANTs(enrofloxacin and colistin methanesulfonate),and GLY+AD(ammonium maduramicin),respectively.The results showed that the addition of GLY,GLY+ANTs,and GLY+AD caused significant changes in the expression of several genes of physiological and economic importance.In particular,genes related to inflammation and apoptosis(interleukin 6(IL6),prostaglandin-endoperoxide synthase 2(PTGS2),and caspase 6(CASP6))were downregulated by up to 99.1%,and those related to antioxidant protection(catalase(CAT),superoxide dismutase 1(SOD1)and peroxiredoxin 6(PRDX6))by up to 98.6%,compared to controls.There was also a significant decline in the values of immunological characteristics in the blood serum observed in the experimental groups,and certain changes in gene expression were concordant with changes in the functioning of the pancreas and blood.The changes revealed in gene expression and blood indices in response to GLY,ANTs,and AD provide insights into the possible mechanisms of action of these agents at the molecular level.Specifically,these changes may be indicative of physiological mechanisms to overcome the negative effects of GLY,GLY+ANTs,and GLY+AD in broilers.展开更多
The forkhead box(FOX)family represents a class of transcription factors characterized by a distinctive winged helical structure.Forkhead box A1(FOXA1),a member of the forkhead box A(FOXA)subfamily within the FOX gene ...The forkhead box(FOX)family represents a class of transcription factors characterized by a distinctive winged helical structure.Forkhead box A1(FOXA1),a member of the forkhead box A(FOXA)subfamily within the FOX gene family,was the first forkhead protein identified in mammals.It serves as a pivotal transcription factor in tissue-specific differentiation and functions.Upon activation,owing to its unique structural domains,FOXA1 can interact with nucleosomes to open chromatin,thereby facilitating the recruitment of other transcription factors.These factorsmay act independently or synergistically with recruited transcription factors to regulate gene expression.Consequently,FOXA1 and other FOXA subfamily members with similar functions are referred to as“pioneer factors.”In recent years,studies on FOXA1 have advanced our understanding of its crucial role in gene regulation and involvement in disease processes.However,owing to their tissue-specific effects and varying biological behaviors in different environmental contexts,the underlying mechanisms remain elusive.Weused the PubMed database to better understand the complexmechanisms of FOXA1.By using keywords such as“FOXA1”and“transcription factor,”an extensive literature was retrieved,and many of the most relevant publications were screened.The selected studies were then thoroughly synthesized and summarized.This review synthesizes recent findings on FOXA1,encompassing its structural characteristics,domain functions,roles in embryonic development and the maintenance of adult organ morphology and function,interactions with histone posttranslational modifications in gene regulation,and the influence of its posttranslational modifications on gene expression.We also explore the involvement of FOXA1 in various diseases.By elucidating the biological mechanisms and disease-related roles of FOXA1,this review aims to provide insights for future research on its complex mechanisms and potential therapeutic targets.展开更多
Short tandem repeats(STRs)modulate gene expression and contribute to trait variation.However,a systematic evaluation of the genomic characteristics of STRs has not been conducted,and their influence on gene expression...Short tandem repeats(STRs)modulate gene expression and contribute to trait variation.However,a systematic evaluation of the genomic characteristics of STRs has not been conducted,and their influence on gene expression in rice remains unclear.Here,we construct a map of 137,629 polymorphic STRs in the rice(Oryza sativa L.)genome using a population-scale resequencing dataset.A genome-wide survey encompassing 4726 accessions shows that the occurrence frequency,mutational patterns,chromosomal distribution,and functional properties of STRs are correlated with the sequences and lengths of repeat motifs.Leveraging a transcriptome dataset from 127 rice accessions,we identify 44,672 expression STRs(eSTRs)by modeling gene expression in response to the length variation of STRs.These eSTRs are notably enriched in the regulatory regions of genes with active transcriptional signatures.Population analysis identifies numerous STRs that have undergone genetic divergence among different rice groups and 1726 tagged STRs that may be associated with agronomic traits.By editing the(ACT)_(7) STR in OsFD1 promoter,we further experimentally validate its role in regulating gene expression and phenotype.Our study highlights the contribution of STRs to transcriptional regulation in plants and establishes the foundation for their potential use as alternative targets for genetic improvement.展开更多
Objectives:Despite the considerable regenerative capacity exhibited by adipose-derived mesenchymal stem cells(ASCs),their genetic and molecular mechanisms remain incompletely understood.Methods:In this study,we analyz...Objectives:Despite the considerable regenerative capacity exhibited by adipose-derived mesenchymal stem cells(ASCs),their genetic and molecular mechanisms remain incompletely understood.Methods:In this study,we analyzed the global gene expression profile of adipose-derived mesenchymal stem cells(ASCs)using microarray analysis and compared it with stromal vascular fraction(SVF)cells.Results:Microarray analysis revealed that ASCs express elevated levels of genes related to the extracellular matrix(ECM;extracellular matrix)and collagen,which are critical components of tissue remodeling and wound healing.Additionally,genes associated with cell growth,differentiation,motility,and plasticity were highly expressed.When compared to stromal vascular fraction(SVF)cells,ASCs demonstrated enrichment of genes involved in anti-inflammatory responses,immune modulation,tissue repair,cell adhesion,and migration processes.Gene Set Enrichment Analysis(GSEA;Gene Set Enrichment Analysis)showed activation of pathways related to angiogenesis,such as vascular endothelial growth factor(VEGF),Integrin,Wnt signaling pathways,transforming growth factor-beta(TGF-β),extracellular matrix(ECM),and matrix metalloproteinase(MMP),highlighting the significant angiogenic potential of ASCs.Gene Ontology(GO;Gene Ontology)analysis further linked ASCs to biological processes associated with the regulation of cell proliferation and muscle cell differentiation.Conclusion:These findings collectively underscore the suitability of adipose-derived mesenchymal stem cells(ASCs)as a promising candidate for regenerative medicine,particularly in applications involving tissue repair,immune modulation,and promotion of angiogenesis.展开更多
Gallbladder cancer(GBC)is a lethal biliary tract malignancy,which is infrequent in most developed countries,but common in many developing countries in specific geographical regions of the world.Non-specific symptoms l...Gallbladder cancer(GBC)is a lethal biliary tract malignancy,which is infrequent in most developed countries,but common in many developing countries in specific geographical regions of the world.Non-specific symptoms leading to late diagnosis is one of the primary factors contributing to poor prognosis in GBC.An understanding of the complex relationship between molecular genetics and epidemiological variances in the incidence rates of GBC is thus of utmost importance.Present review summarizes recent updates on population-specific dysregulated genetic expressions in the genesis of GBC,highlighting the pattern of ethno-geographic variations and on advances in targeted therapies conducted till date;points out the lacunae that deserve further attention and suggest possible new directions for future clinical trials in GBC.The review calls for the need of genetic screening of each GBC patients and for more extensive clinical trials on targeted therapies to move towards the goal of personalized medicine,bringing about more favourable survival outcomes.展开更多
Correction to:J.Iron Steel Res.Int.https://doi.org/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
Background Mobile element variants(MEVs)have a significant and complex impact on genomic diversity and phe-notypic traits.However,the quantity,distribution,and relationship with gene expression and complex traits of M...Background Mobile element variants(MEVs)have a significant and complex impact on genomic diversity and phe-notypic traits.However,the quantity,distribution,and relationship with gene expression and complex traits of MEVs in the pig genome remain poorly understood.Results We constructed the most comprehensive porcine MEV library based on high-depth whole genome sequencing(WGS)data from 747 pigs across 59 breeds worldwide.This database identified a total of 147,993 poly-morphic MEVs,including 121,099 short interspersed nuclear elements(SINEs),26,053 long interspersed nuclear elements(LINEs),802 long terminal repeats(LTRs),and 39 other transposons,among which 54%are newly discovered.We found that MEVs are unevenly distributed across the genome and are strongly influenced by negative selec-tion effects.Importantly,we identified 514,530,and 584 candidate MEVs associated with population differentiation,domestication,and breed formation,respectively.For example,a significantly differentiated MEV is located in the ATRX intron between Asian and European pigs,whereas ATRX is also differentially expressed between Asian and European pigs in muscle tissue.In addition,we identified 4,169 expressed MEVs(eMEVs)significantly associated with gene expression and 6,914 splicing MEVs(sMEVs)associated with gene splicing based on RNA-seq data from 266 porcine liver tissues.These eMEVs and sMEVs explain 6.24%and 9.47%,respectively,of the observed cis-heritability and high-light the important role of MEVs in the regulation of gene expression.Finally,we provide a high-quality SNP–MEV reference haplotype panel to impute MEV genotypes from genome-wide SNPs.Notably,we identified a candidate MEV significantly associated with total teat number,demonstrating the functionality of this reference panel.Conclusions The present investigation demonstrated the importance of MEVs in pigs in terms of population diversity,gene expression and phenotypic traits,which may provide useful resources and theoretical support for pig genetics and breeding.展开更多
N^(6)-Methyladenosine(m^(6)A)is the most common modification in the transcriptome of biological RNA and plays roles that include maintaining the stability and transportation of mRNA,mRNA precursor shearing,polyadenyla...N^(6)-Methyladenosine(m^(6)A)is the most common modification in the transcriptome of biological RNA and plays roles that include maintaining the stability and transportation of mRNA,mRNA precursor shearing,polyadenylation,and the initiation of translation.With the improving understanding of RNA methylation,m^(6)A modification is known to play vital roles in plant development and growth.The multi-petalization of flowering plants has high ornamental and research value in horticultural landscapes.However,the mechanism of RNA methylation in flower formation in Magnolia wufengensis,a classical multi-petalizational plant,remains unclear.This study compared and analyzed RNA m^(6)A methylation and the transcriptome in floral buds of two varieties with large differences in tepal number at the early stage of development.It was found that the degree of RNA m^(6)A methylation and relative expression levels of MawuAGL6-2,MawuPI-4,and MawuAGL9 in‘Jiaodan’with 36 tepals were significantly higher than those in‘Jiaohong’with 9 tepals during the development of floral organ primordia.Combined with quantitative real-time PCR,the expression levels of MawuAGL6-2,MawuPI-4,and MawuAGL9were positively correlated with the number of tepals.Transgenic experiments showed that MawuAGL6-1/2,and MawuPI-4 can increase the number of petals in Arabidopsis.Moreover,MawuAGL6-2 and MawuPI-4 can restore the missing petal phenotype of mutant Arabidopsis.Yeast two hybrid and yeast three hybrid indicated that MawuAGL6-2,MawuAP3-1/2,and MawuPI-4 could interact with each other under the mediation of the class E protein MawuAGL9.Based on these results,it is hypothesized that m^(6)A methylation influences the multi-petalization of Magnolia wufengensis by affecting the expression levels of MawuAGL6-2,MawuAP3-1/2,MawuPI-4,and MawuAGL9.These findings provide a better understanding of the molecular mechanisms of epigenetic modifications in flower developmental diversity.展开更多
Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its im...Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.展开更多
In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on t...In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on the industrial hot-rolled data and the algorithm of gene expression programming(GEP).Firstly,the industrial data of hot-rolled strip steel are preprocessed using the Pauta criterion,so as to eliminate outliers.The key input variables that affect Ys and Ts are selected by using the method of the maximal information coefficient(MIC).Secondly,the explicit prediction models of Ys and Ts are established using GEP.Subsequently,the model results based on GEP are compared with those based on the support vector regression(SVR)and the back propagation neural network(BPNN).Finally,the mathematical expression models for Ys and Ts obtained by GEP are used to further analyse the specific relationships between the chemical composition and mechanical property.It is shown that the errors of Ys and Ts based on GEP are less than 4%,and the coefficient of determination(R^(2))of Ys and Ts based on GEP is above 0.9,which has strong prediction performance.The prediction accuracy of GEP can achieve the same level with SVR and BPNN.It is worth mentioning that the proposed model can not only show the explicit relationship between the chemical composition,production process,and mechanical property of strip steel,but also occupy high prediction accuracy,which can make reliable reference for strip steel product design and optimisation.展开更多
Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing...Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.展开更多
基金supported by a grant from the National High-tech R&D Program (863 Program, No. 2006AA02Z331) to Liangbiao Chen
文摘Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/42/43)This work was supported by Taif University Researchers Supporting Program(project number:TURSP-2020/200),Taif University,Saudi Arabia.
文摘In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.
文摘Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the National Research Foundation of Korea(NRF)grant funded by theKorea government(MSIT)(No.RS-2023-00218176)the Soonchunhyang University Research Fund.
文摘Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).
基金Supported by General Project of Yunnan Provincial Agricultural Basic Research Joint Special Project(202301BD070001-229)Yunnan Provincial Key R&D Program(202403AK140075)+1 种基金Modern Sericulture Industry Technology System of Yunan Province(KJTX-07)Honghe Comprehensive Test Station of National Sericulture Industry Technology System(CARS-18).
文摘[Objectives]The present study was conducted to investigate the change rule ofβ-fructofuranosidase gene expression and its enzyme activity in the midgut of 5 th instar silkworm(Bombyx mori),in order to provide a reference for illustrating the enzymatic mechanism of usingβ-fructofuranosidase to absorb sucrose nutrition from mulberry leaves.[Methods]Real-time fluorescent quantitative PCR was applied to analyze the expression of BmSuc1 and BmSuc2 in midgut of 5 th-instar silkworm larvae,meanwhile the activities ofβ-fructofuranosidase was determined.[Results]BmSuc1 was expressed in the midgut of 5 th-instar silkworm larvae at different developmental stages.Its expression was upregulated at the beginning of the 5 th instar and during the peak feeding period,whereas BmSuc2 expression remained very low throughout the entire 5 th instar.The activity ofβ-fructofuranosidase was relatively high during the peak feeding period of 5 th-instar larvae,showing a trend of increasing first and then decreasing.[Conclusions]The expression pattern of the BmSuc1 gene and the changes inβ-fructofuranosidase activity were generally consistent with the physiological process of sugar nutrient absorption and utilization from mulberry leaves in 5 th-instar silkworms.It suggests that BmSuc1,as a sucrose hydrolase gene,plays a major role in the digestion and absorption of sucrose nutrients from mulberry leaves in the midgut tissue.
文摘DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
基金supported by the National Natural Science Foundation of China(Grant Number 62341210)Natural Science Foundation of Guangxi Province(Grant Number:2025GXNSFHA069267)Science and Technology Development Plan for Baise City(Grant Number 20233654).
文摘DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine.
文摘For the classification of the various species of fishes in the genus Spinibarbus,molecular identification and phylogenetic analysis were conducted for five species of fishes in Spinibarbus based on the mitochondrial COI gene.The results showed that the interspecific genetic distances were greater than 0.02,whereas the intraspecific genetic distances were less than 0.02.The phylogenetic tree revealed that the S.caldwelli and the S.hollandi clustered into clade I.The S.yunnanensis and the S.denticulatus clustered in a branch,and then clustered with the S.sinensis to form clade II.Clades I and II together formed the genus Spinibarbus.The S.caldwelli and S.hollandi clustered in two separate subclades,and the genetic distances between them were greater than the genetic distances between each of them and other species within the same subclade.The results of this study indicated that the mitochondrial COI gene sequences were effective for species identification of fishes in the genus Spinibarbus and could be used to explore the phylogeny of this genus.
文摘Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive thyroid-related cell lines cultured under simulated microgravity.Methods:Five thyroid-related cell lines—normal thyrocytes(Nthy-ori 3-1),papillary thyroid cancer(PTC)cells(SNU-790,TPC-1),poorly differentiated thyroid cancer cell(BCPAP),and anaplastic thyroid cancer cell(SNU-80)—were cultured under simulated microgravity(10-3 g)using a clinostat.Differentially expressed genes(DEGs)were analyzed using cDNA microarray,followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.Results:DEG analysis under simulated microgravity revealed distinct gene expression profiles by gravity condition,with 2980 DEGs in SNU-790,1033 in BCPAP,562 in TPC-1,477 in Nthy-ori 3-1,and 246 in SNU-80,as confirmed by hierarchical clustering.In PTC cell lines(SNU-790,TPC-1),G2–M phase–related genes were upregulated.In non-PTC cell lines(BCPAP,SNU-80),genes associated with innate immune response,Toll-like receptor signaling,were upregulated,whereas Hypoxia-Inducible Factor 1-alpha(HIF-1α)signaling-related genes were downregulated.Additionally,under simulated microgravity,significant migration was observed in SNU-790(3×104 cells)and BCPAP(2×104 and 3×104),while significant invasion occurred in SNU-790,Nthy-ori 3-1,and BCPAP at a seeding density of 2×104.Other conditions showed no significant differences.Conclusion:This study comprehensively evaluates the effects of simulated microgravity using a diverse panel of thyroid-related cell lines.Thesefindings provide valuable insight into how microgravity could influence cancer biology,emphasizing the importance of further research on cancer behavior in space environments and its implications for human health during long-term space missions.
基金supported by the Russian Science Foundation(No.22-16-00128),“Investigation of the Toxic Effect of Glyphosates on the Functional State of the Bird Intestinal Microbial Community,Their Growth and Development,and the Development of a Biological Product Based on the Glyphosate Degrading Strain”.
文摘Drugs and pesticide residues in broiler feed can compromise the therapeutic and production benefits of antibiotic(ANT)application and affect gene expression.In this study,we analyzed the expression of 13 key pancreatic genes and blood physiology parameters after administering one maximum residue limit of herbicide glyphosate(GLY),two ANTs,and one anticoccidial drug(AD).A total of 260 Ross 308 broilers aged 1-40 d were divided into the following four groups of 65 birds each:control group,which was fed the main diet(MD),and three experimental groups,which were fed MD supplemented with GLY,GLY+ANTs(enrofloxacin and colistin methanesulfonate),and GLY+AD(ammonium maduramicin),respectively.The results showed that the addition of GLY,GLY+ANTs,and GLY+AD caused significant changes in the expression of several genes of physiological and economic importance.In particular,genes related to inflammation and apoptosis(interleukin 6(IL6),prostaglandin-endoperoxide synthase 2(PTGS2),and caspase 6(CASP6))were downregulated by up to 99.1%,and those related to antioxidant protection(catalase(CAT),superoxide dismutase 1(SOD1)and peroxiredoxin 6(PRDX6))by up to 98.6%,compared to controls.There was also a significant decline in the values of immunological characteristics in the blood serum observed in the experimental groups,and certain changes in gene expression were concordant with changes in the functioning of the pancreas and blood.The changes revealed in gene expression and blood indices in response to GLY,ANTs,and AD provide insights into the possible mechanisms of action of these agents at the molecular level.Specifically,these changes may be indicative of physiological mechanisms to overcome the negative effects of GLY,GLY+ANTs,and GLY+AD in broilers.
基金supported by grants from the National Natural Science Foundation of China (No.82470042)Liaoning Provincial Joint Science and Technology Plan (No.2023JH2/101800021)+1 种基金Basic Scientific Research Project of Liaoning Provincial Department of Education (No.LJKMZ20221186)Shenyang Municipal Public Health Research and Development Special Project (No.LJKMZ20221186)
文摘The forkhead box(FOX)family represents a class of transcription factors characterized by a distinctive winged helical structure.Forkhead box A1(FOXA1),a member of the forkhead box A(FOXA)subfamily within the FOX gene family,was the first forkhead protein identified in mammals.It serves as a pivotal transcription factor in tissue-specific differentiation and functions.Upon activation,owing to its unique structural domains,FOXA1 can interact with nucleosomes to open chromatin,thereby facilitating the recruitment of other transcription factors.These factorsmay act independently or synergistically with recruited transcription factors to regulate gene expression.Consequently,FOXA1 and other FOXA subfamily members with similar functions are referred to as“pioneer factors.”In recent years,studies on FOXA1 have advanced our understanding of its crucial role in gene regulation and involvement in disease processes.However,owing to their tissue-specific effects and varying biological behaviors in different environmental contexts,the underlying mechanisms remain elusive.Weused the PubMed database to better understand the complexmechanisms of FOXA1.By using keywords such as“FOXA1”and“transcription factor,”an extensive literature was retrieved,and many of the most relevant publications were screened.The selected studies were then thoroughly synthesized and summarized.This review synthesizes recent findings on FOXA1,encompassing its structural characteristics,domain functions,roles in embryonic development and the maintenance of adult organ morphology and function,interactions with histone posttranslational modifications in gene regulation,and the influence of its posttranslational modifications on gene expression.We also explore the involvement of FOXA1 in various diseases.By elucidating the biological mechanisms and disease-related roles of FOXA1,this review aims to provide insights for future research on its complex mechanisms and potential therapeutic targets.
基金supported by the National Natural Science Foundation of China(32172010)the Major Program of Guangdong Basic and Applied Basic Research(2019B030302006).
文摘Short tandem repeats(STRs)modulate gene expression and contribute to trait variation.However,a systematic evaluation of the genomic characteristics of STRs has not been conducted,and their influence on gene expression in rice remains unclear.Here,we construct a map of 137,629 polymorphic STRs in the rice(Oryza sativa L.)genome using a population-scale resequencing dataset.A genome-wide survey encompassing 4726 accessions shows that the occurrence frequency,mutational patterns,chromosomal distribution,and functional properties of STRs are correlated with the sequences and lengths of repeat motifs.Leveraging a transcriptome dataset from 127 rice accessions,we identify 44,672 expression STRs(eSTRs)by modeling gene expression in response to the length variation of STRs.These eSTRs are notably enriched in the regulatory regions of genes with active transcriptional signatures.Population analysis identifies numerous STRs that have undergone genetic divergence among different rice groups and 1726 tagged STRs that may be associated with agronomic traits.By editing the(ACT)_(7) STR in OsFD1 promoter,we further experimentally validate its role in regulating gene expression and phenotype.Our study highlights the contribution of STRs to transcriptional regulation in plants and establishes the foundation for their potential use as alternative targets for genetic improvement.
基金supported through National Research Foundation(NRF)of Korea grants funded by the Korean Government(No.NRF-2022R1F1A1064405)the research fund of Catholic Kwandong University and Catholic Kwandong University International St.Mary’s Hospital for S.-W Kim.
文摘Objectives:Despite the considerable regenerative capacity exhibited by adipose-derived mesenchymal stem cells(ASCs),their genetic and molecular mechanisms remain incompletely understood.Methods:In this study,we analyzed the global gene expression profile of adipose-derived mesenchymal stem cells(ASCs)using microarray analysis and compared it with stromal vascular fraction(SVF)cells.Results:Microarray analysis revealed that ASCs express elevated levels of genes related to the extracellular matrix(ECM;extracellular matrix)and collagen,which are critical components of tissue remodeling and wound healing.Additionally,genes associated with cell growth,differentiation,motility,and plasticity were highly expressed.When compared to stromal vascular fraction(SVF)cells,ASCs demonstrated enrichment of genes involved in anti-inflammatory responses,immune modulation,tissue repair,cell adhesion,and migration processes.Gene Set Enrichment Analysis(GSEA;Gene Set Enrichment Analysis)showed activation of pathways related to angiogenesis,such as vascular endothelial growth factor(VEGF),Integrin,Wnt signaling pathways,transforming growth factor-beta(TGF-β),extracellular matrix(ECM),and matrix metalloproteinase(MMP),highlighting the significant angiogenic potential of ASCs.Gene Ontology(GO;Gene Ontology)analysis further linked ASCs to biological processes associated with the regulation of cell proliferation and muscle cell differentiation.Conclusion:These findings collectively underscore the suitability of adipose-derived mesenchymal stem cells(ASCs)as a promising candidate for regenerative medicine,particularly in applications involving tissue repair,immune modulation,and promotion of angiogenesis.
文摘Gallbladder cancer(GBC)is a lethal biliary tract malignancy,which is infrequent in most developed countries,but common in many developing countries in specific geographical regions of the world.Non-specific symptoms leading to late diagnosis is one of the primary factors contributing to poor prognosis in GBC.An understanding of the complex relationship between molecular genetics and epidemiological variances in the incidence rates of GBC is thus of utmost importance.Present review summarizes recent updates on population-specific dysregulated genetic expressions in the genesis of GBC,highlighting the pattern of ethno-geographic variations and on advances in targeted therapies conducted till date;points out the lacunae that deserve further attention and suggest possible new directions for future clinical trials in GBC.The review calls for the need of genetic screening of each GBC patients and for more extensive clinical trials on targeted therapies to move towards the goal of personalized medicine,bringing about more favourable survival outcomes.
文摘Correction to:J.Iron Steel Res.Int.https://doi.org/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
基金National Key Research and Development Program of China(2022YFF1000103)Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240620.
文摘Background Mobile element variants(MEVs)have a significant and complex impact on genomic diversity and phe-notypic traits.However,the quantity,distribution,and relationship with gene expression and complex traits of MEVs in the pig genome remain poorly understood.Results We constructed the most comprehensive porcine MEV library based on high-depth whole genome sequencing(WGS)data from 747 pigs across 59 breeds worldwide.This database identified a total of 147,993 poly-morphic MEVs,including 121,099 short interspersed nuclear elements(SINEs),26,053 long interspersed nuclear elements(LINEs),802 long terminal repeats(LTRs),and 39 other transposons,among which 54%are newly discovered.We found that MEVs are unevenly distributed across the genome and are strongly influenced by negative selec-tion effects.Importantly,we identified 514,530,and 584 candidate MEVs associated with population differentiation,domestication,and breed formation,respectively.For example,a significantly differentiated MEV is located in the ATRX intron between Asian and European pigs,whereas ATRX is also differentially expressed between Asian and European pigs in muscle tissue.In addition,we identified 4,169 expressed MEVs(eMEVs)significantly associated with gene expression and 6,914 splicing MEVs(sMEVs)associated with gene splicing based on RNA-seq data from 266 porcine liver tissues.These eMEVs and sMEVs explain 6.24%and 9.47%,respectively,of the observed cis-heritability and high-light the important role of MEVs in the regulation of gene expression.Finally,we provide a high-quality SNP–MEV reference haplotype panel to impute MEV genotypes from genome-wide SNPs.Notably,we identified a candidate MEV significantly associated with total teat number,demonstrating the functionality of this reference panel.Conclusions The present investigation demonstrated the importance of MEVs in pigs in terms of population diversity,gene expression and phenotypic traits,which may provide useful resources and theoretical support for pig genetics and breeding.
基金supported by the National Natural Science Foundation of China(Grant No.31570651)。
文摘N^(6)-Methyladenosine(m^(6)A)is the most common modification in the transcriptome of biological RNA and plays roles that include maintaining the stability and transportation of mRNA,mRNA precursor shearing,polyadenylation,and the initiation of translation.With the improving understanding of RNA methylation,m^(6)A modification is known to play vital roles in plant development and growth.The multi-petalization of flowering plants has high ornamental and research value in horticultural landscapes.However,the mechanism of RNA methylation in flower formation in Magnolia wufengensis,a classical multi-petalizational plant,remains unclear.This study compared and analyzed RNA m^(6)A methylation and the transcriptome in floral buds of two varieties with large differences in tepal number at the early stage of development.It was found that the degree of RNA m^(6)A methylation and relative expression levels of MawuAGL6-2,MawuPI-4,and MawuAGL9 in‘Jiaodan’with 36 tepals were significantly higher than those in‘Jiaohong’with 9 tepals during the development of floral organ primordia.Combined with quantitative real-time PCR,the expression levels of MawuAGL6-2,MawuPI-4,and MawuAGL9were positively correlated with the number of tepals.Transgenic experiments showed that MawuAGL6-1/2,and MawuPI-4 can increase the number of petals in Arabidopsis.Moreover,MawuAGL6-2 and MawuPI-4 can restore the missing petal phenotype of mutant Arabidopsis.Yeast two hybrid and yeast three hybrid indicated that MawuAGL6-2,MawuAP3-1/2,and MawuPI-4 could interact with each other under the mediation of the class E protein MawuAGL9.Based on these results,it is hypothesized that m^(6)A methylation influences the multi-petalization of Magnolia wufengensis by affecting the expression levels of MawuAGL6-2,MawuAP3-1/2,MawuPI-4,and MawuAGL9.These findings provide a better understanding of the molecular mechanisms of epigenetic modifications in flower developmental diversity.
基金partially supported by the National Natural Science Foundation of China(42177164,52474121)the Outstanding Youth Project of Hunan Provincial Department of Education(23B0008).
文摘Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.52074187 and 52274388)Liaoning Province Artificial Intelligence Innovation and Development Plan Project(Major Science and Technology Project)(2023JH26-10100002)the National Key Research and Development Program of China(No.2022YFB3304800).
文摘In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on the industrial hot-rolled data and the algorithm of gene expression programming(GEP).Firstly,the industrial data of hot-rolled strip steel are preprocessed using the Pauta criterion,so as to eliminate outliers.The key input variables that affect Ys and Ts are selected by using the method of the maximal information coefficient(MIC).Secondly,the explicit prediction models of Ys and Ts are established using GEP.Subsequently,the model results based on GEP are compared with those based on the support vector regression(SVR)and the back propagation neural network(BPNN).Finally,the mathematical expression models for Ys and Ts obtained by GEP are used to further analyse the specific relationships between the chemical composition and mechanical property.It is shown that the errors of Ys and Ts based on GEP are less than 4%,and the coefficient of determination(R^(2))of Ys and Ts based on GEP is above 0.9,which has strong prediction performance.The prediction accuracy of GEP can achieve the same level with SVR and BPNN.It is worth mentioning that the proposed model can not only show the explicit relationship between the chemical composition,production process,and mechanical property of strip steel,but also occupy high prediction accuracy,which can make reliable reference for strip steel product design and optimisation.
文摘Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.
