Multimedia and Learning: Patterns of Interaction

2000 ◽  
pp. 375-388 ◽  
Author(s):  
Sandra Cairncross ◽  
Mike Mannion
Keyword(s):  
Learning Patterns ◽  
Patterns Of Interaction

Linguistic Interactions: A Therapeutic Consideration for Adults With Aphasia

2008 ◽  
Vol 17 (3) ◽  
pp. 93-98
Author(s):  
Lynn E. Fox
Keyword(s):  
Clinical Outcomes ◽  
Social Contexts ◽  
Interaction Models ◽  
Social Communities ◽  
Wernicke's Aphasia ◽  
Communication Challenges ◽  
Therapeutic Consideration ◽  
Patterns Of Interaction ◽  
Structural Language

Abstract Linguistic interaction models suggest that interrelationships arise between structural language components and between structural and pragmatic components when language is used in social contexts. The linguist, David Crystal (1986, 1987), has proposed that these relationships are central, not peripheral, to achieving desired clinical outcomes. For individuals with severe communication challenges, erratic or unpredictable relationships between structural and pragmatic components can result in atypical patterns of interaction between them and members of their social communities, which may create a perception of disablement. This paper presents a case study of a woman with fluent, Wernicke's aphasia that illustrates how attention to patterns of linguistic interaction may enhance AAC intervention for adults with aphasia.

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

scholarly journals Three-way interaction effects of early life stress, positive parenting and FKBP5 in the development of depressive symptoms in a general population

2021 ◽  
Author(s):  
Rebecka Keijser ◽  
Susanne Olofsdotter ◽  
Kent W. Nilsson ◽  
Cecilia Åslund
Keyword(s):  
Young Adults ◽  
Depressive Symptoms ◽  
Life Stress ◽  
Early Life ◽  
Differential Susceptibility ◽  
Early Life Stress ◽  
Interaction Effects ◽  
Positive Parenting ◽  
Depressive Symptomology ◽  
Patterns Of Interaction

AbstractFKBP5 gene–environment interaction (cG × E) studies have shown diverse results, some indicating significant interaction effects between the gene and environmental stressors on depression, while others lack such results. Moreover, FKBP5 has a potential role in the diathesis stress and differential susceptibility theorem. The aim of the present study was to evaluate whether a cG × E interaction effect of FKBP5 single-nucleotide polymorphisms (SNPs) or haplotype and early life stress (ELS) on depressive symptoms among young adults was moderated by a positive parenting style (PASCQpos), through the frameworks of the diathesis stress and differential susceptibility theorem. Data were obtained from the Survey of Adolescent Life in Västmanland Cohort Study, including 1006 participants and their guardians. Data were collected during 2012, when the participants were 13 and 15 years old (Wave I: DNA), 2015, when participants were 16 and 18 years old (Wave II: PASCQpos, depressive symptomology and ELS) and 2018, when participants were 19 and 21 years old (Wave III: depressive symptomology). Significant three-way interactions were found for the FKBP5 SNPs rs1360780, rs4713916, rs7748266 and rs9394309, moderated by ELS and PASCQpos, on depressive symptoms among young adults. Diathesis stress patterns of interaction were observed for the FKBP5 SNPs rs1360780, rs4713916 and rs9394309, and differential susceptibility patterns of interaction were observed for the FKBP5 SNP rs7748266. Findings emphasize the possible role of FKBP5 in the development of depressive symptoms among young adults and contribute to the understanding of possible differential susceptibility effects of FKBP5.

scholarly journals A Resource-Allocating Network for Function Interpolation

1991 ◽  
Vol 3 (2) ◽  
pp. 213-225 ◽  
Author(s):  
John Platt
Keyword(s):  
Time Series ◽  
Learning Process ◽  
Gradient Descent ◽  
Chaotic Time Series ◽  
Local Region ◽  
Unusual Pattern ◽  
Network Parameters ◽  
Learning Patterns ◽  
Compact Representations

We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.

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