Causal association between 731 immunocyte phenotypes and liver cirrhosis: A bidirectional two-sample mendelian randomization analysis

BACKGROUND Liver cirrhosis is a progressive hepatic disease whose immunological basis has attracted increasing attention.However,it remains unclear whether a concrete causal association exists between immunocyte phenotypes and liver cirrhosis.AIM To explore the concrete causal relationships between immunocyte phenotypes and liver cirrhosis through a mendelian randomization(MR)study.METHODS Data on 731 immunocyte phenotypes were obtained from genome-wide assoc-iation studies.Liver cirrhosis data were derived from the Finn Gen dataset,which included 214403 individuals of European ancestry.We used inverse variable weighting as the primary analysis method to assess the causal relationship.Sensitivity analyses were conducted to evaluate heterogeneity and horizontal pleiotropy.RESULTS The MR analysis demonstrated that 11 immune cell phenotypes have a positive association with liver cirrhosis[P<0.05,odds ratio(OR)>1]and that 9 immu-nocyte phenotypes were negatively correlated with liver cirrhosis(P<0.05,OR<1).Liver cirrhosis was positively linked to 9 immune cell phenotypes(P<0.05,OR>1)and negatively linked to 10 immune cell phenotypes(P<0.05;OR<1).None of these associations showed heterogeneity or horizontally pleiotropy(P>0.05).CONCLUSION This bidirectional two-sample MR study demonstrated a concrete causal association between immunocyte phenotypes and liver cirrhosis.These findings offer new directions for the treatment of liver cirrhosis.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Population genomic analysis reveals key genetic variations and the driving force for embryonic callus induction capability in maize

Genetic transformation has been an effective technology for improving the agronomic traits of maize.However,it is highly reliant on the use of embryonic callus(EC)and shows a serious genotype dependence.In this study,we performed genomic sequencing for 80 core maize germplasms and constructed a high-density genomic variation map using our newly developed pipeline(MQ2Gpipe).Based on the induction rate of EC(REC),these inbred lines were categorized into three subpopulations.The low-REC germplasms displayed more abundant genetic diversity than the high-REC germplasms.By integrating a genome-wide selective signature screen and region-based association analysis,we revealed 95.23 Mb of selective regions and 43 REC-associated variants.These variants had phenotypic variance explained values ranging between 21.46 and 49.46%.In total,103 candidate genes were identified within the linkage disequilibrium regions of these REC-associated loci.These genes mainly participate in regulation of the cell cycle,regulation of cytokinesis,and other functions,among which MYB15 and EMB2745 were located within the previously reported QTL for EC induction.Numerous leaf area-associated variants with large effects were closely linked to several REC-related loci,implying a potential synergistic selection of REC and leaf size during modern maize breeding.

Genetic analysis and candidate gene identification of salt tolerancerelated traits in maize

Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maize is of great importance.In the current study,a teosinte-maize BC2F7 population was used to investigate the genetic basis of 21 salt tolerance-related traits.In total,125 QTLs were detected using a high-density genetic bin map,with one to five QTLs explaining 6.05–32.02%of the phenotypic variation for each trait.The total phenotypic variation explained(PVE)by all detected QTLs ranged from 6.84 to 63.88%for each trait.Of all 125 QTLs,only three were major QTLs distributed in two genomic regions on chromosome 6,which were involved in three salt tolerance-related traits.In addition,10 pairs of epistatic QTLs with additive effects were detected for eight traits,explaining 0.9 to 4.44%of the phenotypic variation.Furthermore,18 QTL hotspots affecting 3–7 traits were identified.In one hotspot(L5),a gene cluster consisting of four genes(ZmNSA1,SAG6,ZmCLCg,and ZmHKT1;2)was found,suggesting the involvement of multiple pleiotropic genes.Finally,two important candidate genes,Zm00001d002090 and Zm00001d002391,were found to be associated with salt tolerance-related traits by a combination of linkage and marker-trait association analyses.Zm00001d002090 encodes a calcium-dependent lipid-binding(CaLB domain)family protein,which may function as a Ca^(2+)sensor for transmitting the salt stress signal downstream,while Zm00001d002391 encodes a ubiquitin-specific protease belonging to the C19-related subfamily.Our findings provide valuable insights into the genetic basis of salt tolerance-related traits in maize and a theoretical foundation for breeders to develop enhanced salt-tolerant maize varieties.

Integrated genomic and transcriptomic analysis reveals genes associated with plant height of foxtail millet

Foxtail millet(Setaria italica)is an important C4 model crop;however,due to its high-density planting and high stature,lodging at the filling stage resulted in a serious reduction in yield and quality.Therefore,it is imperative to identify and deploy the genes controlling foxtail millet plant height.In this study,we used a semi-dwarf line 263A and an elite high-stalk breeding variety,Chuang 29 to construct an F2 population to identify dwarf genes.We performed transcriptome analysis(RNA-seq)using internode tissues sampled at three jointing stages of 263A and Chuang 29,as well as bulk segregant analysis(BSA)on their F2 population.A total of 8918 differentially expressed genes(DEGs)were obtained from RNA-seq analysis,and GO analysis showed that DEGs were enriched in functions such as‘‘gibberellin metabolic process”and‘‘oxidoreductase activity”,which have previously been shown to be associated with plant height.A total 593 mutated genes were screened by BSA-seq method.One hundred and seventy-six out of the 593 mutated genes showed differential expression levels between the two parental lines,and seven genes not only showed differential expression in two or three internode tissues but also showed high genomic variation in coding regions,which indicated they play a crucial role in plant height determination.Among them,we found a gibberellin biosynthesis related GA20 oxidase gene(Seita.5G404900),which had a single-base at the third exon,leading to the frameshift mutation at 263A.Cleaved amplified polymorphic sequence assay and association analysis proved the single-base in Seita.5G404900 co-segregated with dwarf phenotype in two independent F2 populations planted in entirely different environments.Taken together,the candidate genes identified in this study will help to elucidate the genetic basis of foxtail millet plant height,and the molecular marker will be useful for marker-assisted dwarf breeding.

Bending and free vibrational analysis of bi-directional functionally graded beams with circular cross-section

The bending and free vibrational behaviors of functionally graded(FG)cylindrical beams with radially and axially varying material inhomogeneities are investigated.Based on a high-order cylindrical beam model,where the shear deformation and rotary inertia are both considered,the two coupled governing differential motion equations for the deflection and rotation are established.The analytical bending solutions for various boundary conditions are derived.In the vibrational analysis of FG cylindrical beams,the two governing equations are firstly changed to a single equation by means of an auxiliary function,and then the vibration mode is expanded into shifted Chebyshev polynomials.Numerical examples are given to investigate the effects of the material gradient indices on the deflections,the stress distributions,and the eigenfrequencies of the cylindrical beams,respectively.By comparing the obtained numerical results with those obtained by the three-dimensional(3D)elasticity theory and the Timoshenko beam theory,the effectiveness of the present approach is verified.

Application of Grey Associative Analysis to the Assessment of Regional Water Security

The assessment of water security is an important content in the security management of water resources due to the fact that the state of water security directly affects both the sustainable development of regional economy and the improvement on the living quality of mankind. Grey associative analysis is introduced and applied to assessment of water security on the basis of grey characteristics of the assessment index system of water security. As a case study shows, grey associative analysis is used for evaluating water security of some provinces in China, and the satisfactory assessment results are obtained. The sequence of provinces in China with regard to water security from good to poor is obtained and, moreover, the water security level of each region is also confirmed. The results obtained accord with the actual state of each region. They are of practical significance and can be used to guide the management of regional water security and a sustainable development of the economy therein. At the same time, the results demonstrate that grey associative analysis provides a new method for assessing water

Properties and Stability of Max-Product Fuzzy Bi-Directional Associative Memory

In this paper, a fuzzy operator of max-product is defined at first, and the fuzzy bi-directional associative memory (FBAM) based on the fuzzy operator of max-product is given. Then the properties and the Lyapunov stability of equilibriums of the networks are studied.

Bifurcation Analysis of Reduced Network Model of Coupled Gaussian Maps for Associative Memory

This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can generate various phenomena including asymmetric fixed and periodic points. The Gaussian associative memory can effectively recall one of the stored patterns, which were triggered by an input pattern by associating the asymmetric two-periodic points observed in the coupled system with the binary values of output patterns. To investigate the Gaussian associative memory model, we formed its reduced model and analyzed the bifurcation structure. Pseudo-patterns were observed for the proposed model along with other conventional associative memory models, and the obtained patterns were related to the high-order or quasi-periodic points and the chaotic trajectories. In this paper, the structure of the Gaussian associative memory and its reduced models are introduced as well as the results of the bifurcation analysis are presented. Furthermore, the output sequences obtained from simulation of the recalling process are presented. We discuss the mechanism and the characteristics of the Gaussian associative memory based on the results of the analysis and the simulations conducted.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

Diabetes mellitus and prostate cancer risk:A mendelian randomization analysis

BACKGROUND Some studies have directed towards an association between diabetes mellitus(DM)and prostate cancer(PCa);however,this specific relationship remains inconclusive.In recent years,Mendelian randomization(MR)has become a widely used analytical method for inferring epidemiological causes.AIM To investigated the potential relationship between DM and PCa using MR.METHODS We downloaded relevant data on"diabetes"and"PCa"from the IEU OpenGWAS project database,performed three different methods to conduct MR,and carried out sensitivity analysis for verification.RESULTS The results indicated that DM was an independent risk factor for PCa.The odds ratio(OR)values obtained using the inverse variance weighted method in this study were as follows:OR=1.018(95%confidence interval:1.004-1.032),P=0.014.CONCLUSION We found that DM could increase the incidence rate of PCa.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

The association analysis polymorphism of CDKAL1 and diabetic retinopathy in Chinese Han population

AIM： To identify the contribution of CDKAL1 to the development of diabetic retinopathy（DR） in Chinese population.·METHODS： A case-control study was performed to investigate the genetic association between DR and polymorphic variants of CDKAL1 in Chinese Han population with type 2 diabetes mellitus（T2DM）. A welldefined population with T2 DM, consisting of 475 controls and 105 DR patients, was recruited. All subjects were genotyped for the genetic variant（rs10946398） of CDKAL1. Genotyping was performed by i PLEX technology. The association between rs10946398 and T2 DM was assessed by univariate and multivariate logistic regression（MLR） analysis.· RESULTS： There were significant differences in C allele frequencies of rs10946398（CDKAL1） between control and DR groups（45.06% versus 55.00%, P 〈0.05）.The rs10946398 of CDKAL1 was found to be associated with the increased risk of DR among patients with diabetes.·CONCLUSION： Our findings suggest that rs10946398 of CDKAL1 is independently associated with DR in a Chinese Han population.

Disagreement between symptom-reflux association analysis parameters in pediatric gastroesophageal reflux disease investigation

AIM: To assess the agreement within 3 commonly used symptom-reflux association analysis (SAA) parameters investigating gastroesophageal reflux disease (GERD) in infants. METHODS: Twenty three infants with suspected GERD were included in this study. Symptom index (SI), Symptom sensitivity index (SSI) and symptom association probability (SAP) related to cough and irritability were calculated after 24 h combined pH/multiple intraluminal impedance (MII) monitoring. Through defined cutoff values, SI, SSI and SAP values are differentiated in normal and abnormal, whereas abnormal values point towards gastroesophageal reflux (GER) as the origin of symptoms. We analyzed the correlation and the concordance of the diagnostic classification of these 3 SAA parameters.RESULTS: Evaluating the GER-irritability association, SI, SSI and SAP showed non-identical classification of normal and abnormal cases in 39.2% of the infants. When irritability was taken as a symptom, there was only a poor inter-parameter association between SI and SSI, and between SI and SAP (Kendall’s tau b = 0.37, P < 0.05; Kendall’s tau b = 0.36, P < 0.05, respectively). Evaluating the GER-cough association, SI, SSI and SAP showed non-identical classification of normal and abnormal cases in 52.2% of the patients. When cough was taken as a symptom, only SI and SSI showed a poor inter-parameter association (Kendall’s tau b = 0.33, P < 0.05). CONCLUSION: In infants investigated for suspected GERD with pH/MII-monitoring, SI, SSI and SAP showed a poor inter-parameter association and important dis-agreements in diagnostic classification. These limitations must be taken into consideration when interpreting the results of SAA in infants.

Molecular Diversity and Association Analysis of Drought and Salt Tolerance in Gossypium hirsutum L. Germplasm

Association mapping is a useful tool for the detection of genes selected during plant domestication based on their linkage disequilibrium（LD）. This study was carried out to estimate genetic diversity, population structure and the extent of LD to develop an association framework in order to identify genetic variations associated with drought and salt tolerance traits. 106 microsatellite marker primer pairs were used in 323 Gossypium hirsutum germplasms which were grown in the drought shed and salt pond for evaluation. Polymorphism（PIC=0.53） was found, and three groups were detected（K=3） with the second likelihood ΔK using STRUCTURE software. LD decay rates were estimated to be 13-15 cM at r2 0.20. Significant associations between polymorphic markers and drought and salt tolerance traits were observed using the general linear model（GLM） and mixed linear model（MLM）（P 0.01）. The results also demonstrated that association mapping within the population structure as well as stratification existing in cotton germplasm resources could complement and enhance quantitative trait loci（QTLs） information for marker-assisted selection.

An approach to incorporate linkage disequilibrium structure into genomic association analysis

In this study, we propose to use the principal component analysis （PCA） and regression model to incorporate linkage disequilibrium （LD） in genomic association data analysis. To accommodate LD in genomic data and reduce multiple testing, we suggest performing PCA and extracting the PCA score to capture the variation of genomic data, after which regression analysis is used to assess the association of the disease with the principal component score. An empirical analysis result shows that both genotype-based correlation matrix and haplotype-based LD matrix can produce similar results for PCA. Principal component score seems to be more powerful in detecting genetic association because the principal component score is quantitatively measured and may be able to capture the effect of multiple loci.

Association analysis of grain traits with SSR markers between Aegilops tauschii and hexaploid wheat(Triticum aestivum L.)

Seven important grain traits, including grain length（GL）, grain width（GW）, grain perimeter（GP）, grain area（GA）, grain length/width ratio（GLW）, roundness（GR）, and thousand-grain weight（TGW）, were analyzed using a set of 139 simple sequence repeat（SSR） markers in 130 hexaploid wheat varieties and 193 Aegilops tauschii accessions worldwide. In total, 1 612 alleles in Ae. tauschii and 1 360 alleles in hexaploid wheat（Triticum aestivum L.） were detected throughout the D genome. 197 marker-trait associations in Ae. tauschii were identified with 58 different SSR loci in 3 environments, and the average phenotypic variation value（R2） ranged from 0.68 to 15.12%. In contrast, 208 marker-trait associations were identified in wheat with 66 different SSR markers in 4 environments and the average phenotypic R2 ranged from 0.90 to 19.92%. Further analysis indicated that there are 6 common SSR loci present in both Ae. tauschii and hexaploid wheat, which are significantly associated with the 5 investigated grain traits（i.e., GA, GP, GR, GL, and TGW） and in total, 16 alleles derived from the 6 aforementioned SSR loci were shared by Ae. tauschii and hexaploid wheat. These preliminary data suggest the existence of common alleles may explain the evolutionary process and the selection between Ae. tauschii and hexaploid wheat. Furthermore, the genetic differentiation of grain shape and thousand-grain weight were observed in the evolutionary developmental process from Ae. tauschii to hexaploid wheat.

Single-nucleotide polymorphisms,mapping and association analysis of 1-FFT-A1 gene in wheat

Fructans are major nonstructural carbohydrates in wheat （Triticum aestivum L.）. Fructan 1-fructosyltransferase （1-FFT） is the key enzyme in fructan biosynthesis. In the present study, 96 sequence variants were detected in the 1-FFT-A 1 gene among 26 wheat accessions including UR208, and 15 of them result in amino acid substitutions, forming four haplotypes. Two markers M39 and M2164 were developed based on the InDe121-39 and SNP-2164 polymorphisms to distinguish the three haplotypes in the 1-FFT-AI. 1-FFT-A1 was located on chromosome 4A using marker M2164 and was flanked by markers Xcwm27 and 6-SFT-A 1. By association analysis using a natural wheat population consisted of 154 accessions, the results showed that the two markers were significantly associated with water-soluble carbohydrate （WSC） content in the lower internode stem and total stem at the early and middle grain filling stages, 1 000-grain weight （TGW） at different grain filling stages and peduncle length （PLE）. Comparison of the effects of three haplotypes on agronomic traits indicated that TGW, PLE and total number of spikelets per spike （TNSS）were significantly influenced by haplotypes. Haplll showed a significant positive effect on TGW, PLE and TNSS.

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92.

A Developed Algorithm of Apriori Based on Association Analysis

A method for mining frequent itemsets by evaluating their probability of supports based on association analysis is presented. This paper obtains the probability of every 1\|itemset by scanning the database, then evaluates the probability of every 2\|itemset, every 3\|itemset, every k \|itemset from the frequent 1\|itemsets and gains all the candidate frequent itemsets. This paper also scans the database for verifying the support of the candidate frequent itemsets. Last, the frequent itemsets are mined. The method reduces a lot of time of scanning database and shortens the computation time of the algorithm.