Association Analysis Method Research Articles

A multi-trait approach identified 7 novel genes for back pain.

Back pain (BP) is a complex heritable trait with an estimated heritability of 40% to 60%. Less than half of this can be explained by known genetic variants identified in genome-wide association studies. We applied a powerful multi-trait and gene-based approach to association analysis of BP to identify novel genes associated with BP. Using phenotypes and imputed genotypes from the UK Biobank 500k dataset, we generated a multi-trait phenotype by combining 3 BP-related phenotypes: chronic BP, dorsalgia, and intervertebral disk disorders. We performed gene-based association analysis for 3 BP-related phenotypes and multi-trait phenotype. Conditional analysis was applied to account for the effects of genetic variants outside the gene. Finally, we replicated significantly associated genes using the FinnGen database. We identified 32 genes associated with BP and replicated 16 of them. Thirteen genes were detected using the multi-trait phenotype. Seven of the detected genes, MIPOL1, PTPRC, RHOA, MAML3, JADE2, MLLT10, and RERG, were not previously reported. Several new genes are known to be associated with traits genetically correlated with BP or to be involved in pathways associated with BP. Using new powerful methods of association analysis, we identified 7 novel genes associated with BP. Our results provide new insights into the genetics of back pain.

Distribution-on-Scalar Single-Index Quantile Regression Model for Handling Tumor Heterogeneity

This article develops a distribution-on-scalar single-index quantile regression modeling framework to investigate the relationship between cancer imaging responses and scalar covariates of interest while tackling tumor heterogeneity. Conventional association analysis methods typically assume that the imaging responses are well-aligned after some preprocessing steps. However, this assumption is often violated in practice due to imaging heterogeneity. Although some distribution-based approaches are developed to deal with this heterogeneity, major challenges have been posted due to the nonlinear subspace formed by the distributional responses, the unknown nonlinear association structure, and the lack of statistical inference. Our method can successfully address all the challenges. We establish both estimation and inference procedures for the unknown functions in our model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed method is assessed by using both Monte Carlo simulations and a real data example on brain cancer images from TCIA-GBM collection.

First report of Alternaria alternata causing winged leaf dieback disease on Rhus chinensis in Yunnan Province, China.

Rhus chinensis, a deciduous tree of the genus Rhus (family Anacardiaceae), is widely cultivated in China for its medicinal, edible, and ornamental value (Zhang et al., 2022). In April 2022, symptoms of winged leaf dieback disease were observed at Southwest Forestry University in Kunming, Yunnan Province, China (E102°45'42.122″, N25°4'1.36″, H1951m). A plantation survey revealed that 82% of R. chinensis individuals had symptoms consistent with winged leaf dieback disease, representing the disease severity across the district. The early symptoms of the disease included small, irregular dark brown or black spots on the leaves, which progressively expanded into necrotic patches, leading to the withering and shedding of adjacent leaves. A total of 25 samples were collected from different R. chinensis individuals and winged leaves in the plantation at Southwest Forestry University, Kunming, with sampling conducted in April 2022. Fifteen fungal isolates with similar morphological characteristics were purified using the tissue isolation method described by Li et al (Li et al., 2019). Initially, the fungal colonies appeared white, later turned grey, and eventually became greyish-black. The conidia were light brown to brown, ovate to oblong-ellipsoid, and slightly constricted near some septa. They measured 12.4 to 41.8 µm in length and 6.1 to 14.2 µm in width, containing 1 to 7 transverse septa and 0 to 3 longitudinal septa. The conidiophores were straight or slightly curved, light brown to brown, and measured 13 to 30 by 1.5 to 6 µm. These morphological characteristics were consistent with descriptions of A. alternata (Zhang et al., 2023). For molecular identification, four representative fungal strains (RCM1, RCM2, RCM3, and RCM4) were selected. Multilocus analysis was conducted on five genes: internal transcribed spacer (ITS), 18S ribosomal RNA (SSU), 28S ribosomal RNA (LSU), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and translation elongation factor 1-alpha (EF1-α). The gene sequences were subjected to a BLAST search in NCBI, and the corresponding sequences were downloaded from the GenBank database. A phylogenetic tree was then constructed using MEGA11, employing the Maximum Likelihood method for multi-gene association analysis. The resulting sequences were deposited in GenBank with accession numbers: ITS (OR649186, OR649187, OR649188, OR649189), GAPDH (OR727018, OR727019, OR727020, OR723970), SSU (OR649193, OR649194, OR649195, OR649196), LSU (OR649198, OR649199, OR649200, OR649201), and EF1-α (OR683440, OR683441, OR727016, OR727017). Phylogenetic analysis grouped these isolates within the A. alternata clade, supported by a 99% bootstrap value. To verify Koch's postulates, a spore suspension of the pathogen (1.2×105 spores/ml) and distilled water were separately inoculated onto 20 three-year-old potted R. chinensis winged leaves, using the RCM1 isolate. The inoculated winged leaves were covered with cotton to maintain moisture. After 10 days, lesions consistent with those observed in the field developed at all inoculated sites, whereas the control group, inoculated with sterile water, remained symptom-free. The pathogen was successfully reisolated from the diseased winged leaves, fulfilling Koch's postulates. This study confirmed A. alternata as the causative agent of winged leaf dieback disease in R. chinensis. To the best of our knowledge, this is the first report in China identifying A. alternata as responsible for this disease. R. chinensis is an economically important forest species in China, and this disease significantly reduces its yield, leading to substantial economic losses for plantation companies and farmers. These findings provide a theoretical foundation for developing effective strategies to prevent and control winged leaf dieback disease in R. chinensis.

Aligning business with Sustainable Development Goals: in search of an optimal business model

The objective of this paper is to demonstrate the existence of a universal business model supporting the achievement of sustainable development goals, regardless of industry and company origin. This is a novel approach, since most authors concentrate on seeking purely innovative models or projects operating within a single industry. A hypothetico-deductive method of theory buildingwas applied. First, the literature review was conducted to determine the nature of the proposed model, and second, the empirical research and the analysis of acquired material were performed using the association analysis method. It was proved that a universal business model supporting the achievement of sustainable development goals exists and has a relational nature. Its specificcharacteristics were presented, indicating the functioning of relational champions – companies that have implemented it most effectively.

Pengaruh Likuiditas, Solvabilitas, Profitabilitas Terhadap Opini Audit : Studi Empiris Pada Laporan Keuangan Tahun 2019-2023 Perusahaan Perbankan Yang Terdaftar Di BEI

Audit opinion is where the auditor analyzes financial ratios such as LDR, CR, CAR, DER, NIM, ROA and ROE to assess the financial health of a company. High CR and ROA reflect profitability and provide a positive signal to investors. On the other hand, a balanced LDR helps maintain liquidity. A high CAR reflects the financial stability of a company, while a high DER indicates high debt which can reduce investor interest. A good NIM will increase a company's profitability and contribute to increasing ROE. The aim of this research is to understand the impact of liquidity, solvency and profitability on audit opinions, as well as to provide the impact of LDR, CR, CAR, DER, NIM, ROA and ROE. The sample in this study used samples from eight state-owned banking companies listed on the Indonesia Stock Exchange between 2019 and 2023. The total sample was 20 people, the technique used was purposive sampling. In this research, the analysis uses the associative analysis method, namely advanced logistic regression. The results of this research show that CR, DER, ROA and company size have a significant influence on audit opinion, but revenue growth does not have a significant influence.

Incremental spatiotemporal flow colocation quotient: a new spatiotemporal association analysis method for geographical flows

Analyzing spatiotemporal associations between different types of geographical flows across multiple scales is crucial for understanding the dynamic relationships between them. However, few spatiotemporal association analysis methods exist for geographical flows. Moreover, this analysis also faces the challenge that patterns at larger scales are biased by the cumulative effects from smaller scales if a series of spatial and temporal thresholds at multiple scales is used. Furthermore, spurious results arise if the joint population distribution pattern is not considered. To address these problems, this paper proposes an incremental spatiotemporal flow colocation quotient (ISTFCLQ), which aims to detect spatiotemporal associations between two types of flows across multiple spatial and temporal distance intervals by considering the joint population distribution pattern. The ISTFCLQ designs both global and local indicators for measuring the overall spatiotemporal association patterns of flows and their local association patterns, respectively. Synthetic data tests verified that the ISTFCLQ can effectively reduce cumulative effects and remove biases from the joint population distribution pattern, identifying the exact scale of association patterns. A case study of Xiamen Island taxi and ride-hailing trip data demonstrated the applicability of the ISTFCLQ in analyzing spatiotemporal competition patterns between taxi and ride-hailing services.

Understanding of ‘person-centred care’ in an oncology ICU: Associative group analysis

To describe how healthcare professionals, patients, and their significant others understand the concept of 'person-centred care' in an oncology ICU. This study followed the associative group analysis (AGA) method, a quali-quantitative research approach.The population included healthcare professionals, their patients, and significant others in a four-bed oncology adult intensive care unit. Whole population sampling (n=22) allowed all healthcare professionals to participate. Maximum variation purposive sampling was used to identify patients and their significant others (n=22). Data were collected during either face-to-face or telephonic individual interviews. Free associations were weighted using a validated weighting system. Words with similar meanings were then grouped into themes. The themes were then deductively grouped according to the domains of the Person-centred Practice Framework. Participants had a limited understanding of person-centred care and could only identify six of the 23 constructs of the Person-centred Practice Framework. Healthcare professionals embraced the idea of person-centred care, but their understanding of the concept remains vague. Person-centred care remains conceptual in this oncology intensive care unit because the interpretation and operationalisation of the concept are misaligned at various health service levels. Organisations should invest in work-based learning to enable staff to understand the concept of person-centred care. Healthcare workers also need to self-evaluate how they work and be able to adjust their working style to be more person-centred.

Genome-Wide Association Studies of Hair Whorl in Pigs.

In pigs, a hair whorl refers to hairs that form a ring of growth around the direction of the hair follicle at the dorsal hip. In China, a hair whorl is considered a negative trait that affects marketing, and no studies have been conducted to demonstrate whether hair whorl affects pig performance and provide an explanation for its genetic basis. Performance-measured traits and slaughter-measured traits of hair whorl and non-hair whorl pigs were differentially analyzed, followed by genome-wide association analysis (GWAS) and copy number variation (CNV) methods to investigate the genetic basis of hair whorl in pigs. Differential analysis of 2625 pigs (171 hair whorl and 2454 non-hair whorl) for performance measures showed that hair whorl and non-hair whorl pigs differed significantly (p < 0.05) in traits such as live births, total litter size, and healthy litter size (p < 0.05), while differential analysis of carcass and meat quality traits showed a significant difference only in the 45 min pH (p = 0.0265). GWAS identified 4 SNP loci significantly associated with the hair whorl trait, 2 of which reached genome-significant levels, and 23 candidate genes were obtained by annotation with the Ensembl database. KEGG and GO enrichment analyses showed that these genes were mainly enriched in the ErbB signaling, endothelial apoptosis regulation, and cell proliferation pathways. In addition, CNV analysis identified 652 differential genes between hair whorl and non-hair whorl pigs, which were mainly involved in the signal transduction, transcription factor activity, and nuclear and cytoplasmic-related pathways. The candidate genes and copy number variation differences identified in this study provide a new theoretical basis for pig breeding efforts.

The Spatial Patterns and Building Policies of Rural Settlements in the Context of Demolition: The Case of Xian’an, China

In China, the “land-restructuring” policy provides balanced land for urban settlements that is strictly limited in expansion. Therefore, reassessing and adjusting the layout of rural settlements is of great practical significance for promoting rural revitalization. In this paper, taking Xian’an district in Hubei Province as an example, we use the weighted rank-sum ratio comprehensive evaluation method and spatial association analysis method to analyze the development level and spatial pattern of settlements. The results show that: (1) The development level of settlements in Xian’an shows obvious spatial differences, with a spatial pattern of ‘high in the core–low in the periphery’ and ‘high in the northwest–low in the southeast’, which is the result of the combined effect of natural geographical conditions and socioeconomic conditions; (2) The comprehensive development level of settlements, evaluated based on four major indicators—population size, resource endowment, spatial characteristics, and material construction—reveals the presence of cluster effects, distance decay effects, administrative hierarchy effects, and “long board” effects; (3) Within village communities, settlements with significantly high levels and settlements with significantly low levels have a similar geographic distribution and mosaic spatial patterns. Lastly, based on the overall development level and spatial association patterns of settlements, this article presents possible options for governmental settlement governance from the standpoint of rural building management.

Influence of public health emergency on city image: the case of COVID-19 stigma on Wuhan city

This study explored the influence of COVID-19 stigma on the city image of Wuhan by adopting the associative group analysis method. The collected data were free associations of the word “Wuhan” from 39 non-Chinese participants residing outside of China. It found that there were 12 meaning categories related to the city image of Wuhan, half of which were related to COVID-19, and the other half were related to Wuhan city. 56.45% of the associated words were related to COVID-19 and 43.55% to Wuhan city. The results demonstrated that the city image of Wuhan was impacted by the COVID-19 pandemic, mediated by personal experience and perceived media reports. The associated words have constructed a “Wuhan-COVID-19” stereotype, including “Wuhan-birthplace of COVID” and “Wuhan-eating wild animals”. This reflected that the city image of Wuhan endured stigma in the global community. The study provided implications for eliminating stigma and reconstructing group relationships in the global community.

Investigation on tastes, properties, and traditional efficacy of Mongolian medicines of genus Artemisia based on pharmacophylogeny

The genus Artemisia is one of the most abundant groups of medicinal plants in the Asteraceae family. Based on the theoretical basis of the pharmacophylogeny, the phylogenetic analysis, association analysis and other methods were used to systematically sort out the genetic relationships, properties, tastes, chemical constituents, and traditional efficacy of Mongolian medicines of genus Artemisia(MMA). The correlation and regularity were discussed. The results showed that MMA could be divided into two main groups according to phylogenetic analysis. The first group was Sect. Artemisia, which was based on bitter taste, and most of them were pungent. The proportion of warm drugs was 85.71%. The second group was the non-Artemisia group, including the medicinal plants of Sect. Absinthium, Sect. Abrotanum, Sect. Dracunculus, and Sect. Latilobus. In this group, the taste was mainly bitter, and the cold drugs accounted for 80%. The analysis of chemical constituents and medicinal tastes showed that MMA were rich in volatile oils, flavonoids, terpenoids, phenylpropanoids, and other components. Among them, the volatile oils were important sources of the unique smell of MMA. The analysis of medicinal properties and traditional efficacy showed that under the set analysis parameters, a total of 8 association rules were obtained. The association rule between cold MMA with cold and fever, the association rule between cool MMA with respiratory diseases, and the association rule between warm MMA with gynecological diseases had the highest lift value in their respective medicinal property groups. This study provides an important academic reference for establishing a new relationship between pharmacophylogeny with the tastes, properties, and traditional efficacy of medicinal materials.

A Method of Multiple Targets and Sensors Track Association Analysis

In order to reduce the probability of leakage and improve detection accuracy, multiple radar equipment is required to perform multiple detections on the same airspace. Due to the overlap of airspace, there are multiple local tracks reported by various radar equipment that belong to the same target. The local tracks reported by each radar require track fusion to form a system track, and track association is a prerequisite for track fusion. This paper proposes a multi-target and multi-sensor track association analysis method, which can distinguish whether local tracks come from the same target and associate local tracks with the same target, providing conditions for track fusion to form system tracks. This method divides track association into two steps: Coarse association and fine association, which greatly improves the accuracy of track association.

PENGARUH KUALITAS PRODUK, HARGA, DAN PROMOSI TERHADAP KEPUTUSAN PEMBELIAN SEPATU VENTELA PADA MAHASISWA/I FAKULTAS EKONOMI UNIVERSITAS YOS SOEDARSO

Abstract. This research was conducted with the aim of finding out how much influence product quality, price and promotion have on purchasing decisions for Ventelapada shoes for students at the Faculty of Economics, Yos Soedarso University. The research method used is the associative analysis method, and uses the multiple regression analysis method. The data used in this research are primary and secondary data. The sample in this study was 97 consumers with a sampling technique using accidental sampling. The results of this research show that partially product quality, price and promotion have a positive and significant effect on purchasing decisions for Ventela shoes among students at the Faculty of Economics, Yos Soedarso University.

THE EFFECT OF ELECTRONIC WORD OF MOUTH AND PRICE ON PURCHASING DECISIONS WITH TRUST AS AN INTERVENING VARIABLE IN SHOPEE E-COMMERCE

This study aims to analyze the factors that influence purchasing decisions for Scarlett Whitening products in Shopee e-commerce, where the factors for making product purchasing decisions are Electronic Word Of Mouth, Price, and Trust.The data type is primary data, obtained from distributing questionnaires to consumers who have purchased Scarlett Whitening products in Shopee e-commerce, using non-probability purposive sampling so that the total sample is 155 respondents. This study uses the Causal Associative Analysis method with a qualitative approach and data analysis techniques using the Structural Equation Modeling (SEM) AMOS version 26.0 program.The results showed that electronic word of mouth has a significant effect on trust, price has a significant effect on trust, trust has no significant effect on purchasing decisions, electronic word of mouth has a significant effect on purchasing decisions, and price has a significant effect on purchasing decisions.

Data mining techniques on astronomical spectra data – III. Association analysis

ABSTRACT Association analysis is an important task that aims to investigate correlations in astronomical spectral data and mine relationships between different data features. With the rapid development of various sky survey projects, multiple association analysis methods have been applied to efficiently investigate the correlation between spectral data. However, due to the different focuses of analysing the relationships within spectral data, the performance and applicability scenarios of association analysis methods vary. We present the third article in the series to provide a comprehensive review of algorithms for astronomical spectral association analysis. First, this paper outlines the ideas and applications of association analysis algorithms for astronomical spectra in the current literature. Secondly, experiments are conducted on a unified A-type stellar spectral data set constructed based on three different signal-to-noise ratios and data volumes to examine the performance of different algorithms in analysing the correlation between data features. The results indicate that association rule algorithms can more comprehensively and effectively uncover the correlations among different spectral features, while regression analysis algorithms offer a simpler and more intuitive approach to analysing relationships between features. The spectral data used in the experiments are obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. Finally, the source code of association analysis algorithms and manuals for usage are provided on GitHub.

Analysis Of Purchasing Decisions In Terms Of Product Quality, Price, And Brand Ambassador In E-Commerce

This study aimed to examine the elements that affect Scarlett Whitening customers' e-commerce purchase decisions. The factors studied are product quality, price, and brand ambassador. The critical data used in this study were collected directly from respondents by sending research questionnaires to Scarlett Whitening customers shopping online in Jabodetabek. In contrast, secondary data in this study came from other statistical data and websites. Non-probability purposive sampling was used to acquire the data, and there were 110 respondents. With the aid of SPSS software version 25, this kind of research uses an associative analysis method with a quantitative approach. This study discovered that Scarlett Whitening's consumer purchase decisions in e-commerce are positively and significantly impacted by product quality, price, and brand ambassador.

Gene association analysis to determine the causal relationship between immune cells and juvenile idiopathic arthritis

BackgroundJuvenile idiopathic arthritis (JIA) is a type of chronic childhood arthritis with complex pathogenesis. Immunological studies have shown that JIA is an acquired self-inflammatory disease, involving a variety of immune cells, and it is also affected by genetic and environmental susceptibility. However, the precise causative relationship between the phenotype of immune cells and JIA remains unclear to date. The objective of our study is to approach this inquiry from a genetic perspective, employing a method of genetic association analysis to ascertain the causal relationship between immune phenotypes and the onset of JIA.MethodsIn this study, a two-sample Mendelian randomization (MR) analysis was used to select single nucleotide polymorphisms (SNPs) significantly associated with immune cells as instrumental variables to analyze the bidirectional causal relationship between 731 immune cells and JIA. There were four types of immune features (median fluorescence intensity (MFI), relative cellular (RC), absolute cellular (AC), and morphological parameters (MP)). Finally, the heterogeneity and horizontal reproducibility of the results were verified by sensitivity analysis, which ensured more robust results.ResultsWe found that CD3 on CM CD8br was causally associated with JIA at the level of 0.05 significant difference (95% CI = 0.630 ~ 0.847, P = 3.33 × 10−5, PFDR = 0.024). At the significance level of 0.20, two immunophenotypes were causally associated with JIA, namely: HLA DR on CD14+ CD16- monocyte (95% CI = 0.633 ~ 0.884, P = 6.83 × 10–4, PFDR = 0.16) and HLA DR on CD14+ monocyte (95% CI = 0.627 ~ 0.882, P = 6.9 × 10−4, PFDR = 0.16).ConclusionOur study assessed the causal effect of immune cells on JIA from a genetic perspective. These findings emphasize the complex and important role of immune cells in the pathogenesis of JIA and lay a foundation for further study of the pathogenesis of JIA.

Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection

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.

Study of the Genetic Adaptation Mechanisms of Siberian Larch (Larix sibirica Ledeb.) Regarding Climatic Stresses Based on Dendrogenomic Analysis

Dendrogenomics is a new interdisciplinary approach that allows joint analysis of dendrological and genomic data and opens up new ways to study the temporal dynamics of forest treelines, delineate spatial and temporal population structures, decipher individual tree responses to abiotic and biotic stresses, and evaluate the adaptive genetic potential of forest tree populations. These data are needed for the prediction of climate change effects and mitigation of the negative effects. We present here an association analysis of the variation of 27 individual tree traits, including adaptive dendrophenotypes reflecting the individual responses of trees to drought stress, such as the resistance (Rt), recovery (Rc), resilience (Rs), and relative resilience (RRs) indexes measured in 136 Siberian larch trees in 5 populations in the foothills of the Batenevsky Ridge (Kuznetsk Alatau, Republic of Khakassia, Russia), with variation of 9742 SNPs genotyped using ddRADseq in the same trees. The population structure of five closely located Siberian larch populations was relatively weak (FST = 0.018). We found that the level of individual heterozygosity positively correlated with the Rc and RR indices for the five studied drought periods and partly with the Rs indices for three drought periods. It seems that higher individual heterozygosity improves the adaptive capabilities of the tree. We also discovered a significant negative relationship between individual heterozygosity and the Rt index in four out of five periods, which means that growth slows down during droughts more in trees with higher individual heterozygosity and is likely associated with energy and internal resource reallocation toward more efficient water and energy usage and optimization of larch growth during drought years. We found 371 SNPs with potentially adaptive variations significantly associated with the variation of adaptive dendrophenotypes based on all three different methods of association analysis. Among them, 26 SNPs were located in genomic regions carrying functional genes: 21 in intergenic regions and 5 in gene-coding regions. Based on the obtained results, it can be assumed that these populations of Siberian larch have relatively high standing adaptive genetic variation and adaptive potential underlying the adaptations of larch to various climatic conditions.

A Priori Algorithm Based Network Security Situational Awareness Multi-Source Data Correlation Analysis Method

In the context of the information age, the Internet has developed rapidly, but the accompanying network security threats have also become an issue that cannot be ignored. In order to effectively respond to these threats and improve the data processing capabilities of network security situational awareness, the study focuses on the challenges of multi-source data processing and proposes a multi-source data association analysis method based on the A priori algorithm. This method aims to deeply explore the implicit relationships between data and provide stronger support for network attack detection. In addition, the study also designed a multi-level evaluation method based on coefficient of variation indicators, aiming to provide a more objective and comprehensive evaluation of the detection results. After a series of experimental verification, the proposed correlation analysis method has achieved significant results in detecting phishing attacks and DOS attacks, with detection rates of 90.3% and 93.8%, respectively. At the same time, the multi-level evaluation method has also been experimentally proven to provide more reasonable and accurate results for data evaluation. The methods and technologies proposed in the study can not only improve the multi-source data processing ability of network security situational awareness, but also provide valuable references for future network security research and practice.