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Journal articles on the topic 'Interaction analysis'

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Published: 4 June 2021

Last updated: 9 June 2025

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Bataeva, Ekaterina V. "Аction-analysis and interaction-analysis of cybercommunication". Sociological Journal 21, № 1 (2015): 6–22. http://dx.doi.org/10.19181/socjour.2015.21.1.1247.

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Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (March 17, 2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

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The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

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Lin, C. Y., and C. S. Lin. "Investigation of genotype-environment interaction by cluster analysis in animal experiments." Canadian Journal of Animal Science 74, no. 4 (December 1, 1994): 607–12. http://dx.doi.org/10.4141/cjas94-089.

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The conventional ANOVA (F ratio of GE interaction mean squares to error mean square) provides a means to test if GE interaction is significant, but it does not tell us which factor levels are significantly different or how they are interacting. To answer the latter question, plant researchers developed a technique to group genotypes for similarity of GE interactions and through the resulting groups to explore the GE interaction structure. The basic idea of the technique is to stratify genotypes (or environments) into subgroups such that GE interactions among genotypes (or environments) are homogeneous within groups but heterogeneous among groups. This technique is introduced in this paper using an animal experiment as an example for illustration. The possibilities and limitations of applying this technique to animal data are also discussed. Key words: Genotype-environment interaction, cluster analysis

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Alqaseer, Ahlam Mohammed. "Interaction Analysis of Smoking with Liver Function Tests." International Journal Of Pharmaceutical And Bio-Medical Science 02, no. 11 (November 17, 2022): 513–17. http://dx.doi.org/10.47191/ijpbms/v2-i11-07.

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Introduction: Smoking has been linked to harmful health impacts on several body organs including the cardiovascular system. Study objectives: The primary goal of the current study is to assess how smoking models affect the level of liver enzymes. Methods: Thirty male albino rats served as the subjects for this experimental study. We used male albino rats (Rattusratas) that were 6–8 weeks old and weighed 50–180 g. Rats were randomly allocated to one of three groups (n = 10): group 1 served as a negative control and was solely exposed to fresh air; group 2 was exposed to the most popular cigarette brands available in Jordan (red LM cigarettes) at a rate of 1 cigarette per rat per day for 30 days. Rats in group 3 were given flavored water pipes for 30 days, once a day for the entire body, after completely burning 20 g of one moassal. The smoking device was digital. LDH, AST, ALT, and liver enzymes were assessed in study groups. The collected data were examined using SPSS version 20. Results: Both exposures to cigarette smoking and water pipe smoking resulted in a considerable rise in liver enzymes. Interesting results showed that quitting smoking restored liver enzymes to levels close to those of the control group. Conclusion: Increased levels of liver enzymes are a sign that smoking causes liver damage, which can be reversed by quitting.

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Bublyk, Yevhen, Olena Borzenko, and Anna Hlazova. "Cryptocurrency energy consumption: Analysis, global trends and interaction." Environmental Economics 14, no. 2 (August 18, 2023): 49–59. http://dx.doi.org/10.21511/ee.14(2).2023.04.

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The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.

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HAYASHI, YOSHIHARU, MIME KOBAYASHI, KATSUYOSHI SAKAGUCHI, NAO IWATA, MASAKI KOBAYASHI, YO KIKUCHI, and YOSHIMASA TAKAHASHI. "PROTEIN CLASSIFICATION USING COMPARATIVE MOLECULAR INTERACTION PROFILE ANALYSIS SYSTEM." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 2004): 497–510. http://dx.doi.org/10.1142/s0219720004000703.

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We recently introduced a new molecular description factor, interaction profile Factor (IPF) that is useful for evaluating molecular interactions. IPF is a data set of interaction energies calculated by the Comparative Molecular Interaction Profile Analysis system (CoMIPA). CoMIPA utilizes AutoDock 3.0 docking program, and the system has shown to be a powerful tool in clustering the interacting properties between small molecules and proteins. In this report, we describe the application of CoMIPA for protein clustering. A sample set of 15 proteins that share less than 20% homology and have no common functional motifs in primary structure were chosen. Using CoMIPA, we were able to cluster proteins that bound to the same small molecule. Other structural homology-based clustering programs such as PSI-BLAST or PFAM were unable to achieve the same classification. The results are striking because it is difficult to find any common features in the active sites of these proteins that share the same ligand. CoMIPA adds new dimensions for protein classification and has the potential to be a helpful tool in predicting and analyzing molecular interactions.

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Sainyakit, Paulus, and Yan Imam Santoso. "A Classroom Interaction Analysis of Teacher and Students by Using FIACS." Acitya: Journal of Teaching and Education 6, no. 1 (January 23, 2024): 157–67. http://dx.doi.org/10.30650/ajte.v6i1.3825.

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Classroom interaction plays an important role in the teaching and learning process. It is because it involves the interaction between teacher, student and student-student in which they influence each other in the classroom interaction. It is also said that classroom interaction gives a chance to ask, to guess, to think even to the course material in order to make interaction between the teacher and students their selves in the classroom. This research is aimed to analyze the classroom interaction by using FIACS. This research is included as a quantitative content analysis design. This research involves a teacher and the 2nd semester students of STKIP Terang Bangsa. The observation is employed in this research as the method of collecting data. After that, the data would be calculated by using a formula. The results show that the Teacher Initiated category is the highest dominant in the classroom interaction. It has 3.422 or 95,50% out of 3.583 interactions in the classroom. Then, the Student-Initiated category has 161 or 4,50% of the total. Lastly, the Student-student initiated category has no interactions. Due to the limitations of the research, the researcher suggests that future research on this topic should involve more participants in interacting not only teacher to student but student to student, so the conclusion can be drawn more valid.

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Kulharia, Mahesh. "Geometrical and electro-static determinants of protein-protein interactions." Bioinformation 17, no. 10 (October 31, 2021): 851–60. http://dx.doi.org/10.6026/97320630017851.

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Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.

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JOHNSTON, RICHARD D., and GEOFFREY W. BARTON. "Structural interaction analysis." International Journal of Control 41, no. 4 (April 1985): 1005–13. http://dx.doi.org/10.1080/0020718508961179.

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Stockley, Peter G. "Biomolecular interaction analysis." Trends in Biotechnology 14, no. 2 (February 1996): 39–41. http://dx.doi.org/10.1016/0167-7799(96)80916-4.

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Gambari, Roberto. "Biospecific Interaction Analysis." American Journal of PharmacoGenomics 1, no. 2 (2001): 119–35. http://dx.doi.org/10.2165/00129785-200101020-00005.

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Malmqvist, Magnus, and Russ Granzow. "Biomolecular Interaction Analysis." Methods 6, no. 2 (June 1994): 95–98. http://dx.doi.org/10.1006/meth.1994.1012.

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Kang, Seoktae, and Sunyoung Kim. "In visual arts using VR: An interaction analysis interaction analysis." Korean Society of Culture and Convergence 42, no. 5 (May 30, 2020): 597–620. http://dx.doi.org/10.33645/cnc.2020.05.42.5.597.

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Khan, Raja Muhammad Ishtiaq, and Tribhuwan Kumar. "Interaction Analysis of WhatsApp Application Integration in M-Learning." Webology 19, no. 1 (January 20, 2022): 795–806. http://dx.doi.org/10.14704/web/v19i1/web19056.

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Interaction analysis (IA) describes and analyzes an instructor's verbal interaction with a student (s). The use of interaction analysis has aided in the monitoring of teacher verbal behavior. WhatsApp is also extensively used in language learning, and it is the most widely used program for this function. WhatsApp Interaction was investigated using Language Related Episodes (LRE) and Interaction Analysis to utilize WhatsApp. Eight pairs of pupils were formed from the sampled students. The 16 students were all enrolled in the public university's PYP section. The investigation took place for four weeks, for three days for the class, and the sampled students collaborated in pairs via WhatsApp. Pearson's correlation method was used to demonstrate the association between two or more groups without any modification. Findings indicated that there was a negative correlation between the time spent on WhatsApp and vocabulary scores.

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KURNIASARI, CECILIA INDRI. "Social Interaction on Patients with Schizophrenia in Psychiatric Hospital." Jurnal Ilmiah Kesehatan Keperawatan 15, no. 2 (January 15, 2020): 25. http://dx.doi.org/10.26753/jikk.v15i2.335.

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Social interaction is one of important indicator in the recovery process of mental patients, especially in patients with schizophrenia. Active social interactions can help patients with schizophrenia to socialize, while less active social interactions can cause social isolation to the risk of suicide. The purpose of this study was to determine the social interaction of patients with schizophrenia in psychiatric hospital. The number of participant in this study were 52 patients. Sampling with a purposive sampling technique. Data were collected using Social Interaction Questionnaire and Behavior Observation Sheet consisting of 18 statements. The analysis of this study was using univariate analysis with table of frequency distribution. The results showed that social interactions in schizophrenia patients were 45 patients with less active interacting categories, 5 patients with moderately active interacting categories, and 2 patients with active interacting categories. The results of the study can be used as a reference in determining appropriate nursing therapy in increasing social interaction in schizophrenia patients in mental hospitalsKeywords: social interaction; social psychological factors; schizophrenia;

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Yashin, Anatoliy, Dequing Wu, Konstantin Arbeev, Eric Stallard, Qihua Tan, Alexander Kulminski, Mary Feitosa, and Svetlana Ukraintseva. "Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)." Innovation in Aging 4, Supplement_1 (December 1, 2020): 491. http://dx.doi.org/10.1093/geroni/igaa057.1589.

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Abstract Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Uncovering hidden patterns of such interactions from human data will help better understand the nature of AD heterogeneity and find new targets for AD prevention. In this paper, we applied a newly developed method of evaluating joint GxG effects on AD to analysis of the Long Life Family Study data. The analysis included several steps: (i) selecting candidate genes from stress response pathways that are thought to be involved in AD; (ii) estimating interaction effects of SNP-pairs on AD risk, and selecting the top interacting SNPs; (iii) running GWAS-like interaction analysis for SNP-pairs, with one SNP fixed; (iv) using characteristics of the detected SNP-pairs interactions to construct the SNP-specific Interaction Polygenic Risk Scores (IPRS); and (v) evaluating the effects of IPRSs on AD. We found that SNP-specific IPRS have highly significant effects on AD risk. For most SNPs involved in the significant interaction effects on AD, their individual effects were statistically not significant. Male and female analyses yielded different subsets of the top interacting SNPs. These results support major role of genetic interactions in heterogeneity of AD, and indicate that AD mechanisms can involve different combinations of the interacting genetic variants in males and females, which may point to different pathways of resistance/response to stressors in two genders.

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Dai, Fei, Hao Chen, Zhenping Qiang, Zhihong Liang, Bi Huang, and Leiguang Wang. "Automatic Analysis of Complex Interactions in Microservice Systems." Complexity 2020 (March 31, 2020): 1–12. http://dx.doi.org/10.1155/2020/2128793.

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Interactions in microservice systems are complex due to three dimensions: numerous asynchronous interactions, the diversity of asynchronous communication, and unbounded buffers. Analyzing such complex interactions is challenging. In this paper, we propose an approach for interaction analysis using model checking techniques, which is supported by the Process Analysis Toolkit (PAT) tool. First, we use Labeled Transition Systems (LTSs) to model interaction behaviors in microservice systems as sequences of send actions under synchronous and asynchronous communications. Second, we introduce a notion of correctness called “interaction soundness” which is considered as a minimal requirement for microservice systems. Third, we propose an encoding of LTSs into the CSP# process algebra for automatic verification of the property interaction soundness. The experimental results show that our approach can automatically and effectively identify interaction faults in microservice systems.

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Zhou, Xueqian, Cong Liu, Huilong Ren, and Chen Xu. "Numerical Analysis of Propeller-Induced Hydrodynamic Interaction between Ships." Journal of Marine Science and Engineering 11, no. 3 (March 1, 2023): 537. http://dx.doi.org/10.3390/jmse11030537.

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The hydrodynamic interaction effects between ships are significantly pronounced in restricted waters, and this may potentially threaten the safety of ships, especially given that ship dimensions and waterway traffic have kept increasing. Although there has been a good amount of research on ship hydrodynamic interactions, the study of the effect of the propeller on the ship’s hydrodynamic interaction is very limited. In this paper, a series of RANSE-based numerical simulations are carried out to study the characteristics of the propeller in near-field interaction between ships without speed. The hydrodynamic forces and moment acting on the ship are calculated and analyzed. Through the analysis of the characteristics of the flow field and the behavioral pattern of the hydrodynamic forces, it is found that the propeller has a significant influence on the pressure distribution on the hull as well as on the hydrodynamic interaction forces. The maximum lateral force acting on the interacting ship could reach 0.58 times the standard thrust of a KP458 propeller (the revolution is 594 rpm and the velocity coefficient is 0.25 in open water).

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Nemeth, Lea, Tim Blumenfeld, Ann-Katrin Denn, Anastasia Hirstein, and Frank Lipowsky. "An Exploratory Analysis of Transactive Interaction Patterns in Cooperative Learning Using Sequential Analysis." Education Sciences 13, no. 8 (August 3, 2023): 790. http://dx.doi.org/10.3390/educsci13080790.

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For cooperative learning to be effective, the quality of student–student interaction is crucial. Interactions, which are transactive in nature, are positively related to students’ learning success during cooperative learning. However, little is known about typical interaction patterns during transactive interaction in face-to-face cooperative learning. Therefore, the current study aims to analyze typical interaction patterns of transactive interaction in cooperative learning. Sixty-eight students from seventh to tenth grade were randomly assigned to a total of 23 groups in their classes. The groups were videotaped while solving the same open-ended mathematical modelling task. The interaction behavior was coded, and interaction patterns were analyzed using sequential analysis with first- and second-order Markov chains. The results indicate that the likelihood that students confirm and pick up correct proposals is relatively high, indicating transactive interaction. However, it is almost equally likely that incorrect proposals are confirmed erroneously, as students barely correct them. Still, students do frequently engage in transactive interaction by discussing incorrect proposals, even though these discussions rarely lead to correct solution approaches. Limitations of these results, as well as the practical implications for cooperative learning in classroom settings, are discussed.

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Le, Thu, Daniel Bolt, Eric Camburn, Peter Goff, and Karl Rohe. "Latent Factors in Student–Teacher Interaction Factor Analysis." Journal of Educational and Behavioral Statistics 42, no. 2 (January 6, 2017): 115–44. http://dx.doi.org/10.3102/1076998616676407.

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Classroom interactions between students and teachers form a two-way or dyadic network. Measurements such as days absent, test scores, student ratings, or student grades can indicate the “quality” of the interaction. Together with the underlying bipartite graph, these values create a valued student–teacher dyadic interaction network. To study the broad structure of these values, we propose using interaction factor analysis (IFA), a recently developed statistical technique that can be used to investigate the hidden factors underlying the quality of student–teacher interactions. Our empirical study indicates there are latent teacher (i.e., teaching style) and student (i.e., preference for teaching style) types that influence the quality of interactions. Students and teachers of the same type tend to have more positive interactions, and those of differing types tend to have more negative interactions. IFA has the advantage of traditional factor analysis in that the types are not presupposed; instead, the types are identified by IFA and can be interpreted in post hoc analysis. Whereas traditional factor analysis requires one to observe all interactions, IFA performs well even when only a small fraction of potential interactions are actually observed.

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Sun, Zheng, Shihao Li, Fuhua Li, and Jianhai Xiang. "Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/416543.

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WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1) and the constructed transcriptome data ofF. chinensiswere used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP) encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA), two integrin beta (ITGB), and one syndecan (SDC). Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

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Gustafson, Karl. "Interaction antieigenvalues." Journal of Mathematical Analysis and Applications 299, no. 1 (November 2004): 174–85. http://dx.doi.org/10.1016/j.jmaa.2004.06.012.

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Müller-Frommeyer, Lena C., and Simone Kauffeld. "Gaining insights into organizational communication dynamics through the analysis of implicit and explicit communication." Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO) 52, no. 1 (January 26, 2021): 173–83. http://dx.doi.org/10.1007/s11612-021-00559-9.

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AbstractThis report in the journal Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie aims at presenting how the analysis of implicit and explicit communication in organizational interaction can advance our insights into and implications for these interactions for research and science. Communication is a central process in modern organizations. Especially recurring forms of interaction in organizations (e.g., meetings or appraisal interviews) are of great importance for personal and organizational success. In these interactions, the communication between the interacting organizational members has a decisive impact on the interactions’ course and outcomes (e.g., satisfaction with the interaction, performance during the interaction). Therefore, the aim of this paper is to present two aspects of communication that are empirically shown to contribute to successful outcomes of organizational interactions. Based on a practical problem, we illustrate the analysis and implications of (1) implicit communication (that is, the use and coordination of unconsciously used function words such as pronouns, articles, or prepositions) and (2) explicit communication (that is, the overarching meaning of a statement). To further illustrate the practical relevance of both communication behaviors, we present empirical insights and their implications for practice. Taking a glance at the future, possible combinations of these communication behaviors, the resulting avenues for future research, and the importance of a strengthened cooperation between research and practice to gain more naturalistic insights into organizational communication dynamics are discussed.

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Batool, Zakia, Waqas Munir, Mohsin Abbas, Umar Waqs Khan, and Mahwish Rabia. "Bayesian approach: an alternative to the additive main effect and multiplicative interaction models for genotypes through environmental interactions." Nativa 12, no. 4 (December 31, 2024): 597–604. https://doi.org/10.31413/nat.v12i4.17484.

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Genotypes of different genetic structures behave differently in various environmental conditions. Genotype-by-environment interaction (GEI) is referred to as differential responses of different genotypes across different environments; GEI is of great importance because of the higher performance of genotypes that GEI assesses. However, the presence of GEI makes analysis more complicated. To up-root these assessment complications, several methods have been proposed, such as Principal Component Analysis (PCA), Cluster Analysis, Additive Main effects, Multiplicative Interaction (AMMI) models, and Genotype plus Genotype by Environment interaction (GGE). These methods neither overcome the problem of overparameterization nor use the prior information. This study aims to use a technique to address these problems; for this purpose, wheat crop data comprised of 30 genotypes tested across 13 different locations of Punjab, Pakistan, for two consecutive years was used. The layout of the experiment was a Randomized Complete Block Design (RCBD). In this study, a comparison was made between Classical methods AMMI, GGE biplot, and Bayesian approach using Von-Mises Fisher distribution as prior. Classical methods showed that genotype V-11098 was the most desirable based on stability and high-yield performance. The Bayesian approach was used for GEI because it simplifies statistical interpretation by relaxing some constraints. It uses the prior information and provides solutions using the Markov Chain Monte Carlo (MCMC) algorithm. Bayesian strategy for analysis of GEI was used to assess the general, specific performance of genotypes and risk related to genotype. Analysis revealed that bi-linear terms 𝜇ଶହ,ଵ for genotype NS-10 genotype and 𝜈ଵଷ,ଵ for environment S13 (Piplan-14) were found significant, indicating that these affect interaction. It was observed that the Bayesian approach could nicely explore GE interaction.

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Huang, Tianchun. "Analysis of the Irreplaceability of Offline Relationship." Lecture Notes in Education Psychology and Public Media 4, no. 1 (May 17, 2023): 1067–73. http://dx.doi.org/10.54254/2753-7048/4/2022797.

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During covid 19, traditional offline social interaction becomes difficult. Benefiting from the availability of social media, people are increasingly relying on social media to socialize. Can social media interaction replace real-life interaction? Is a social media-based relationship a sufficient substitution for a real relationship? This article divides social media interaction into reciprocal and non-reciprocal and compares two different social media interactions with real-life interactions. For the reciprocal social media interaction, The first research question distinguishes computer-mediated communication (CMC) from face-to-face (FtF) and discusses the possibility of CMC replacing FtF. Because the absence of social cues cannot be made up, CMC is not a sufficient substitute for FtF. For the non-reciprocal social media interaction, the second research question focuses on parasocial interaction-induced parasocial relationships. Because of non-reciprocality and lack of authenticity, the parasocial relationship is not a good substitute for a real relationship. In conclusion, social media relationship is not a sufficient substitute for a real relationship, but they can be used as a good supplement to a real relationship. The difference between reciprocal social media relationships and non-reciprocal social media relationships is also discussed. Reciprocal social media relationships and real relationships are interchangeable, while non-reciprocal social media relationships cannot transform into real relationships.

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Nedelkov, Dobrin, and Randall W. Nelson. "Delineating protein-protein interactions via biomolecular interaction analysis-mass spectrometry." Journal of Molecular Recognition 16, no. 1 (January 2003): 9–14. http://dx.doi.org/10.1002/jmr.600.

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Ryu, Hokyoung, and Andrew Monk. "Interaction Unit Analysis: A New Interaction Design Framework." Human-Computer Interaction 24, no. 4 (October 2009): 367–407. http://dx.doi.org/10.1080/07370020903038086.

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P, Krishnapriya. "Analysis of Pathogen-Immune Interaction Dynamics with Three Time delays." Journal of Computational Mathematica 1, no. 2 (December 30, 2017): 1–17. http://dx.doi.org/10.26524/cm11.

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Lin, H. J., M. S. Lin, P. S. Ruan, S. W. Chen, J. D. Lee, and J. R. Wang. "Transient Pressure Analysis and Air-water Interaction in Churn Flow." International Journal of Materials, Mechanics and Manufacturing 6, no. 6 (December 2018): 397–401. http://dx.doi.org/10.18178/ijmmm.2018.6.6.415.

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Kolaki, Aravind I., and Basavaraj M. Gudadappanavar. "Performance Based Analysis of Framed Structure Considering Soil Structure Interaction." Bonfring International Journal of Man Machine Interface 4, Special Issue (July 30, 2016): 106–11. http://dx.doi.org/10.9756/bijmmi.8165.

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Zhang, Xizhe, Sergio Branciamore, Grigoriy Gogoshin, Andrei S. Rodin, and Arthur D. Riggs. "Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling." Proceedings of the National Academy of Sciences 114, no. 48 (November 13, 2017): E10359—E10368. http://dx.doi.org/10.1073/pnas.1620425114.

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Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)–cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF–cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA–DNA, protein–DNA, and protein–protein interactions.

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Kagan, Dona M., and Donald J. Grandgenett. "Personality and interaction analysis." Research in Education 37, no. 1 (May 1987): 13–24. http://dx.doi.org/10.1177/003452378703700102.

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Newman, Sally, Gregory A. Morris, and Heidi Streetman. "Elder-Child Interaction Analysis." Child & Youth Services 20, no. 1-2 (October 18, 1999): 129–45. http://dx.doi.org/10.1300/j024v20n01_10.

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Wu, Mengyun, and Shuangge Ma. "Robust genetic interaction analysis." Briefings in Bioinformatics 20, no. 2 (April 20, 2018): 624–37. http://dx.doi.org/10.1093/bib/bby033.

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Sugai, George, and Timothy Lewis. "Teacher/Student Interaction Analysis." Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children 12, no. 4 (October 1989): 131–38. http://dx.doi.org/10.1177/088840648901200401.

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Klein, Ágnes, and Tünde Tancz. "Interaction Analysis in Nurseries." Acta Educationis Generalis 12, no. 3 (November 1, 2022): 135–46. http://dx.doi.org/10.2478/atd-2022-0028.

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Abstract Introduction: The present research aimed to review the qualitative aspects of communication between children and their caregivers. We focused on the presence of quality indicators, on the strategies we encounter in influencing language acquisition in the interaction between children and early childhood educators. Methods: We examined the diversity, awareness and efficiency with which educators use communication tools and techniques in various preschool education situations for children under 3 years of age. In the empirical survey of day-care interactions a questionnaire was based on the evaluation and observation of questionnaire responses. Results: The emotional and motivational basis of language acquisition is formed by the toddler’s social inclinations and attachment needs, as well as social inclinations, with his environment playing a prominent role in changing these processes. Discussion: These interactions provide a framework for language acquisition, where in the classic case language acquisition is not guided, but takes place through everyday situations, through participation in authentic communication situations. The axiom is that language skills develop in language use. Limitations: The questionnaire was validly completed by 60 people. The data collection concentrated on the region of southern and northern Transdanubia in Hungary. Although not in national terms, this offers the opportunity to evaluate and reflect on the situation at the regional level. Conclusions: The professional communication and competent language development activity of educators’ results from the interplay of scientific-theoretical knowledge, implicit empirical knowledge, competence-oriented procedures

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Rizkiyah, Faiqotur, and Umi Salamah. "Analysis of Classroom Verbal Interaction Using Fiacs (Flanders Interaction Analysis Categories System)." Darussalam English Journal (DEJ) 3, no. 1 (June 15, 2023): 31–66. http://dx.doi.org/10.30739/dej.v3i1.2234.

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One of the primary factors that influence the teaching and learning process is interaction. Verbal interaction is the most interaction happened between teacher and students in the classroom. The quality of verbal interaction affect the result of teaching and learning itself. Thus, this study aimed to find out teacher-students talk characteristic during classroom interaction using FIACS and to find out the dominant talk between teacher and students in classroom interaction. This study was qualitative research. To achieve the purposes of this study the writer used observation which was adopted by Flander’s Interaction Analysis Category to find out verbal interaction between teacher and student. The researcher also use interview and recording for get the data. The study found that all teachers used all FIAC categories and the dominance talk was the teacher’s talk. The teacher speaks about 77 per cent in the classroom while students 13 per cent. From the result, it is suggested that teacher should give more opportunities to the students to speak and interact during tteaching and learning process

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Shruthi.K.V. "Soil-Structure Interaction Analysis for Foundation Design." International Journal of Information Technology, A Peer reviewed Refereed Research Journal 9, no. 1 (June 7, 2020): 51–56. https://doi.org/10.5281/zenodo.14891230.

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The response of a structure to earthquake shaking is affected by interactions between three linked systems: the structure, the foundation, and the soil underlying and surrounding the foundation. Soil-structure interaction analysis evaluates the collective response of these systems to a specified ground motion. The terms Soil Structure Interaction (SSI) and Soil-Foundation-Structure Interaction (SFSI) are both used to describe this effect in the literature. In this paper, the foundation is considered part of the structure, and the term SSI has been adopted.

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Batool, Tahira, Aly R. Seadawy, and Syed T. R. Rizvi. "Multiple lump solutions and their interactions for an integrable nonlinear dispersionless PDE in vector fields." Nonlinear Analysis: Modelling and Control 28 (February 22, 2023): 1–24. http://dx.doi.org/10.15388/namc.2023.28.31449.

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In this article, lump solutions, lump with I-kink, lump with II- kink, periodic, multiwaves, rogue waves and several other interactions such as lump interaction with II-kink, interaction between lump, lump with I-kink and periodic, interaction between lump, lump with II-kink and periodic are derived for Pavlov equation by using appropriate transformations. Additionally, we also present 3-dimensional, 2-dimensional and contour graphs for our solutions.

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DeBlasio, Stacy L., Juan D. Chavez, Mariko M. Alexander, John Ramsey, Jimmy K. Eng, Jaclyn Mahoney, Stewart M. Gray, James E. Bruce, and Michelle Cilia. "Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology." Journal of Virology 90, no. 4 (December 9, 2015): 1973–87. http://dx.doi.org/10.1128/jvi.01706-15.

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ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in theLuteoviridaeand with unrelated viruses in theHerpesviridaeandAdenoviridae. Functional analysis of three PLRV-interacting host proteinsin plantausing a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.IMPORTANCEThe exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

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Dong, Yukun. "An Analysis of the Impacts of Interaction using Social Media Platforms on Stock Liquidity." Highlights in Business, Economics and Management 30 (April 10, 2024): 270–75. http://dx.doi.org/10.54097/6nfv2p08.

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Social media platforms have developed into a way for more and more investors to interact with each other, and investors are interacting more and more frequently on social media platforms. The study looked at the association between the number of forum posts in the Oriental Wealth stock bar as a measure of social media interaction and stock liquidity as a measure of turnover rate is examined through a panel fixed-effects model, and the analysis results suggest that the number of bar posts has a significant positive effect on stock turnover rate, which indicates that the interaction on social media platforms affects the liquidity of stocks, and the more frequent the interaction is, the higher the liquidity of stocks is. This study provides empirical evidence for the field of investors' social media interactions and stock liquidity, while individual stock companies and regulators should also pay attention to investors' interactions on social media platforms, and do a good job of communicating with investors in a healthy way to avoid abnormal fluctuations in the stock market.

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Kang, Jee Eun, Ji Hae Jun, Jung Hyun Kwon, Ju-Hyun Lee, Kidong Hwang, Sungjong Kim, and Namhee Jeong. "Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool—Liquid/Liquid Phase Separation, Oligomerization, GO Analysis: A Toolkit for Interaction Data-Based Domain Analysis." Genes 14, no. 7 (July 19, 2023): 1476. http://dx.doi.org/10.3390/genes14071476.

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Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool–liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein–nucleic acid interaction (PNI) and protein–protein interaction (PPI) analysis based on domain–domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain–motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms.

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Yu, Ping, Xiulan He, and Lanqun Mao. "Tuning interionic interaction for highly selective in vivo analysis." Chemical Society Reviews 44, no. 17 (2015): 5959–68. http://dx.doi.org/10.1039/c5cs00082c.

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The interionic interaction demonstrated here refers to the interaction between ions and their counterparts, which is not only composed of electrostatic attraction between oppositely charged species but also other kinds of weak interactions. This review focuses on the recent progress in the tuning of interionic interaction to improve the selectivity of biosensors for in vivo analysis.

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Botelho, João, Paulo Mascarenhas, José João Mendes, and Vanessa Machado. "Network Protein Interaction in Parkinson’s Disease and Periodontitis Interplay: A Preliminary Bioinformatic Analysis." Genes 11, no. 11 (November 23, 2020): 1385. http://dx.doi.org/10.3390/genes11111385.

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Recent studies supported a clinical association between Parkinson’s disease (PD) and periodontitis. Hence, investigating possible interactions between proteins associated to these two conditions is of interest. In this study, we conducted a protein–protein network interaction analysis with recognized genes encoding proteins with variants strongly associated with PD and periodontitis. Genes of interest were collected via the Genome-Wide Association Studies (GWAS) database. Then, we conducted a protein interaction analysis, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with a highest confidence cutoff of 0.9 and sensitivity analysis with confidence cutoff of 0.7. Our protein network casts a comprehensive analysis of potential protein–protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted, giving the limitations of this approach.

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Roter, Debra, and Susan Larson. "The Roter interaction analysis system (RIAS): utility and flexibility for analysis of medical interactions." Patient Education and Counseling 46, no. 4 (April 2002): 243–51. http://dx.doi.org/10.1016/s0738-3991(02)00012-5.

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Ollerton, Jeff, Duncan McCollin, Daphne G. Fautin, and Gerald R. Allen. "Finding NEMO: nestedness engendered by mutualistic organization in anemonefish and their hosts." Proceedings of the Royal Society B: Biological Sciences 274, no. 1609 (November 29, 2006): 591–98. http://dx.doi.org/10.1098/rspb.2006.3758.

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The interaction structure of mutualistic relationships, in terms of relative specialization of the partners, is important to understanding their ecology and evolution. Analyses of the mutualistic interaction between anemonefish and their host sea anemones show that the relationship is highly nested in structure, generalist species interacting with one another and specialist species interacting mainly with generalists. This supports the hypothesis that the configuration of mutualistic interactions will tend towards nestedness. In this case, the structure of the interaction is at a much larger scale than previously hypothesized, across more than 180° of longitude and some 60° of latitude, probably owing to the pelagic dispersal capabilities of these species in a marine environment. Additionally, we found weak support for the hypothesis that geographically widespread species should be more generalized in their interactions than species with small ranges. This study extends understanding of the structure of mutualistic relationships into previously unexplored taxonomic and physical realms, and suggests how nestedness analysis can be applied to the conservation of obligate species interactions.

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MAEd, Mr Jayson D. Pentinio,. "CLASSROOM INTERACTION IN THE PANDEMIC: AN ANALYSIS USING FLANDERS INTERACTION ANALYSIS CATEGORIES SYSTEM." Education & Learning in Developing Nations 1, no. 1 (June 16, 2023): 105–7. http://dx.doi.org/10.26480/eldn.02.2023.105.107.

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Classroom interaction is a relevant means of course content exchange among classroom interlocutors. Unquestionably, interaction between the teacher and the students became a challenge during the pandemic, especially that classes transitioned to the virtual format. In order to help address the problem, this descriptive research was conducted to describe science classroom interaction in tertiary education in the virtual format in the time of COVID-19 pandemic among 22 participants using Flanders Interaction Analysis Categories System (FIACS). The thorough analysis using FIACS revealed that the virtual classroom interaction in the time of pandemic is highly teacher-centred. It was dominated by Teacher Talk, particularly Lecture/Lecturing, which indicates that teachers often act as sage on the stage who gives facts and explains ideas in the teaching and learning process. Also, the virtual classroom interaction was predominantly Content Cross and minorly student-focused which indicates teacher dependence on questions and lectures and least students’ involvement. In this setup, students become passive listeners of content for poor verbal involvement. This justifies the need to upskill teachers on online pedagogy and the use of virtual space, flexible learning interaction and the Art of Questioning to transform teacher-centred classroom interaction and instruction to a learner-focused classroom.

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Gunawardena, Charlotte N., Constance A. Lowe, and Terry Anderson. "Analysis of a Global Online Debate and the Development of an Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing." Journal of Educational Computing Research 17, no. 4 (December 1997): 397–431. http://dx.doi.org/10.2190/7mqv-x9uj-c7q3-nrag.

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This study attempts to find appropriate interaction analysis/content analysis techniques that assist in examining the negotiation of meaning and co-construction of knowledge in collaborative learning environments facilitated by computer conferencing. The authors review strengths and shortcomings of existing interaction analysis techniques and propose a new model based on grounded theory building for analyzing the quality of CMC interactions and learning experiences. This new Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing was developed after proposing a new definition of “interaction” for the CMC context and after analyzing interactions that occurred in a Global Online Debate. The application of the new model for analysis of collaborative construction of knowledge in the online debate and in a subsequent computer conference are discussed and future research suggested.

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Venkatraman, S., M. J. Doktycz, H. Qi, and J. L. Morrell-Falvey. "Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions." International Journal of Biomedical Imaging 2006 (2006): 1–10. http://dx.doi.org/10.1155/ijbi/2006/69851.

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The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.

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Yuan, Haiyue, Janko Ćalić, and Ahmet Kondoz. "Analysis of User Requirements in Interactive 3D Video Systems." Advances in Human-Computer Interaction 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/343197.

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The recent development of three dimensional (3D) display technologies has resulted in a proliferation of 3D video production and broadcasting, attracting a lot of research into capture, compression and delivery of stereoscopic content. However, the predominant design practice of interactions with 3D video content has failed to address its differences and possibilities in comparison to the existing 2D video interactions. This paper presents a study of user requirements related to interaction with the stereoscopic 3D video. The study suggests that the change of view, zoom in/out, dynamic video browsing, and textual information are the most relevant interactions with stereoscopic 3D video. In addition, we identified a strong demand for object selection that resulted in a follow-up study of user preferences in 3D selection using virtual-hand and ray-casting metaphors. These results indicate that interaction modality affects users’ decision of object selection in terms of chosen location in 3D, while user attitudes do not have significant impact. Furthermore, the ray-casting-based interaction modality using Wiimote can outperform the volume-based interaction modality using mouse and keyboard for object positioning accuracy.

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