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Journal articles on the topic 'Interaction analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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1

Bataeva, Ekaterina V. "Аction-analysis and interaction-analysis of cybercommunication." Sociological Journal 21, no. 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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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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4

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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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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6

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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7

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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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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9

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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10

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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11

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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12

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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13

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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14

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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15

Zayabalaradjane, Zayapragassarazan, and Thomas V. Chacko. "Interaction Analysis of FAIMER Mentor-Learner Web Online Collaborative Learning Session." Malaysian Online Journal of Educational Technology 8, no. 2 (April 1, 2020): 12–27. http://dx.doi.org/10.17220/mojet.2020.02.002.

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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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17

Davitti, Elena. "Methodological explorations of interpreter-mediated interaction: novel insights from multimodal analysis." Qualitative Research 19, no. 1 (November 20, 2018): 7–29. http://dx.doi.org/10.1177/1468794118761492.

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Research in Dialogue Interpreting (DI) has traditionally drawn on qualitative analysis of verbal behaviour to explore the complex dynamics of these ‘triadic’ exchanges. Less attention has been paid to interpreter-mediated interaction as a situated, embodied activity where resources other than talk (such as gaze, gestures, head and body movement, proxemics) play a central role in the co-construction of the communicative event. This article argues that understanding the complexity of DI requires careful investigation of the interplay between multiple interactional resources, i.e. verbal in conjunction with visual, aural, embodied and spatial meaning-making resources. This call for methodological innovation is strengthened by the emergence of video-mediated interpreting, where interacting via screens without sharing the same physical space adds a further layer of complexity to interactional dynamics. Drawing on authentic extracts from interpreter-mediated interaction, both face-to-face and video-mediated, this article problematizes how the integration of a multimodal perspective into qualitative investigation of interpreter-mediated interaction can contribute to the advancement of our understanding of key interactional dynamics in DI and, in turn, broaden the scope of multimodality to include new, uncharted territory.
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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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Mykhailovska, Olena Vasylivna, Volodymyr Ihorovych Gurkovskyi, and Olga Mstyslavivna Rudenko. "ANALYSIS OF PRACTICAL ASPECTS OF INTERACTION BETWEEN CIVIL SOCIETY AND PUBLIC GOVERNANCE." SCIENTIFIC BULLETIN OF POLISSIA 2, no. 4(12) (2017): 149–57. http://dx.doi.org/10.25140/2410-9576-2017-2-4(12)-149-157.

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28

Gunamalai, Lavanya, and C. Jaynthy C.Jaynthy. "In Silico Molecular Interaction Analysis Of Type I Collagen Telopeptides With Cyclodextrins." International Journal of Scientific Research 3, no. 8 (June 1, 2012): 25–27. http://dx.doi.org/10.15373/22778179/august2014/8.

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29

Bhargava, A., S. Shukla, and D. Ohri. "Analysis of Genotype × Environment Interaction for Grain Yield in Chenopodium spp." Czech Journal of Genetics and Plant Breeding 41, No. 2 (November 21, 2011): 64–72. http://dx.doi.org/10.17221/3673-cjgpb.

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Twenty-seven germplasm lines of Chenopodium spp. were grown in four environments and evaluated for genotype &times; environment interactions and comparisons between 4 stability parameters viz. Eberhart and Russel&rsquo;s &delta;<sub>i</sub><sup>2</sup>, Shukla&rsquo;s s<sub>i</sub><sup>2</sup>, Wricke&rsquo;s W <sub>i</sub><sup>2</sup> and Tai&rsquo;s &euml;<sub>i</sub> were made. Highly significant variance due to environment + (G &times; E) interaction indicated that genotypes interacted differentially with environments. Shukla&rsquo;s and Wricke&rsquo;s methods gave more or less the same results while large differences occurred between Shukla&rsquo;s and Tai&rsquo;s methods. s<sub>i</sub><sup>2</sup> and W<sub>i</sub><sup>2</sup> exhibited the highest correlation (0.9999**) between themselves. Two diploid and two hexaploid lines, viz. C.&nbsp;album cv. Siliguri, C. album cv. Chandanbathua, C. album PRC 9803 and C. giganteum PI596371, were found to be stable and high yielding. &nbsp;
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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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Asbah, Asbah. "ANALYSIS OF CLASSROOM INTERACTION IN EFL CLASS." Linguistics and ELT Journal 3, no. 1 (June 9, 2015): 137. http://dx.doi.org/10.31764/leltj.v3i1.822.

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This study was to find out classroom interaction types and how those types emerge in the language teaching process. The research design was a qualitative descriptive. Participants of the research were an English teacher and 25 eight grade students of MTs-Al Raisiyah Sekarbela. The results of the study showed that there were seven types of classroom interactions which were teacher-whole class, teacher- an individual student, teacher-groups of students, student- teacher, student-student, student-whole class, and student-groups of students. The interaction occurred through teacher talk, questioning, giving feedback and discussion.
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32

Rao, V. Srinivasa, K. Srinivas, G. N. Sujini, and G. N. Sunand Kumar. "Protein-Protein Interaction Detection: Methods and Analysis." International Journal of Proteomics 2014 (February 17, 2014): 1–12. http://dx.doi.org/10.1155/2014/147648.

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Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.
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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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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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35

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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36

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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37

Soukal, Ivan. "Novel Interaction Cost Analysis Applied to Bank Charges Calculator." Computers 8, no. 3 (September 4, 2019): 64. http://dx.doi.org/10.3390/computers8030064.

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This paper presents an online calculator for bank charges, motivated by information asymmetry in the market for payment accounts. The calculator provides users with a personalized list of the most suitable bank accounts based on required services and monthly fee criteria. This paper outlines the conceptual foundation, workflows, and matrix of the data for the underlying logic of the calculator, as well as the design of the user interface. The proposed calculator was validated by performing an interaction cost analysis. This paper presents a novel methodology for conducting this analysis, including rules for expressing interactions in graphs for the objective evaluation of the usability of the user interface. Scenarios were defined and analyzed with the intended goal of choosing the best bank account. The interaction cost analysis then confirmed the differences in cost between traditional approaches (interacting with various web interfaces) and using a specialized online service (the calculator). The consistency of the layout and navigation contributed significantly to the final results being in favor of the proposed bank charges calculator. These conclusions are applicable not just within the selected market, but also in many others that are prone to problems arising from price information asymmetry.
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38

Tannen, Deborah, and Cynthia Wallat. "Medical professionals and parents: A linguistic analysis of communication across contexts." Language in Society 15, no. 3 (September 1986): 295–311. http://dx.doi.org/10.1017/s0047404500011787.

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ABSTRACTThe study is based on analysis of videotaped conversation that occurred in five different settings involving various family members and medical professionals in a single pediatric case. We examine (1) the elaboration and condensation of information through spoken and written channels; (2) the negotiation of information exchanged in interactions characterized by different participant structures; and (3) the methodological benefit of examining interaction across contexts. We find that (a) information is negotiated, as well as discovered, during the medical interviews; and (b) information exchanged is often less resilient than participants' cognitive schemas which precede and apparently outlive the exchange of information in the interaction. These findings contribute to an understanding of the negotiation of meaning as well as the creation of context in interaction. (Discourse, interactional sociolinguistics, context, doctor–patient communication, spoken and written language, schema theory)
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Friedman, Avner, and J. Ignacio Tello. "Head–Media Interaction in Magnetic Recording." Journal of Differential Equations 171, no. 2 (April 2001): 443–61. http://dx.doi.org/10.1006/jdeq.2000.3844.

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Bruneau, Laurent, Alain Joye, and Marco Merkli. "Asymptotics of repeated interaction quantum systems." Journal of Functional Analysis 239, no. 1 (October 2006): 310–44. http://dx.doi.org/10.1016/j.jfa.2006.02.006.

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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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Stebbins, Robert A., A. Paul Hare, and Herbert H. Blumberg. "Dramaturgical Analysis of Social Interaction." Contemporary Sociology 18, no. 3 (May 1989): 462. http://dx.doi.org/10.2307/2073914.

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ITOH, Tomoki. "An Analysis of Senile Interaction." Annual review of sociology 1997, no. 10 (1997): 145–56. http://dx.doi.org/10.5690/kantoh.1997.145.

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Vogel, Carl, and Anna Esposito. "Interaction Analysis and Cognitive Infocommunications." Infocommunications journal 12, no. 1 (2020): 2–9. http://dx.doi.org/10.36244/icj.2020.1.1.

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di Ronza, Alberto, Michela Palmieri, and Marco Sardiello. "Analysis of NCL Interaction Network." Molecular Genetics and Metabolism 102, no. 2 (February 2011): S14—S15. http://dx.doi.org/10.1016/j.ymgme.2010.11.049.

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Rod, Naja Hulvej, Theis Lange, Ingelise Andersen, Jacob Louis Marott, and Finn Diderichsen. "Additive Interaction in Survival Analysis." Epidemiology 23, no. 5 (September 2012): 733–37. http://dx.doi.org/10.1097/ede.0b013e31825fa218.

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Moriarty, Laura. "SPR Solutions for Interaction Analysis." Genetic Engineering & Biotechnology News 32, no. 12 (June 15, 2012): 24–25. http://dx.doi.org/10.1089/gen.32.12.09.

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Wu, Mengyun, Qingzhao Zhang, and Shuangge Ma. "Structured gene‐environment interaction analysis." Biometrics 76, no. 1 (October 9, 2019): 23–35. http://dx.doi.org/10.1111/biom.13139.

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Conlon, Margaret MM. "Interaction analysis: a therapeutic tool." British Journal of Nursing 4, no. 21 (November 23, 1995): 1269–70. http://dx.doi.org/10.12968/bjon.1995.4.21.1269.

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Shapiro, David A. "Interaction Analysis and Self-Study." Language, Speech, and Hearing Services in Schools 25, no. 2 (April 1994): 67–75. http://dx.doi.org/10.1044/0161-1461.2502.67.

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This article introduces supervisors and supervisees to interaction analysis and self-study in the supervisory process in speech-language pathology and audiology. Four interaction analysis systems served as the basis of a descriptive self-study in which 10 consecutive, individual supervisory conferences were transcribed, coded, and analyzed. Results are reviewed for the purposes of profiling a supervisor’s and supervisee’s conference behavior, monitoring changes in specific supervisory objectives, critiquing the instruments utilized, and demonstrating the importance of self-study. Collecting, analyzing, and sharing objective data are emphasized as components of self-study and as a foundation for understanding the supervisory process and for facilitating professional growth in school-based and other professional settings.
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