Learners’ frequent pattern discovering in a dynamic collaborative learning environment designed based on game theory

Background and Objectives:In any educational system, the optimal output of educational approach is of particular importance. Therefore, considering the personality characters of individuals and providing educational services in accordance with their characteristics are effective factors in learning and educational efficiency improvement. Analyzing the data related to learner’s behavior in an educational environment and implicitly discovering the learner’s personality based on their behavior is a well-noticed study in recent years. Over the last few years, using learners’ information such as number of friends, the level of activities in educational forum, writing style of learner, study duration, the difficulty of solved problem, the difficulty of presented example by learners, number of clicks, number of signs in sentences, the time spent doing homework are items that has been used to personal characteristic identification. This study is aimed at using teammates’ changing / not changing data in order to learners’ personality identification. For this purpose the teammates’ changing/ not changing data extracted from a dynamic collaborative learning environment that allows leaners to change their teammate during the different sessions of learning, are used. The design and implementation of mentioned dynamic collaborative learning environment is based on game theory. Game theory provides mathematical models of conflict and collaboration between intelligent rational decision-makers. Methods: In this paper, we collect teammates’ changing/not changing information of 119 randomly selected computer engineering students from a game theoretical dynamic collaborative learning environment. At the next step, using frequent pattern mining, as a tools of data mining, some aspects of the neo big 5 personality traits of learners are identified. In this survey, in order to evaluate the results, the extracted patterns from frequent pattern mining are compared with the neo big 5 personality questionnaire that have been filled by learners. In another part of research, using the Laplace’s rule of succession, valuable predictions were made about the probability of teammate’s changing of learners during the learning process. Findings: In this study, using frequent pattern mining in learners’ behaviour, we identified some neo big 5 personality traits such as those in the first (neuroticism), second (extraversion), and third (openness to experience) dimensions, with an acceptable support value. The results of this part of research can be used in any adaptive learning environment that adapt learning process for individual learners with different personality. At the next step of our study, we predicted the probability of the teammate changing in the sessions after. At this step, we had a prediction accuracy of up to 67.44%. Using the results of this part, teammate suggestion can be made to learner based on likelihood of their teammates’ changing. That is, higher teammate changing probability, more appropriate teammate suggestion to learner. Conclusion: The results of the present study can be used in any adaptive system that requires predicting group change behaviour or identifying personality dimensions based on behaviour.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Relationships between mammographic density, tissue microvessel density, and breast biopsy diagnosis

Association between microvessel density (MVD) and tumor characteristics among breast cancer cases (n = 44). (DOC 54 kb

Language experience impacts brain activation for spoken and signed language in infancy: Insights from unimodal and bimodal bilinguals

Recent neuroimaging studies suggest that monolingual infants activate a left lateralised fronto-temporal brain network in response to spoken language, which is similar to the network involved in processing spoken and signed language in adulthood. However, it is unclear how brain activation to language is influenced by early experience in infancy. To address this question, we present functional near infrared spectroscopy (fNIRS) data from 60 hearing infants (4-to-8 months): 19 monolingual infants exposed to English, 20 unimodal bilingual infants exposed to two spoken languages, and 21 bimodal bilingual infants exposed to English and British Sign Language (BSL). Across all infants, spoken language elicited activation in a bilateral brain network including the inferior frontal and posterior temporal areas, while sign language elicited activation in the right temporo-parietal area. A significant difference in brain lateralisation was observed between groups. Activation in the posterior temporal region was not lateralised in monolinguals and bimodal bilinguals, but right lateralised in response to both language modalities in unimodal bilinguals. This suggests that experience of two spoken languages influences brain activation for sign language when experienced for the first time. Multivariate pattern analyses (MVPA) could classify distributed patterns of activation within the left hemisphere for spoken and signed language in monolinguals (proportion correct = 0.68; p = 0.039) but not in unimodal or bimodal bilinguals. These results suggest that bilingual experience in infancy influences brain activation for language, and that unimodal bilingual experience has greater impact on early brain lateralisation than bimodal bilingual experience

Circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3 and terminal duct lobular unit involution of the breast:a cross-sectional study of women with benign breast disease

BACKGROUND: Terminal duct lobular units (TDLUs) are the primary structures from which breast cancers and their precursors arise. Decreased age-related TDLU involution and elevated mammographic density are both correlated and independently associated with increased breast cancer risk, suggesting that these characteristics of breast parenchyma might be linked to a common factor. Given data suggesting that increased circulating levels of insulin-like growth factors (IGFs) factors are related to reduced TDLU involution and increased mammographic density, we assessed these relationships using validated quantitative methods in a cross-sectional study of women with benign breast disease. METHODS: Serum IGF-I, IGFBP-3 and IGF-I:IGFBP-3 molar ratios were measured in 228 women, ages 40-64, who underwent diagnostic breast biopsies yielding benign diagnoses at University of Vermont affiliated centers. Biopsies were assessed for three separate measures inversely related to TDLU involution: numbers of TDLUs per unit of tissue area (“TDLU count”), median TDLU diameter (“TDLU span”), and number of acini per TDLU (“acini count”). Regression models, stratified by menopausal status and adjusted for potential confounders, were used to assess the associations of TDLU count, median TDLU span and median acini count per TDLU with tertiles of circulating IGFs. Given that mammographic density is associated with both IGF levels and breast cancer risk, we also stratified these associations by mammographic density. RESULTS: Higher IGF-I levels among postmenopausal women and an elevated IGF-I:IGFBP-3 ratio among all women were associated with higher TDLU counts, a marker of decreased lobular involution (P-trend = 0.009 and <0.0001, respectively); these associations were strongest among women with elevated mammographic density (P-interaction <0.01). Circulating IGF levels were not significantly associated with TDLU span or acini count per TDLU. CONCLUSIONS: These results suggest that elevated IGF levels may define a sub-group of women with high mammographic density and limited TDLU involution, two markers that have been related to increased breast cancer risk. If confirmed in prospective studies with cancer endpoints, these data may suggest that evaluation of IGF signaling and its downstream effects may have value for risk prediction and suggest strategies for breast cancer chemoprevention through inhibition of the IGF system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-016-0678-4) contains supplementary material, which is available to authorized users

Cell-based therapies for retinal diseases: a review of clinical trials and direct to consumer cell therapy clinics

Background: The retinal pigment epithelium (RPE) is implicated in the pathophysiology of many retinal degenerative diseases. This cell layer is also an ideal target for cell-based therapies. Several early phase clinical trials evaluating cell therapy approaches for diseases involving the RPE, such as age-related macular degeneration and Stargardt\u27s macular dystrophy have been published. However, there have also been numerous reports of complications from unproven cell therapy treatments marketed by cell therapy clinics. This review aims to outline the particular approaches in the different published clinical trials for cell-based therapies for retinal diseases. Additionally, the controversies surrounding experimental treatments offered outside of legitimate studies are presented. Main body: Cell-based therapies can be applied to disorders that involve the RPE via a variety of techniques. A defining characteristic of any cell therapy treatment is the cell source used: human embryonic stem cells, induced pluripotent stem cells, and human umbilical tissue-derived cells have all been studied in published trials. In addition to the cell source, various trials have evaluated particular immunosuppression regiments, surgical approaches, and outcome measures. Data from early phase studies investigating cell-based therapies in non-neovascular age-related macular degeneration (70 patients, five trials), neovascular age-related macular degeneration (12 patients, four trials), and Stargardt\u27s macular dystrophy (23 patients, three trials) have demonstrated safety related to the cell therapies, though evidence of significant efficacy has not been reported. This is in contrast to the multiple reports of serious complications and permanent vision loss in patients treated at cell therapy clinics. These interventions are marketed directly to patients, funded by the patient, lack Food and Drug Administration approval, and lack significant oversight. Conclusion: Currently, there are no proven effective cell-based treatments for retinal diseases, although several trials have investigated potential therapies. These studies reported favorable safety profiles with multiple surgical approaches, with cells derived from multiple sources, and with utilized different immunosuppressive regiments. However, data demonstrating the efficacy and long-term safety are still pending. Nevertheless, cell therapy clinics continue to conduct direct-to consumer marketing for non-FDA-approved treatments with potentially blinding complications

Physics-based dynamic simulation opportunities with digital twins

Abstract This paper aims to provide a viewpoint on the exploitation of physics-based dynamic simulation in product development and discrete manufacturing products. The dynamics models can be represented with computationally light models when the product and its dynamics are well known and thereby analyzing the performance e.g., with AI methods rapidly and accurately. The recent developments with methodologies, sensor development, measuring techniques and increased computing capacity are making the simulation world closer to reality and the ability for real-time operation simulations paralleled to the real system. This enables the exploitation of the digital twin paradigm at full capacity together with high-maturity digital twin models