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Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset. [ABSTRACT FROM AUTHOR]

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors. [ABSTRACT FROM AUTHOR]

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. [ABSTRACT FROM AUTHOR]

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was “Data”, which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested. [ABSTRACT FROM AUTHOR]

Introduction: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. Methods: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). Results: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. Conclusion: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants. [ABSTRACT FROM AUTHOR]

Ports are essential and strategic nodes of international trade and economic activities. The integration of port resources helps enhance the optimal division of port functions to further promote economic development. This paper explores the evolutionary trends of port integration policy from 2011 to 2021 in China through a text-mining approach. We first used the Latent Dirichlet Allocation (LDA) topic analysis method to analyze the port integration policies and summarize the evolutionary trends of port policies. In addition, an association analysis was conducted to explore port integration policies' impacts by examining their relationships among the environmental protection, digitalization level, and port development scale. Our findings suggested that China's port integration policy has evolved from specific and simple to abstract and complicated at the managerial level. Port development has enriched from infrastructure construction to the ideology of economic development. Our findings show that China's port integration is gradually improving the port operation. The findings of the study contribute to the extant literature by analyzing the port integration policy evolution in China and can be referred to by other countries. • Text mining method was used to explore the port integration policy evolution. • Port integration policy evolved from specific and simple to abstract and complicated. • Port development enriched from basic infrastructure to the governance model. • The evolution of port integration policy was related to reducing carbon emissions in the port sector. • The evolution of port integration policy made the ports intelligent in association with digital technology such as big data and AI. [ABSTRACT FROM AUTHOR]

The UPI (University Personality Inventory) is a mental health check in which respondents answer yes or no to whether the symptoms indicated by each question match their own symptoms. The UPI consists of a questionnaire asking the presence of mental symptoms and a questionnaire asking the presence of physical symptoms. In this paper, we find strong causal relationships between mental symptoms and physical symptoms in UPI by calculating lift-value used in marketing. Then, by visualizing them as a network, we try to understand the overall causal relationships between mental and physical symptoms and obtain knowledge that can contribute to health of students. We also try to remove confounding from these lift-value. Confounding is the creation of spurious causal relationships by confounding factors. This paper has significance because 1)it shows effectiveness of using marketing methods to address health sector problem, and 2) it focuses confounding problem in association rules. [ABSTRACT FROM AUTHOR]

Functional linear regression models have been widely used in the gene association analysis of complex traits. These models retain all the genetic information in the data and take full advantage of spatial information in genetic variation data, which leads to brilliant detection power. However, the significant association signals identified by the high-power methods are not all the real causal SNPs, because it is easy to regard noise information as significant association signals, leading to a false association. In this paper, a method based on the sparse functional data association test (SFDAT) of gene region association analysis is developed based on a functional linear regression model with local sparse estimation. The evaluation indicators CSR and DL are defined to evaluate the feasibility and performance of the proposed method with other indicators. Simulation studies show that: (1) SFDAT performs well under both linkage equilibrium and linkage disequilibrium simulation; (2) SFDAT performs successfully for gene regions (including common variants, low-frequency variants, rare variants and mix variants); (3) With power and type I error rates comparable to OLS and Smooth, SFDAT has a better ability to handle the zero regions. The Oryza sativa data set is analyzed by SFDAT. It is shown that SFDAT can better perform gene association analysis and eliminate the false positive of gene localization. This study showed that SFDAT can lower the interference caused by noise while maintaining high power. SFDAT provides a new method for the association analysis between gene regions and phenotypic quantitative traits. [ABSTRACT FROM AUTHOR]

Phenotype–gene association studies can uncover disease mechanisms for translational research. Association with multiple phenotypes or clinical variables in complex diseases has the advantage of increasing statistical power and offering a holistic view. Existing multi-variate association methods mostly focus on SNP-based genetic associations. In this paper, we extend and evaluate two adaptive Fisher's methods, namely AFp and AFz, from the p-value combination perspective for phenotype–mRNA association analysis. The proposed method effectively aggregates heterogeneous phenotype–gene effects, allows association with different data types of phenotypes, and performs the selection of the associated phenotypes. Variability indices of the phenotype–gene effect selection are calculated by bootstrap analysis, and the resulting co-membership matrix identifies gene modules clustered by phenotype–gene effect. Extensive simulations demonstrate the superior performance of AFp compared to existing methods in terms of type I error control, statistical power and biological interpretation. Finally, the method is separately applied to three sets of transcriptomic and clinical datasets from lung disease, breast cancer, and brain aging and generates intriguing biological findings. [ABSTRACT FROM AUTHOR]

Background: In an earlier paper, we examined the relationship between headache-attributed disability, measured as proportion of time in ictal state, and lost productivity. In a linear model, we found positive and significant associations with lost paid worktime, lost household worktime and total lost productivity (paid + household), but with high variance, which was increased when headache intensity was introduced as a factor. We speculated that analyses based on headache frequency alone as the independent variable, eliminating both the subjectivity of intensity estimates and the uncertainties of duration, might show stronger associations. Methods: Focusing on migraine, we used individual participant data from 16 countries surveyed either in population-based studies or in the Eurolight project. These data included frequency (headache days/month), usual attack duration (hours), usual headache intensity ("not bad", "quite bad", "very bad") and lost productivity from paid and household work according to enquiries using the Headache-Attributed Lost Time (HALT) questionnaire. We used multiple linear regressions, calculating regression equations along with unstandardized and standardized regression coefficients. We made line and bar charts to visualize relationships. Results: Both frequency and intensity were significant predictors of lost productivity in all multiple linear regressions, but duration was a non-significant predictor in several of the regressions. Predicted productivity in paid work decreased among males by 0.75–0.85 days/3 months for each increase of 1 headache day/month, and among females by 0.34–0.53 days/3 months. In household chores, decreases in productivity for each added day/month of headache were more similar (0.67–0.87 days/3 months among males, 0.83–0.89 days/3 months among females). Visualizations showed that the impact of duration varied little across the range of 2–24 h. The standardized regression coefficients demonstrated that frequency was a much better predictor of lost productivity than intensity or duration. Conclusion: In the relationship between migraine-attributed impairment (symptom burden) and lost productivity, frequency (migraine days/month) is the dominating variable – more important than headache intensity and far more important than episode duration. This has major implications for current practice in headache care and for health policy and health-resource investment. Preventative drugs, grossly underutilized in current practice, offer a high prospect of economic benefit (cost-saving), but new preventative drugs are needed with better efficacy and/or tolerability. [ABSTRACT FROM AUTHOR]

AMPK plays an important role in regulating the metabolism of carbohydrate, lipid and protein in an organism, and is considered to be a key regulator of cellular energy homeostasis. In recent years, attention has been drawn to AMPK subunit polymorphisms and their association with economical traits of domestic animals and fowls. PRKAB1 encodes the β1 regulatory subunit of AMPK, and it has been reported that PRKAB1 may be applied in breeding programs of meat-type chicken. To date, the polymorphism of goat PRKAB1 gene and its associations remain unknown. In this paper, the polymorphism of PRKAB1 gene was detected in 316 goats of three breeds. A total of four novel single nucleotide polymorphisms (SNPs) of PRKAB1 gene were revealed by sequence analysis. Among them, three were in the coding region (285 C > A, 297 C > A, 309 C > T), and they were all synonymous. One was in the intron (229 A > G). The associations between polymorphic loci and the growth traits of Xuhuai and Haimen goats were analyzed, and significant associations were found in body length index and trunk index (p < 0.05) for Xuhuai breed, while no significant associations in Haimen breed. Our results provide useful information for the improvement and breeding of Chinese native goats. [ABSTRACT FROM AUTHOR]

Genome-wide association analysis is an important approach to identify genetic variants associated with complex traits. Complex traits are not only affected by single gene loci, but also by the interaction of multiple gene loci. Studies of association between gene regions and quantitative traits are of great significance in revealing the genetic mechanism of biological development. There have been a lot of studies on single-gene region association analysis, but the application of functional linear models in multi-gene region association analysis is still less. In this paper, a functional multi-gene region association analysis test method is proposed based on the functional linear model. From the three directions of common multi-gene region method, multi-gene region weighted method and multi-gene region loci weighted method, that test method is studied combined with computer simulation. The following conclusions are obtained through computer simulation: (a) The functional multi-gene region association analysis test method has higher power than the functional single gene region association analysis test method; (b) The functional multi-gene region weighted method performs better than the common functional multi-gene region method; (c) the functional multi-gene region loci weighted method is the best method for association analysis on three directions of the common multi-gene region method; (d) the performance of the Step method and Multi-gene region loci weighted Step for multi-gene regions is the best in general. Functional multi-gene region association analysis test method can theoretically provide a feasible method for the study of complex traits affected by multiple genes. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a leading cause of cancer morbidity and mortality worldwide. The etiology and pathogenesis of CRC remain unclear. A growing body of evidence suggests dysbiosis of gut bacteria can contribute to the occurrence and development of CRC by generating harmful metabolites and changing host physiological processes. Metabolomics, a systems biology method, will systematically study the changes in metabolites in the physiological processes of the body, eventually playing a significant role in the detection of metabolic biomarkers and improving disease diagnosis and treatment. Metabolomics, in particular, has been highly beneficial in tracking microbially derived metabolites, which has substantially advanced our comprehension of host-microbiota metabolic interactions in CRC. This paper has briefly compiled recent research progress of the alterations of intestinal flora and its metabolites associated with CRC and the application of association analysis of metabolomics and gut microbiome in the diagnosis, prevention, and treatment of CRC; furthermore, we discuss the prospects for the problems and development direction of this association analysis in the study of CRC. Gut microbiota and their metabolites influence the progression and causation of CRC, and the association analysis of metabolomics and gut microbiome will provide novel strategies for the prevention, diagnosis, and therapy of CRC. [ABSTRACT FROM AUTHOR]

The relationships in multivariable data are intricate, and there are usually implicit associations between scalar values variables. However, existing association analysis methods lack spatial measurement of scalar values, and fail to collaboratively analyze the association between scalar values and variables. Thus association results may be one-sided. In this paper, we construct a scalar-value neighborhood graph to preserve the spatial information for scalar values and propose a graph neural network model composed of multiple graph convolutional layers and a self-attention mechanism for learning the low-dimensional vectors of scalar values and variables simultaneously. Several case studies show the scalability and flexibility of our method on analyzing the association between scalar values and variables. [ABSTRACT FROM AUTHOR]

The safety and stability of power networks have become increasingly important in the field of industrial control. Traditional information analysis for power networks overly relies on expert knowledge, and existing analysis models suffer from problems such as high algorithm complexity and rule redundancy. To address these issues, this paper proposes an advanced alarm information correlation analysis method that takes into account the characteristics of power networks. The method first eliminates noisy parts in the original alarm logs through a pre-processing module, then generates alarm transaction sets using a proposed method based on dynamic sliding time window, and subsequently applies the FP-Growth algorithm to mine alarm association rules for power networks. Finally, a time-based alarm rule filtering algorithm is proposed to eliminate invalid rules. Experiments conducted on alarm data collected from a situation awareness platform deployed in a power grid company show that this method reduces the redundancy of alarm rules by an average of about 30% compared to other similar association analysis method, and can effectively extract key alarm rules in power networks to guide fault warning. [ABSTRACT FROM AUTHOR]

Human activities such as deforestation, farming, and uncontrolled construction have had a negative impact on mangrove areas in the Reserve. This paper explores how the mangrove area and space have changed since the establishment of the Dongzhaigang Mangrove Nature Reserve, as well as the relationship between this change and the community residents around the Reserve. Six remote sensing images, from 1976 to 2021, were used to obtain wetland data of different ages in the Dongzhaigang Mangrove Forest Nature Reserve in Haikou City. Using the transfer matrix, a questionnaire survey, and principal component analysis, we analyzed the changes in mangrove forests in protected areas and their relationship with surrounding communities over the past 45 years. The results show that: (1) From 1976 to 2021, the area of mangroves in the reserve has increased from 1, 395.84 hm2 in 1976 to 1, 589.28 hm2 in 2021, showing a trend of "suddenly slowing and rising" during this period, and the proportion of mangrove area has increased from 28.6% in 1976 to 32.6% in 2021, becoming the main wetland type. The largest decline (250 hm2) in the mangrove area was from 1976 to 1985. The largest growth occurred from 2005 to 2018, when the mangrove area increased by 190.08 hm2. (2) From 1976 to 2021, 91.77 hm2 of mangrove wetlands in the study area were converted into other wetlands, 10.71 hm2 were converted into non-wetlands, and 267.71 hm2 of other wetlands and 28.21 hm2 of non-wetlands were converted into mangrove wetlands. In the same period, the transfer targets of mangrove forests in the reserve were muddy beaches (34.32 hm2), rivers (25.81 hm2), marine aquaculture farms (21.06 hm2), other land (10.58 hm2) and flooded wetlands/interiors. Land flats (8.26 hm2); silt beaches (177.41 hm2), rivers (38.18 hm2), other land (28.21 hm2), deltas/sands/sand islands (22.15 hm2), flooding Wetlands/inland tidal flats (21.79 hm2), and paddy fields (6.34 hm2) are the most common areas transferred into mangroves. (3) Fishing income from surrounding community-dwelling individuals was significantly associated with farming, disfiguring forest-digging pond areas, conservation awareness, and mangrove area variation. The factor quality of fishing practices (cos2), values of fishing practices and income, months of fishing, fishing volume, and conservation attitude in the surrounding community residents accounted for more than 0.6 in the principal component analysis of area change of mangrove forests, where the factor quality of fishing practices and fishing income were more than 0.8. The decline in residential fishing frequency corresponds with the shift in household income sources, the occupational move from fishing to wage and service industries, the obvious increase in conservation awareness and the conservation attitude of residents, and the transformation of the mangrove area away from serious destruction to less fragmentation. The contributions of freshwater farms, resident population, number of secondary schools, and village to mangrove distance factors were all greater than 10 and negatively correlated with mangrove area change. Therefore, it is necessary to explore the relationship between mangroves and community residents to obtain a scientific reference for the utilization, protection, restoration, and management of mangroves in the Dongzhaigang Mangrove Nature Reserve. [ABSTRACT FROM AUTHOR]

Soil pollution by heavy metals is a serious environmental concern globally. Hexavalent (VI) chromium (Cr) is one of the main pollutants causing groundwater and soil heavy metal pollution. Miscanthus sinensis is a C4 perennial grass species with a high level of heavy metal tolerance. This species can effectively remove Cr from soils and maintain desirable biomass production under Cr stress. This research aimed to characterize and compare Cr accumulation in 58 genotypes of M. sinensis and to develop Expressed Sequence Tag–Simple Sequence Repeat (EST-SSR) markers associated with Cr tolerance. The results show that the pattern of translocation of Cr in plants differed among the 58 M. sinensis genotypes following treatment of 200 mg/L of Cr6+; however, in most genotypes, the Cr was primarily accumulated in roots. A total of 43,367 EST-SSRs were identified, and 88 EST-SSR primer pairs corresponding to candidate genes involved in Cr accumulation in M. sinensis were selected for validation. Subsequently, 170 polymorphic loci generated from 24 validated EST-SSRs were used for the population structure and marker–trait association analysis. Based on a general linear model (GLM), a total of 46 associations were identified (p < 0.05), with 14 EST-SSRs markers associated with target traits. Among them, four genes related to ABC transporters, wall-associated receptor kinases, as well as two high-affinity sulfate transporters (ST), were identified under Cr stress (p < 0.05). The results of this study help to accelerate the screening across M. sinensis genotypes for desirable traits under Cr stress and provide a platform for M. sinensis genetic improvement and molecular-marker-assisted breeding. [ABSTRACT FROM AUTHOR]

Colour is an important factor in the expression of recognizability and cultural identity of regional cultural and creative design. At present, the colour recognition of regional characteristic and the colour association of regional culture mainly rely on the designer's subjective perception. To obtain the target colour resources with reference for regional cultural and design need, this study proposes a scientific method of colour extraction and strong colour association matching of Shanghai style Chinese paintings by machine learning, and applies the related results to the colour design of cultural and creative products. Firstly, using the SLIC superpixel algorithm and Mean shift algorithm to realize the overall dimensionality reduction of the image features and the colour aggregation gradually, so as to extract the characteristic colours of Shanghai style Chinese paintings; Secondly, we introduce a data mining algorithm (Apriori) to mine out the association rules from multiple characteristics colours and filter out strongly associated colour combinations; Finally, we apply the colour combinations and colour tones to the colour design of the creative products. In order to verify the scientificity of the colour extraction and colour matching method proposed in this paper, we selected another painter's paintings in the same school as the algorithm experimental validation sample, similar results were obtained. In addition, we measured user satisfaction using the degree of awakening to Shanghai style culture and the propensity to make consumer decisions as the evaluation dimensions, which proves that the method of this study is effective. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to search for molecular markers closely linked to the main agronomic traits of broad bean, and to explore the excellent allelic variation and germplasm of agronomic traits. [Method] 321 faba bean accessions were tested, and their initial pod height, initial pod nodes, pod nodes, reproductive nodes, effective grains, effective branches, effective pods, grains per pod, pod length, pod width, grain area, grain thickness, grain perimeter, grain length, grain width, plant height and 100-grain weight were investigated. 76 pairs of SSR markers with significant polymorphism were used for genotyping. Based on the analysis of population structure, the GLM and MLM models of Tassel 2. 1 software were used for correlation analysis of the above 17 agronomic traits. [Result] The variation coefficient of traits ranged from 10. 06 % to 51. 64 %, with an average value of 25. 07 %, indicating that the tested population had rich phenotypic diversity. A total of 389 polymorphic loci were detected by 76 markers in 321 faba beans, and the average number of alleles at each locus was 5. 12, with a variation range of 2 - 11. The PIC value ranged from 0. 1903 to 0. 7766, with an average of 0. 5156, indicating that the selected markers had high polymorphism. Structure analysis was performed using structure 2. 3. 4, and 321 samples into two subgroups, which showed that the population structure was relatively simple and thus it was beneficial to association analysis. Based on GLM model, eleven markers were detected associated with twelve agronomic traits (P < 0. 01), and the R2 of phenotypic variation by a single marker was 0. 0393 - 0. 1101. Based on the MLM model, eight markers were detected associated with eight agronomic traits were (P < 0. 01), and the R2 of phenotypic variation by a single marker was 0. 0291 - 0. 0897. Finally, based on the phenotypic effect of allelic variation in the extremely significant (P < 0. 01) association loci in the results of MIM model analysis, seven excellent allelic variations with the greatest synergistic potential on number of pods, number of grains per pod, effective pod number, effective grain number, plant height, grain thickness, grain length and grain perimeter, and seven excellent accessions were screened out. [Conclusion] Through association analysis, a number of molecular markers associated with agronomic traits of faba bean and excellent germplasm were mined, which laid a theoretical foundation for molecular marker-assisted selection breeding of faba bean and the selection of parental materials. [ABSTRACT FROM AUTHOR]

Thunderstorms are a common natural phenomenon posing significant hazards to power systems, structures, and humans. With technological advancements, protection against lightning is gradually shifting from passive to active measures, which require the prediction of thunderstorm occurrences. Current research on lightning warning relies on various data sources, such as satellite data and atmospheric electric field data. However, these studies have placed greater emphasis on the process of warning implementation, overlooking the correlation between parameters used for lightning warning and lightning phenomena. This study relied on the ERA5 dataset and lightning location dataset from 117.5°E to 119.5°E longitude and 24.5°N to 25.5°N latitude during 2020–2021, utilizing Kriging interpolation to standardize the spatiotemporal precision of different parameters. After that, we conducted preliminary screening of the involved parameters based on the chi-squared test and utilized the Apriori algorithm to identify parameter intervals that were strongly associated with the occurrence of lightning. Subsequently, we extracted strong association rules oriented towards the occurrence of lightning and analyzed those rules with respect to lightning current amplitude, types, and ERA5 parameters. We found that thunderstorm phenomena are more likely to occur under specific ranges of temperature, humidity, and wind speed conditions, and we determined their parameter ranges. After that, we divided the target area into regions with different levels of lightning probability based on the strong association rules. By comparing the actual areas where lightning phenomena occurred with the areas at high risk of lightning based on ERA5 parameters, we validated the credibility of the obtained strong association rules. [ABSTRACT FROM AUTHOR]

The association between pre-harvest sprouting (PHS) and seed coat color has long been recognized. Red-grained wheats generally exhibit greater PHS resistance compared to white-grained wheat, although variability in PHS resistance exists within red-grained varieties. Here, we conducted a genome-wide association study on a panel consisting of red-grained wheat varieties, aimed at uncovering genes that modulate PHS resistance and red color components of seed coat using digital image processing. Twelve loci associated with PHS traits were identified, nine of which were described for the first time. Genetic loci marked by SNPs AX-95172164 (chromosome 1B) and AX-158544327 (chromosome 7D) explained approximately 25% of germination index variance, highlighting their value for breeding PHS-resistant varieties. The most promising candidate gene for PHS resistance was TraesCS6B02G147900, encoding a protein involved in aleurone layer morphogenesis. Twenty-six SNPs were significantly associated with grain color, independently of the known Tamyb10 gene. Most of them were related to multiple color characteristics. Prioritization of genes within the revealed loci identified TraesCS1D03G0758600 and TraesCS7B03G1296800, involved in the regulation of pigment biosynthesis and in controlling pigment accumulation. In conclusion, our study identifies new loci associated with grain color and germination index, providing insights into the genetic mechanisms underlying these traits. [ABSTRACT FROM AUTHOR]

Periprosthetic joint infections (PJIs) are serious complications of prosthetic surgery. The criteria for the diagnosis of PJI integrate clinical and laboratory findings in a complex and sometimes inconclusive workflow. Host immune factors hold potential as diagnostic biomarkers in bone and joint infections. We reported that the humoral pattern-recognition molecule long pentraxin 3 (PTX3) predicts PJI in total hip and knee arthroplasty (THA and TKA, respectively). If and how genetic variation in PTX3 and inflammatory genes that affect its expression (IL-1β, IL-6, IL-10, and IL-17A) contributes to the risk of PJI is unknown. We conducted a case–control study on a Caucasian historic cohort of THA and TKA patients who had prosthesis explant due to PJI (cases) or aseptic complications (controls). Saliva was collected from 93 subjects and used to extract DNA and genotype PTX3, IL-1β, IL-6, IL-10, and IL-17A single-nucleotide polymorphisms (SNPs). Moreover, the concentration of IL-1β, IL-10, and IL-6 was measured in synovial fluid and plasma. No association was found between PTX3 polymorphisms and PJI; however, the AGG haplotype, encompassing rs2853550, rs1143634, and rs1143627 in IL-1β, was linked to the infection (p = 0.017). Also, synovial levels of all inflammatory markers were higher in cases than in controls, and a correlation emerged between synovial concentration of PTX3 and that of IL-1β in cases only (Spearman r = 0.67, p = 0.004). We identified a relationship between rs2853550 and the synovial concentration of IL-1β and PTX3. Our findings suggest that IL-1β SNPs could be used for the early identification of THA and TKA patients with a high risk of infection. [ABSTRACT FROM AUTHOR]

Phenolamides are important secondary metabolites in plant species. They play important roles in plant defense responses against pathogens and insect herbivores, protection against UV irradiation and floral induction and development. However, the accumulation and variation in phenolamides content in diverse maize lines and the genes responsible for their biosynthesis remain largely unknown. Here, we combined genetic mapping, protein regulatory network and bioinformatics analysis to further enhance the understanding of maize phenolamides biosynthesis. Sixteen phenolamides were identified in multiple populations, and they were all significantly correlated with one or several of 19 phenotypic traits. By linkage mapping, 58, 58, 39 and 67 QTLs, with an average of 3.9, 3.6, 3.6 and 4.2 QTLs for each trait were mapped in BBE1, BBE2, ZYE1 and ZYE2, explaining 9.47%, 10.78%, 9.51% and 11.40% phenotypic variation for each QTL on average, respectively. By GWAS, 39 and 36 significant loci were detected in two different environments, 3.3 and 2.8 loci for each trait, explaining 10.00% and 9.97% phenotypic variation for each locus on average, respectively. Totally, 58 unique candidate genes were identified, 31% of them encoding enzymes involved in amine and derivative metabolic processes. Gene Ontology term analysis of the 358 protein-protein interrelated genes revealed significant enrichment in terms relating to cellular nitrogen metabolism, amine metabolism. GRMZM2G066142, GRMZM2G066049, GRMZM2G165390 and GRMZM2G159587 were further validated involvement in phenolamides biosynthesis. Our results provide insights into the genetic basis of phenolamides biosynthesis in maize kernels, understanding phenolamides biosynthesis and its nutritional content and ability to withstand biotic and abiotic stress. [ABSTRACT FROM AUTHOR]

Introduction: Air curing (AC) plays a crucial role in cigar tobacco leaf production. The AC environment is relatively mild, contributing to a diverse microbiome. Fungi are important components of the tobacco and environmental microbiota. However, our understanding of the composition and function of fungal communities in AC remains limited. Methods: In this study, changes in the chemical constituents and fungal community composition of cigar tobacco leaves during AC were evaluated using flow analysis and high-throughput sequencing. Results: The moisture, water-soluble sugar, starch, total nitrogen, and protein contents of tobacco leaves exhibited decreasing trends, whereas nicotine showed an initial increase, followed by a decline. As determined by highthroughput sequencing, fungal taxa differed among all stages of AC. Functional prediction showed that saprophytic fungi were the most prevalent type during the AC process and that the chemical composition of tobacco leaves is significantly correlated with saprophytic fungi. Conclusion: This study provides a deeper understanding of the dynamic changes in fungal communities during the AC process in cigar tobacco leaves and offers theoretical guidance for the application of microorganisms during the AC process. [ABSTRACT FROM AUTHOR]

The Cyber-Physical System covers a wide range of applications, many of which are involved in critical infrastructure, and the cybersecurity attacks on them become more and more threatening. Currently, most of the comprehensive analysis of compound attacks depend on the experience of security analysts. To improve the efficiency and accuracy of compound attack research, this paper introduces a knowledge graph into compound attack detection and constructs a cybersecurity knowledge graph based on the knowledge of known attacks. The cybersecurity knowledge graph can carry out correlation analysis on real-time data to restore the attack process. The main work of this paper is to construct the cybersecurity knowledge graph and to apply mining found compound attacks automatically. Besides, a multi-dimensional data association analysis algorithm based on dynamic clustering mechanism, and an attack chain complementation-pruning method based on optimal reaching path queries are proposed to solve the problem of low efficiency in correlation analysis caused by redundant data and the problem of missing and misunderstandings in the collection data. Experiments show that the cyber security knowledge graph construction method and attack chain optimization-pruning method proposed in this paper improve the accuracy and efficiency of attack chain mining. [ABSTRACT FROM AUTHOR]

While container adoption has witnessed significant growth in facilitating the operation of large-scale applications, this increased attention has also attracted adversaries who exploit numerous vulnerabilities present in contemporary containers. Unfortunately, existing security solutions largely overlooked the need to restrict container access to the shared host kernel, particularly exhibiting critical limitations in enforcing the least privilege for containers during runtime. Hence, we propose Optimus, an automated and comprehensive system that confines container operations and governs their interactions with the host kernel using an association-based system call filtering. Optimus efficiently identifies the essential system calls required by containers and enhances their security posture by dynamically enforcing the minimal set of system calls for each container during runtime. This is achieved through (1) lightweight system call monitoring leveraging eBPF, (2) system call validation via association analysis, and (3) dynamic system call filtering by adopting covert container renewal. Our evaluation shows that Optimus effectively minimizes the necessary system calls for containers while maintaining their serviceability and operational efficiency during runtime. [ABSTRACT FROM AUTHOR]

Background: The genetic progress of fertility and reproduction traits in dairy cattle has been constrained by the low heritability of these traits. Identifying candidate genes and variants associated with fertility and reproduction could enhance the accuracy of genetic selection and expedite breeding process of dairy cattle with low-heritability traits. While the bovine LAP3 and SIRT1 genes exhibit well-documented associations with milk production traits in dairy cattle, their effect on cow fertility have not yet been explored. Eleven single nucleotide polymorphisms (SNPs), comprising five in the promoter (rs717156555: C > G, rs720373055: T > C, rs516876447: A > G, rs461857269: C > T and rs720349928: G > A), two in 5'UTR (rs722359733: C > T and rs462932574: T > G), two in intron 12 (rs110932626: A > G and rs43702363: C > T), and one in 3'UTR of exon 13 (rs41255599: C > T) in LAP3 and one in SIRT1 (rs718329990:T > C) genes, have previously been reported to be associated with various traits of milk production and clinical mastitis in Sahiwal and Karan Fries dairy cattle. In this study, the analysis primarily aimed to assess the impact of SNPs within LAP3 and SIRT1 genes on fertility traits in Sahiwal and Karan Fries cattle. Association studies were conducted using mixed linear models, involving 125 Sahiwal and 138 Karan Fries animals in each breed. The analysis utilized a designated PCR-RFLP panel. Results: In the promoter region of the LAP3 gene, all variants demonstrated significant (P < 0.05) associations with AFC, except for rs722359733: C > T. However, specific variants with the LAP3 gene's promoter region, namely rs722359733: C > T, rs110932626: A > G, rs43702363: C > T, and rs41255599: C > T, showed significant associations with CI and DO in Sahiwal and Karan Fries cows, respectively. The SNP rs718329990: T > C in the promoter region of SIRT1 gene exhibited a significant association with CI and DO in Sahiwal cattle. Haplotype-based association analysis revealed significant associations between haplotype combinations and AFC, CI and DO in the studied dairy cattle population. Animals with H2H3 and H2H4 haplotype combination exhibited higher AFC, CI and DO than other combinations. Conclusions: These results affirm the involvement of the LAP3 and SIRT1 genes in female fertility traits, indicating that polymorphisms within these genes are linked to the studied traits. Overall, the significant SNPs and haplotypes identified in this study could have the potential to enhance herd profitability and ensure long-term sustainability on dairy farms by enabling the selection of animals with early age first calving and enhance reproductive performance in the dairy cattle breeding program. [ABSTRACT FROM AUTHOR]

The livestock industry in Türkiye is vital to the country's agricultural sector and economy. In particular, sheep products are an important source of income and livelihood for many Turkish smallholder farmers in semi-arid and highland areas. Türkiye is one of the largest sheep producers in the world and its sheep production system is heavily dependent on indigenous breeds. Given the importance of the sheep industry in Türkiye, a systematic literature review on sheep breeding and genetic improvement in the country is needed for the development and optimization of sheep breeding programs using modern approaches, such as genomic selection. Therefore, we conducted a comprehensive literature review on the current characteristics of sheep populations and farms based on the most up-to-date census data and breeding and genetic studies obtained from scientific articles. The number of sheep has increased in recent years, mainly due to the state's policy of supporting livestock farming and the increase in consumer demand for sheep dairy products with high nutritional and health benefits. Most of the genetic studies on indigenous Turkish sheep have been limited to specific traits and breeds. The use of genomics was found to be incipient, with genomic analysis applied to only two major breeds for heritability or genome-wide association studies. The scope of heritability and genome-wide association studies should be expanded to include traits and breeds that have received little or no attention. It is also worth revisiting genetic diversity studies using genome-wide single nucleotide polymorphism markers. Although there was no report of genomic selection in Turkish sheep to date, genomics could contribute to overcoming the difficulties of implementing traditional pedigree-based breeding programs that require accurate pedigree recording. As indigenous sheep breeds are better adapted to the local environmental conditions, the proper use of breeding strategies will contribute to increased income, food security, and reduced environmental footprint in a sustainable manner. [ABSTRACT FROM AUTHOR]

Cytonuclear interaction refers to the complex and ongoing process of coevolution between nuclear and organelle genomes, which are responsible for cellular respiration, photosynthesis, lipid metabolism, etc. and play a significant role in adaptation and speciation. There have been a large number of studies to detect signatures of cytonuclear interactions. However, identification of the specific nuclear and organelle genetic polymorphisms that are involved in these interactions within a species remains relatively rare. The recent surge in whole genome sequencing has provided us an opportunity to explore cytonuclear interaction from a population perspective. In this study, we analyzed a total of 3,439 genomes from 7 species to identify signals of cytonuclear interactions by association (linkage disequilibrium) analysis of variants in both the mitochondrial and nuclear genomes across flowering plants. We also investigated examples of nuclear loci identified based on these association signals using subcellular localization assays, gene editing, and transcriptome sequencing. Our study provides a novel perspective on the investigation of cytonuclear coevolution, thereby enriching our understanding of plant fitness and offspring sterility. [ABSTRACT FROM AUTHOR]

Seed weight is an important target trait in pomegranate breeding and culture. Expansins act by loosening plant cell walls and cellulosic materials, permitting turgor-driven cell enlargement. However, the role of expansin genes (EXPs) in pomegranate seed weight remains elusive. A total of 29 PgrEXPs were identified in the 'Dabenzi' genome. These genes were classified into four subfamilies and 14 subgroups, including 22 PgrEXPAs, 5 PgrEXPBs, 1 PgrEXPLA, and 1 PgrEXPLB. Transcriptome analysis of PgrEXPs in different tissues (root, leaf, flower, peel, and seed testa) in 'Dabenzi', and the seed testa of the hard-seeded pomegranate cultivar 'Dabenzi' and soft-seeded cultivar 'Tunisia' at three development stages showed that three PgrEXPs (PgrEXPA11, PgrEXPA22, PgrEXPA6) were highly expressed throughout seed development, especially in the sarcotesta. SNP/Indel markers of these PgrEXPs were developed and used to genotype 101 pomegranate accessions. The association of polymorphic PgrEXPs with seed weight-related traits (100-seed weight, 100-kernel weight, 100-sarcotesta weight, and the percentage of 100-sarcotesta to 100-seed weight) were analyzed. PgrEXP22 was significantly associated with 100-seed weight and 100-sarcotesta weight and is a likely candidate for regulating seed weight and sarcotesta development in particular. This study provides an effective tool for the genetic improvement of seed weight in pomegranate breeding programs. [ABSTRACT FROM AUTHOR]

Soil salinization is the main abiotic stressor faced by crops. An improved understanding of the transcriptional response to salt stress in roots, the organ directly exposed to a high salinity environment, can inform breeding strategies to enhance tolerance and increase crop yield. Here, RNA-sequencing was performed on the roots of salt-tolerant wheat breeding line CH7034 at 0, 1, 6, 24, and 48 h after NaCl treatment. Based on transcriptome data, a weighted gene co-expression network analysis (WGCNA) was constructed, and five gene co-expression modules were obtained, of which the blue module was correlated with the time course of salt stress at 1 and 48 h. Two GO terms containing 249 differentially expressed genes (DEGs) related to osmotic stress response and salt-stress response were enriched in the blue module. These DEGs were subsequently used for association analysis with a set of wheat germplasm resources, and the results showed that four genes, namely a Walls Are Thin 1-related gene (TaWAT), an aquaporin gene (TaAQP), a glutathione S-transfer gene (TaGST), and a zinc finger gene (TaZFP), were associated with the root salt-tolerance phenotype. Using the four candidate genes as hub genes, a co-expression network was constructed with another 20 DEGs with edge weights greater than 0.6. The network showed that TaWAT and TaAQP were mainly co-expressed with fifteen interacting DEGs 1 h after salt treatment, while TaGST and TaZFP were mainly co-expressed with five interacting DEGs 48 h after salt treatment. This study provides key modules and candidate genes for understanding the salt-stress response mechanism in wheat roots. [ABSTRACT FROM AUTHOR]

Genomic studies focusing on the contribution of common and rare genetic variants of schizophrenia and bipolar disorder support the view that substantial risk is conferred through molecular pathways involved in synaptic plasticity in the neurons of cortical and subcortical brain regions, including the hippocampus. Synaptic long-term potentiation (LTP) is central to associative learning and memory and depends on a pattern of gene expression in response to neuronal stimulation. Genes related to the induction of LTP have been associated with psychiatric genetic risk, but the specific cell types and timepoints responsible for the association are unknown. Using published genomic and transcriptomic datasets, we studied the relationship between temporally defined gene expression in hippocampal pyramidal neurons following LTP and enrichment for common genetic risk for schizophrenia and bipolar disorder, and for copy number variants (CNVs) and de novo coding variants associated with schizophrenia. We observed that upregulated genes in hippocampal pyramidal neurons at 60 and 120 min following LTP induction were enriched for common variant association with schizophrenia and bipolar disorder subtype I. At 60 min, LTP-induced genes were enriched in duplications from patients with schizophrenia, but this association was not specific to pyramidal neurons, perhaps reflecting the combined effects of CNVs in excitatory and inhibitory neuron subtypes. Gene expression following LTP was not related to enrichment for de novo coding variants from schizophrenia cases. Our findings refine our understanding of the role LTP-related gene sets play in conferring risk to conditions causing psychosis and provide a focus for future studies looking to dissect the molecular mechanisms associated with this risk. [ABSTRACT FROM AUTHOR]

The three distinct medicinal parts of Angelica sinensis (Oliv.) Diels (Ang) roots are the head, body, and tail (ARH, ARB, and ART, respectively). How endophytic fungi shape the differences in metabolic components among these parts remains unclear. We quantified the distribution of active components and endophytic fungi along the ARH, ARB, and ART and their relationships. Based on the metabolic components and their abundances detected via non-target metabolism, the different medicinal parts were distinguishable. The largest number of dominant metabolic components was present in ART. The difference between ART and ARH was the greatest, and ARB was in a transitional state. The dominant active molecules in ART highlight their effects in haemodynamics improvement, antibacterial, anti-inflammatory, and hormone regulation, while ARH and ARB indicated more haemostasis, blood enrichment, neuromodulation, neuroprotection and tranquilisation, hepatoprotection, and antitumour activities than that of ART. The ARHs, ARBs, and ARTs can also be distinguished from each other based on the endophytic fungi at the microbiome level. The most dominant endophytic fungi were distributed in ART; the differences between ART and ARH were the largest, and ARB was in a transition state, which is consistent with the metabolite distributions. Structural equation modelling showed that the endophytic fungi were highly indicative of the metabolic components. Correlation analysis further identified the endophytic fungi significantly positively correlated with important active components, including Condenascus tortuosus, Sodiomyces alcalophilus, and Pleotrichocladium opacum. The bidirectional multivariate interactions between endophytic fungi and the metabolic components shape their spatial variations along the longitudinal direction in the Ang root. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the most representative deep learning has been applied to various fields, which is greatly influencing human society. However, deep neural networks (DNNs) are still a black-box model, and the process how they make decisions internally is still difficult to understand and control. At the same time, DNNs take up more hardware resources, resulting in high energy consumption. Therefore, it is significant to study the characteristics of deep AI models and deeply understand the interactions between parameters within AI models so as to improve the interpretability of DNNs, optimize their structure and increase their computational efficiency. In this paper, we propose a graph-based interpretability method for deep neural networks (GIMDNN). The running parameters of DNNs are modeled as a graph by using a kernel function or the Graph Transformer Networks (GTN), where the nodes of the graph are obtained by dimensional mapping of the parameters of the DNNs, and the edges are calculated by the Gaussian kernel function. The generated graphs are classified by a graph convolutional network (GCN). The association relationship between the adjacent layers and the running mechanism of DNNs are analyzed, and the importance of the parameters of each layer in the DNNs for the final classification result can be obtained. Convolutional neural networks (CNNs) are one of the most representative models in DNNs. The proposed method is experimentally evaluated on the CNNs. The experimental results show that the proposed method can interpret the associations among the weight parameters as well as the correlation between two adjacent layers. Therefore, the DNNs for special tasks, such as portable applications, edge computing, and so on, can be customized, the number of parameters can be reduced. It is valuable to interpret the operation and principle of CNNs. [ABSTRACT FROM AUTHOR]

Growth traits are the economically important traits of sheep, and screening for genes related to growth and development is helpful for the genetic improvement of ovine growth traits. The fatty acid desaturase 3 (FADS3) is one of the important genes affecting the synthesis and accumulation of polyunsaturated fatty acids in animals. In this study, the expression levels of the FADS3 gene and polymorphism of the FADS3 gene associated with growth traits in Hu sheep were detected using quantitative real-time PCR (qRT-PCR), Sanger sequencing, and KAspar assay. The result showed that the expression levels of the FADS3 gene were widely expressed in all tissues, and the expression level of FADS3 in the lung was significantly higher than in other tissues (p <.05). Then, the polymorphism locus g. 2918 A > C was detected in intron 2 of the FADS3 gene, and associated analysis showed that the mutation in the FADS3 gene was associated significantly with growth traits (including body weight, body height, body length, and chest circumference, p <.05). Therefore, individuals with AA genotype showed significantly better growth traits than those with CC genotype, and FADS3 gene could be a candidate gene for improving growth traits in Hu sheep. [ABSTRACT FROM AUTHOR]

Lipid and energy metabolism are major constituents of mammal growth and thus fattening performance of cattle. This study was designed to evaluate the effects of polymorphisms in lipid and energy metabolism-related genes including oxidized low-density lipoprotein receptor 1 (OLR1), lactoferrin (LTF), stearoyl-CoA desaturase (SCD), beta-lactoglobulin (LGB), thyroglobulin (TG), annexin A9 (ANXA9), myogenic factor 5 (MYF5), protein kinase AMP-activated non-catalytic subunit gamma 3 (PRKAG3), and pituitary-specific transcriptional factor 1 (PIT1), on fattening performance in Simmental cattle. A total of 72 purebred Simmental bulls with a similar initial age and weight were fattened on the same farm for 10 months. Association analysis was performed using linear mixed models. The OLR1 marker was significantly associated with the final weight (FW), hot carcass weight (HCW), chilled carcass weight (CCW), dressing percentage (DP), and total weight gain (TWG). SCD affected the FW, TWG, and average daily live weight gain (ADWG). The present results clearly demonstrated the significant impact of the TG marker on fattening performance. It was highly significantly associated with the FW, HCW, CCW, and TWG. The SCD × TG and the OLR1 × TG interactions had remarkable effects on the traits analyzed. The GACC and CCCC haplotypes of the SCD × TG and OLR1 × TG, respectively, were found to be powerful markers for fattening performance in Simmentals. Novel associations in this study may be useful for further genetic evaluations to improve beef cattle breeding. [ABSTRACT FROM AUTHOR]

Genetic pleiotropy refers to the simultaneous association of a gene with multiple phenotypes. It is widely distributed in the whole genome and can help to understand the common genetic mechanism of diseases or traits. In this study, a multivariate response best-subset selection (MRBSS) model based pleiotropic association analysis method is proposed. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables while the multiple phenotypic data as predictor variables. Moreover, the response best-subset selection procedure is converted into an 0-1 integer optimization problem by introducing a separation parameter and a tuning parameter. Furthermore, the model parameters are estimated by using the curve search under the modified Bayesian information criterion. Simulation experiments show that the proposed method MRBSS remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield traits and pig lipid traits further verifies the effectiveness. [ABSTRACT FROM AUTHOR]

Background: Maize has great importance in South Sudan as the most cultivated cereal after sorghum; however, numerous challenges are encountered in its production. To raise maize production, it is critical to exploit the wealth of its genetic variation for grain yield enhancement. Objective: This study aimed to conduct association analysis to identify specific simple sequence repeat (SSR) markers associated with quantitative agronomic traits. Methods: Genetic variation and population structure were investigated among 31 maize accessions by association analysis using 50 SSR markers and seven quantitative agronomic traits. Results: The genotypes exhibited abundant genetic variation, and 418 alleles were detected with an average of 8.4 alleles per locus. The average genetic diversity, major allele frequency, and polymorphic information content were 0.754, 0.373, and 0.725, respectively. The population structure based on 50 SSR markers divided the maize accessions into two main groups and an admixed group without considering their descent. Association analysis was performed using a general linear model (Q GLM) and a mixed linear model (Q + K MLM). Q GLM detected 44 trait-marker associations involving 23 SSR markers. Q + K MLM detected four marker-trait associations involving three SSR markers (umc2286, umc1303, umc1429) associated with days to tasseling, days to silking, leaf length, and leaf width. Conclusions: The detected significant SSR markers related to agronomic traits could be useful for future genetic studies. Additionally, markers affecting several agronomic traits and overlapped SSR markers require further testing on a wide range of genotypes prior to their consideration as candidate markers for marker assisted selection for South Sudan maize improvement. [ABSTRACT FROM AUTHOR]

The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data provide an unprecedented opportunity for researchers to conduct studies to detect rare variants associated with the disease. However, none of the current existing statistical methods has uniform power in all scenarios because they are more or less affected by nonfunctional variants and variants with opposite effects. Here, we present a robust approach to identify rare variants using weighted entropy theory. Here, this approach takes the proportion of the minor allele among all k variants as its probability distribution, which reduces the noise incurred by noncausal variants, and uses a weight to strike a balance between deleterious rare variants and protective rare variants, which makes our method impacted less by variants with opposite effect. Through simulation studies, we investigate the performance of our method for rare variant association analyses as well as for common variant association analyses and compared it with Burden test and the SKAT. Simulation studies show that the proposed method is valid and affected slightly by noncausal variants and opposite effect variants with high and stable power for various parameters set. [ABSTRACT FROM AUTHOR]

Isocitrate dehydrogenase (IDH) is a key rate-limiting enzyme in the tricarboxylic acid cycle and acts in glutamine synthesis. IDH also participates in plant growth and development and in response to abiotic stresses. We identified 11 maize IDH genes (ZmIDH) and classified these genes into ZmNAD-IDH and ZmNADP-IDH groups based on their different coenzymes (NAD+ or NADP+). The ZmNAD-IDH group was further divided into two subgroups according to their catalytic and non-catalytic subunits, as in Arabidopsis. The ZmIDHs significantly differed in physicochemical properties, gene structure, conserved motifs, and protein tertiary structure. Promoter prediction analysis revealed that the promoters of these ZmIDHs contain cis-acting elements associated with light response, abscisic acid, phytohormones, and abiotic stresses. ZmIDH is predicted to interact with proteins involved in development and stress resistance. Expression analysis of public data revealed that most ZmIDHs are specifically expressed in anthers. Different types of ZmIDHs responded to abiotic stresses with different expression patterns, but all exhibited responses to abiotic stresses to some extent. In addition, analysis of the public sequence from transcription data in an association panel suggested that natural variation in ZmIDH1.4 will be associated with drought tolerance in maize. These results suggested that ZmIDHs respond differently and/or redundantly to abiotic stresses during plant growth and development, and this analysis provides a foundation to understand how ZmIDHs respond to drought stress in maize. [ABSTRACT FROM AUTHOR]